Methods and kits for diagnosing and treating fetal growth disorders

Abstract

Marker panels and methods of using the markers in circulating DNA (cirDNA) in maternal blood and / or maternal urine for early detection of fetal growth disorders (FGDs) are disclosed herein. Use of these marker panels will enable accurate prediction of FGDs using a low invasive approach that can be implemented as early as the first gestational trimester.

Description

Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 METHODS AND KITS FOR DIAGNOSING AND TREATING FETAL GROWTH DISORDERS CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 63 / 592,818 filed on October 24, 2023, which is hereby incorporated by reference in its entirety. INCORPORATION OF SEQUENCE LISTING

[0002] A paper copy of the Sequence Listing and a computer readable form of the Sequence Listing containing the file named "UMCO 40485WO Sequence Listing", which is 63,286 bytes in size (as measured in MICROSOFT WINDOWS® EXPLORER), are provided herein and are herein incorporated by reference. This Sequence Listing consists of SEQ ID Nos: 1-68. BACKGROUND OF THE DISCLOSURE

[0003] The present disclosure relates generally to methods of using markers in DNA and particularly in circulating DNA (cirDNA) in maternal blood and / or maternal urine for early detection of fetal growth disorders (FGDs). Physical and epigenetic variables in maternal blood cirDNA can serve as early FGD markers. Marker panels of the present disclosure will enable accurate prediction of FGDs using a low invasive approach that can be implemented as early as the first gestational trimester.

[0004] According to the United Nations, 385,000 babies are born daily worldwide. Of those, it is estimated that 8.6% are born small for gestational age (SGA, <10th percentile), 80.9% appropriate for gestational age (AGA, 10-90th percentile), and 10.5% large for gestational age (LGA, >90th percentile). Many potential adverse health outcomes that could affect the mother and fetus are associated with fetal growth disturbances during pregnancy. SGA infants may undergo metabolic, thermal, and hematological disturbancesAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 leading to neonatal morbidities. Furthermore, changes in the fetal nutritional environment may result in increased risk of developing long term consequences in infants born with SGA, such as systolic hypertension, obesity, insulin resistance, and diabetes type II in adulthood. Likewise, LGA pregnancies may have adverse short- and long-term outcomes. LGA infants are at increased risk for neonatal intensive care unit admission, respiratory distress, neonatal metabolic abnormalities including hypoglycemia, birth trauma, and even stillbirth or neonatal death. Later in life, LGA individuals have increased risk for overweight / obesity, diabetes, cardiovascular disease, and even some childhood cancers.

[0005] Notwithstanding, there is no intervention available that can improve outcomes in SGA and LGA pregnancies targeting the affected molecular and physiological pathways. Hence, early diagnosis, close follow-up and timely delivery constitute the main elements for the appropriate detection and management of pregnancies with FGD. Currently, screening of fetal growth abnormalities is based on fetal biometry by ultrasound in which serial sonographic assessments of fetal size over time can provide useful information about growth. However, besides the unprecedented technological development in the field, errors and approximations still hinder FGD detection and assessment. Hence, there is an unmet need for reliable pre-natal biomarkers of fetal growth, which can be assessed using low-invasive approaches.

[0006] Fetal Growth Restriction (FGR) is associated with fetal genetic anomalies in 5-20 % of the cases, mainly due to aneuploidies, uniparental disomies, chromosomal aberrations and single gene mutations. Genetic and congenital disorders are also observed in fetal macrosomia including Beckwith-Wiedemann, Sotos, Weaver and Fragile X syndromes. In addition, non-chromosomal congenital anomalies or specific syndromes may result in SGA and LGA babies. On the other hand, fetal infections are responsible for 5-10% of FGR cases. Importantly, the placenta controls intrauterine development in mammals by supplying oxygen and nutrients, and by regulating the bioavailability of specific hormones involved in fetal growth and development. Consequently, the placenta is likely to have a key role in mediating the programming effects of suboptimal conditionsAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 during development. Altered placental growth, development and function are major causes of impaired fetal growth and development, and therefore of FGD. Several molecular mechanisms are compromised in FGR placentas, such as macronutrients transport, oxidative stress and master regulation and signal transduction. Moreover, maternal morbidities can adversely interfere with uteroplacental-fetal blood flow and cause FGR, including among others, chronic hypertension, gestational or pregestational diabetes mellitus, substance abuse, extreme maternal age, and poor maternal nutritional status. Importantly, mothers who were growth restricted carry twice the risk for delivering FGR neonates. In turn, maternal obesity and gestational diabetes mellitus are the main risk factors for excessive fetal growth leading to LGA newborns and macrosomia, which may be caused by alterations in placental function and structure through inflammation and oxidative stress. Very importantly, there is a significant overlap between fetoplacental and maternal origins in both SGA and LGA, increasing the complexity for FGD pathophysiology. However, whether fetal, placental, and maternal causes have different weight in FGD etiology throughout the extent of pregnancy remains to be determined. Hence, it is reasonable that an analyte that will reflect such complexity, such as circulating DNA (cirDNA) in maternal blood and / or urine, will hold the potential for sensitive and specific assessment of FGDs at different gestational times, paving the way for the development of molecular diagnostic tools and providing insight on the temporal differences in fetoplacental and maternal pathophysiology in FGD. SUMMARY OF THE DISCLOSURE

[0007] In various embodiments, the present disclosure discusses methods of diagnosing fetal growth disorders using a marker panel and statistical analysis methods. In various embodiments, the present disclosure also describes a method of treating a fetal growth disorder diagnosed according to the description provided herein.

[0008] This summary is provided merely for purposes of summarizing various example embodiments of the present disclosure so as to provide a basic understanding of various aspects of the teachings herein. Various embodiments, aspects, and advantages willAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 become apparent from the following detailed description taken in conjunction with the accompanying drawings which illustrate, by way of example, the principles of the described embodiments. Accordingly, it should be understood that the description and specific examples set forth herein are intended for purposes of illustration only and are not intended to limit the scope of the present teachings.

[0009] Analyses of circulating nucleic acids (i.e., DNA and RNA) in maternal blood and / or maternal urine are widely used as a non-invasive prenatal diagnostic test (NIPT). cirDNA is shed into maternal blood via apoptosis, necrosis and oxidative stress. Experimental evidence indicates that cirDNA in plasma derives from the fetus, the placenta and the mother, and there are dynamics on the relative contribution of each source to the total amount of DNA recovered from maternal plasma or serum, with a rapid clearance after birth. Likewise, cirDNA is filtered through the glomeruli and it is detectable in maternal urine from the early first trimester until two months after birth. Thus, the assessment of cirDNA in maternal blood can provide valuable evidence for fetoplacental health and disease throughout pregnancy.

[0010] Numerous tests and applications have been developed during the last two decades demonstrating the utility and precision of such tests for determining fetal health (e.g., chromosomal alterations), as well as for predicting obstetric complications, such as pre-eclampsia and premature birth. Nevertheless, some controversy still remains on the clinical utility of cirDNA testing during pregnancy mainly based on the limited fraction of fetal DNA in the total circulating DNA in maternal blood, especially in early gestation. Hence, despite the tremendous advances in the field, the use of liquid biopsies for monitoring fetal growth and FGD detection still requires further development. A recent study including over 16,000 individuals, demonstrated that the amount of cirDNA in maternal blood shows a steady increase during pregnancy with inflection points at the 10th, 19th and 30th week of gestation. Importantly, the present disclosure demonstrates that the concentration of cirDNA in maternal blood reached the standard NIPT requirements at the 9thweek of gestation, encouraging the interrogation of fetal markers at early gestationalAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 time using high sensitivity approaches. Similarly, fetal DNA is detectable in maternal urine from the early first trimester until two months after birth. However, the amount of fetal DNA in urine is small, making up only 0.2%–20% of the DNA in the plasma requiring highly sensitive and specific approaches for its analysis. Using a comprehensive cirDNA profiling maternal blood samples, herein is described a marker panel which enables accurate prediction of FGDs using a low-invasiveness approach that can be implemented as early as the first gestational trimester. Further, a similar approach using the same marker panel from maternal urine was found to also enable accurate prediction of FGDs as early as the first gestational trimester. The disclosed marker panel combines the total amount of cirDNA, with physical characterization of the cirDNA (i.e., fragmentation analysis and ratio of mitochondrial DNA) which are representative of the increase shedding of cirDNA into circulation upon cellular turnover. Furthermore, the panel includes DNA methylation assessment in 14 genes that have been selected due to their direct role in fetal growth and placental homeostasis, a crucial regulator of fetal growth. DNA methylation profiles can be used to determine the tissue of origin for plasma cirDNA, thereby enabling the identification of fetal- and placenta-specific cirDNA methylation markers in maternal blood and / or maternal urine.

[0011] Since epigenetic alterations represent a common trait in most complex diseases, they hold great potential as robust and precise biomarkers in tissues and bodily fluids. In particular, the analysis of cirDNA in maternal blood and urine is of outstanding clinical relevance, and it is currently a standard practice in non-invasive pre-natal diagnosis (NIPT). Several features of DNA methylation support their potential as early diagnostic biomarkers. First, the number of epigenetic aberrations in affected cells is significant and greatly exceeds the number of DNA sequence mutations. Second, DNA methylation changes occur in very early phases of disease development. Third, DNA methylation markers are stable and can be detected in small amounts of DNA, which are evident advantages compared to RNA and proteomic strategies. DNA methylation is de facto being already used in clinical practice. DNA methylation biomarkers are instrumental inAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 oncology and several commercial tests based on DNA methylation markers are already used regularly in the clinics.

[0012] Large-scale DNA methylation profiles have been proven as a successful source for candidate biomarkers, but their application as molecular diagnostics is hampered by costs and other intrinsic limitations (e.g., the generation of information that is beyond the clinical application). Hence, clinical assay design requires the reduction of the number of measured loci to a manageable level. In the present disclosure, a multi-marker signature was found that can differentiate with high precision (> 90% accuracy) between FGD and normal pregnancies in first trimester maternal blood and urine samples. The number of markers can be as many as 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or more. The clinical utility of such marker panels will be further enhanced by combining them with clinical and demographic parameters (e.g., BMI, maternal and gestational age, diabetes, hypertension, etc.) using multivariate models towards the operationalization of personalized diagnostic and monitoring algorithms in FGD. The application of such algorithms in a clinical setting holds the potential to enable a disruptive path toward precision medicine in FGD.

[0013] In various forms, described herein are a set of marker panels, a method of determining at least one fetal growth disorder, a method of treatment for a fetal growth disorder, and a multiplex PCR kit. In general, the set of marker panels comprises a first marker panel for determining the total amount of cirDNA and / or genomic DNA in a DNA- containing sample that includes mitochondrial and nuclear DNA, a second marker panel for determining at least one physical characteristic of the cirDNA and / or genomic DNA, a third marker panel for determining the ratio of mitochondrial DNA to nuclear DNA in the DNA-containing sample, and a fourth marker for assessing DNA methylation in a plurality of genes in the DNA-containing sample. In various forms, the plurality of genes whose methylation is assessed in the fourth marker panel consists of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14 genes. In some forms, the plurality of genes whose methylation is assessed in the fourth marker panel consists of HSD2, RASSF1, CYP19A1, IGF2R, SLC16A10, IRS1, IL10, PTPRN2, SLC36A1, LEP, ADIPOQ, IGF2, INSR, and SYN1. In some forms, theAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 plurality of genes whose methylation is assessed in the fourth marker panel consists of HSD2, RASSF1, CYP19A1, IGF2R, SLC16A10, IRS1, IL10, PTPRN2, SLC36A1, and LEP. In some forms, the plurality of genes whose methylation is assessed in the fourth marker panel consists of ADIPOQ, INSR, SYN1, CYP19A1, HSD2, IGF2R, IL10, IRS1, LEP, PTPRN2, SLC16A10, SLC36A1. In some forms, the DNA-containing sample is from blood or urine. In some forms, the physical characteristic determined by the second marker panel is a fragmentation analysis.

[0014] The method of determining at least one fetal growth disorder, in various forms, comprises the steps of obtaining a maternal fluid sample comprising DNA, measuring molecular markers, and integrating the markers into a statistical analysis that correlates the markers with at least one fetal growth disorder and thereby predicts the presence or absence of at least one fetal growth disorder. In some forms, the molecular markers comprise a first marker, which measures the amount of DNA; a second marker, which is a fragmentation analysis of the DNA; a third marker, which measures a ratio of mitochondrial DNA to nuclear DNA; and a fourth marker, which assesses the DNA for the presence of at least one DNA methylation marker. In various forms, the step of integrating the markers into a statistical analysis is a step of integrating the first, second, third, and fourth markers, or any combination thereof, into a statistical analysis. In various forms, the DNA methylation markers whose presence is assessed by the fourth marker consists of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or 14 markers. In some forms, the DNA methylation markers whose presence is assessed by the fourth marker consists of HSD2.methylation, RASSF1.methylation, CYP19A1.methylation, IGF2R.methylation, SLC16A10.methylation, IRS1.methylation, IL10.methylation, PTPRN2.methylation, SLC36A1.methylation, LEP.methylation, ADIPOQ.methylation, IGF2.methylation, INSR.methylation, and SYN1.methylation. In some forms, the fragmentation analysis of the second marker is performed using a set of specific qPCR assays comprising assays that measure the amount of intact DNA, assays that measure amounts of nucleosome-sized DNA, and assays that measure the amount of fragmented DNA. In some forms, theAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 maternal fluid comprises blood and / or urine. In some forms, the DNA comprises cirDNA and / or genomic DNA.

[0015] The method of treatment for a fetal growth disorder, in various forms, comprises the steps of obtaining a obtaining a maternal fluid sample comprising DNA, measuring molecular markers, and integrating the markers into a statistical analysis that correlates the markers with at least one fetal growth disorder and thereby predicts the presence or absence of at least one fetal growth disorder, selecting one or more treatments for the diagnosed fetal growth disorder, and administering the one or more appropriate treatments. In some forms, the molecular markers comprise a first marker, which measures the amount of DNA; a second marker, which is a fragmentation analysis of the DNA; a third marker, which measures a ratio of mitochondrial DNA to nuclear DNA; and a fourth marker, which assesses the DNA for the presence of at least one DNA methylation marker. In various forms, the step of integrating the markers into a statistical analysis is a step of integrating the first, second, third, and fourth markers, or any combination thereof, into a statistical analysis. In various forms, the DNA methylation markers whose presence is assessed by the fourth marker consists of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or 14 markers. In some forms, the DNA methylation markers whose presence is assessed by the fourth marker consists of HSD2.methylation, RASSF1.methylation, CYP19A1.methylation, IGF2R.methylation, SLC16A10.methylation, IRS1.methylation, IL10.methylation, PTPRN2.methylation, SLC36A1.methylation, LEP.methylation, ADIPOQ.methylation, IGF2.methylation, INSR.methylation, and SYN1.methylation. In some forms, the fragmentation analysis of the second marker is performed using a set of specific qPCR assays comprising assays that measure the amount of intact DNA, assays that measure amounts of nucleosome-sized DNA, and assays that measure the amount of fragmented DNA. In some forms, the maternal fluid comprises blood and / or urine. In some forms, the DNA comprises cirDNA and / or genomic DNA. In some forms, the one or more appropriate treatments comprise nutritional adjustments for a mother during pregnancy. In some forms, the one or more appropriate treatments comprise early delivery of a fetus by induction or emergency c-section.Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011

[0016] The multiplex PCR kit for early detection of at least one fetal growth disorder from maternal fluid, in various forms, comprises a first set of primers corresponding to a first marker that measures an amount of DNA in the maternal fluid; a second set of primers corresponding to a second marker that is a fragmentation analysis of the DNA; a third set of primers corresponding to a third marker that measures a ratio of mitochondrial DNA to nuclear DNA in the DNA; and a fourth set of primers corresponding to a fourth marker that assesses the DNA for the presence of at least one diagnostic DNA methylation marker. In various forms, the fourth set of primers correspond to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14 methylation markers. In various forms, the fourth set of primers correspond to methylation markers selected from the list consisting of HSD2.methylation, RASSF1.methylation, CYP19A1.methylation, IGF2R.methylation, SLC16A10.methylation, IRS1.methylation, IL10.methylation, PTPRN2.methylation, SLC36A1.methylation, LEP.methylation ADIPOQ.methylation, IGF2.methylation, INSR.methylation, and SYN1.methylation. In some forms, the maternal fluid comprises blood and / or urine. In some forms, the DNA comprises cirDNA and / or genomic DNA. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The disclosure will be better understood, and features, aspects and advantages other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such detailed description makes reference to the following drawings, wherein:

[0018] FIG. 1 provides an exemplary schematic of the methods of present disclosure.

[0019] FIG. 2A provides a comparison of circulating DNA concentrations in maternal plasma from SGA, AGA, and LGA pregnancies measured in accordance with the methods of the present disclosure.Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011

[0020] FIG. 2B provides a comparison of the fragmentation levels of circulating DNA in maternal plasma from SGA, AGA, and LGA pregnancies measured in accordance with the methods of the present disclosure.

[0021] FIG. 2C provides a comparison of the ratio of mitochondrial DNA to nuclear DNA in maternal plasma from SGA, AGA, and LGA pregnancies measured in accordance with the methods of the present disclosure.

[0022] FIG.2D provides a comparison of the extent of methylation of ten candidate genes in maternal plasma from SGA, AGA, and LGA pregnancies measured in accordance with the methods of the present disclosure.

[0023] FIG.2E provides a more detailed comparison of five of the ten exemplary candidate genes from Example 1 that showed significant differential methylation between SGA, AGA, and LGA groups.

[0024] FIG.2F provides a more detailed comparison of five of the ten exemplary candidate genes from Example 1 that did not show significant differential methylation between SGA, AGA, and LGA groups.

[0025] FIG. 3A provides a PCA plot from an exemplary principal component analysis showing clustering of samples corresponding to SGA, AGA, and LGA pregnancies analyzed from maternal plasma in accordance with the methods of the present disclosure.

[0026] FIG. 3B provides a variable plot, or individuals factor map, from an exemplary principal component analysis showing the relative contributions of each variable in a sample distribution of maternal plasma from SGA, AGA, and LGA pregnancies analyzed in accordance with the methods of the present disclosure.Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011

[0027] FIG. 3C provides an exemplary correlation matrix comparing the correlations between measured aspects of circulating DNA in maternal plasma samples and the clinical variables of age, body mass index, hypertension, and diabetes.

[0028] FIGS. 4A-4D provide Receiving-Operating Curve (ROC) analyses assessing the performance of the molecular signature in maternal plasma for the discrimination between SGA, LGA and AGA pregnancies as analyzed in Example 1. FIG. 4A demonstrates differentiation of FGDs generally, including SGA and LGA, from AGA. FIG. 4B demonstrates differentiation of SGA from AGA. FIG. 4C demonstrates differentiation of LGA from AGA. FIG. 4D demonstrates differentiation of SGA from LGA.

[0029] FIGS. 5A-5C provide Receiving-Operating Curve (ROC) analyses assessing the performance of the molecular signature in plasma for the discrimination between SGA, LGA and AGA pregnancies as analyzed in Example 1. FIG. 5A demonstrates differentiation AGA from LGA. FIG. 5B demonstrates differentiation of AGA from SGA. FIG.5C demonstrates differentiation of SGA from LGA.

[0030] FIGS. 6A and 6B provide box and scatterplots depicting the two-way ANOVA test shown in Table 3 with respect to the connection between IL-10 methylation, hypertension, and the AGA, LGA, and SGA groups.

[0031] FIGS.7A-7P provide bar plots with dot overlay for the cellular fraction of maternal urine for each of the four markers described in the methods of the present disclosure, according to the Example 2, including individual plots for methylation of each of the candidate genes studied in Example 2.

[0032] FIGS 8A-8D provide Receiving-Operating Curve (ROC) analyses assessing the performance of the molecular signature for the discrimination between SGA, LGA and AGA pregnancies as analyzed in Example 2. FIG.8A demonstrates differentiation of AGA from SGA / LGA. FIG. 8B demonstrates differentiation of AGA from LGA. FIG. 8CAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 demonstrates differentiation of AGA from SGA. FIG.8D demonstrates differentiation of SGA from LGA.

[0033] FIGS.9A-9O provide bar plots with dot overlay for the fluidic fraction of maternal urine for each of the four markers described in the methods of the present disclosure, according to Example 3, including individual plots for methylation of each of the candidate genes studied in Example 3.

[0034] FIGS 10A-10D provide Receiving-Operating Curve (ROC) analyses assessing the performance of the molecular signature for the discrimination between SGA, LGA and AGA pregnancies as analyzed in cirDNA in the cellular fraction of Example 3. FIG. 10A demonstrates differentiation of AGA from SGA / LGA. FIG. 10B demonstrates differentiation of AGA from LGA. FIG. 10C demonstrates differentiation of AGA from SGA. FIG.10D demonstrates differentiation of SGA from LGA.

[0035] FIGS 11A-11D provide Receiving-Operating Curve (ROC) analyses assessing the performance of the molecular signature for the discrimination between SGA, LGA and AGA pregnancies as analyzed in the combined cirDNA from the urine fluidic fraction and genomic DNA from the urine cellular fraction as analyzed in Example 3. FIG. 11A demonstrates differentiation of AGA from SGA / LGA. FIG. 11B demonstrates differentiation of AGA from LGA. FIG. 11C demonstrates differentiation of AGA from SGA. FIG.11D demonstrates differentiation of SGA from LGA. DETAILED DESCRIPTION

[0036] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the disclosure belongs.

[0037] Described herein are methods for the prediction, determination and treatment of one or more fetal growth disorders as well as multiplex PCR kits for the early detection of the one or more fetal growth disorders. FIG. 1 provides an exemplary high-Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 level depiction of the methods of the present disclosure according to various embodiments. A maternal fluid sample 110 is acquired from a subject, wherein the maternal fluid sample 110 comprises DNA 200 from the subject. In various embodiments, the DNA 200 is circulating DNA. In various embodiments, the DNA is genomic DNA. In various embodiments, the maternal fluid sample 110 can comprise blood plasma or urine. In step A, molecular markers of the DNA 200 are measured. The molecular markers comprise a first marker 210 that corresponds to a measured amount of the DNA 200, a second marker 220 that corresponds to a fragmentation analysis of the DNA 200, a third marker 230 that corresponds to a ratio of mitochondrial DNA to nuclear DNA in the DNA 200, and a fourth marker 240 that corresponds to the presence and / or measurement of at least one DNA methylation marker selected from a list of known DNA methylation markers. In step B, the first marker 210, the second marker 220, the third marker 230, and the fourth marker 240 are integrated into a statistical analysis 300 that correlates the markers with at least one fetal growth disorder and thereby predicts the presence or absence of at least one fetal growth disorder.

[0038] In various exemplary embodiments, the first marker 210 is a count of the number and / or length of strands of the DNA 200. The first marker 210 can alternately be a measured or calculated concentration of the DNA 200 in the maternal fluid 110. The first marker 210 can be determined by any method known to one of ordinary skill in the art, such as by qPCR assay.

[0039] In various exemplary embodiments, the second marker 220 corresponds to a fragmentation analysis of the DNA, or the output of such a fragmentation analysis. The second marker 220 can be calculated, in some exemplary embodiments, by using three quantitative polymerase chain reaction (qPCR) assays that target the KRAS gene, wherein each qPCR assay amplifies different lengths of DNA. One qPCR assay can amplify 357 base pair (bp) long DNA fragments, a second can target 145 bp long DNA fragments, and a third can target 60 bp long DNA fragments. Each assay can be distinguished by having the same reverse primer but different forward primers targeting differing DNA fragmentAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 lengths. The relative enrichment of each fraction can then be calculated as a ratio of amplification, from which a DNA Fragmentation Index (DFI) can be calculated. In various alternative embodiments, the second marker 220 can be calculated using any means known to one of ordinary skill in the art. In lieu of three qPCR assays, 2, 4, 5, 6, 7, 8, 9, 10 or more qPCR assays can be employed. In preferred embodiments, the DFI is a single number representative of the level of DNA fragmentation measured across multiple assays.

[0040] In various exemplary embodiments, the third marker 230 corresponds to the ratio of mitochondrial DNA to nuclear DNA in the DNA 200. The ratio of mitochondrial DNA to nuclear DNA (MNR) can be calculated according to any means known to one of ordinary skill in the art. For example, in various embodiments, the MNR can be calculated by first selectively quantifying DNA sequences of nuclear and mitochondrial origins. Quantification can be performed via amplification of a 67 bp sequence on the KRAS gene and of a 67 bp sequence on the MT-COX-3 gene corresponds to amplification of short nuclear and mitochondrial amplicons, respectively. The ratio of the concentration of short- amplicon-quantified mitochondrial DNA to short-amplicon-quantified nuclear DNA then corresponds to the MNR.

[0041] In various exemplary embodiments, the fourth marker 240 corresponds to the presence of at least one methylation marker selected from a list of methylation markers. The methylation markers comprise markers indicating methylation of genes that are involved in placental homeostasis and / or fetal development. In various exemplary embodiments, if the maternal fluid 110 is urine, the fourth marker 240 corresponds to the presence of at least one methylation marker selected from a list of fifteen methylation markers comprising markers indicating methylation of the genes HSD2, RASSF1, CYP19A1, IGF2R, SLC16A10, IRS1, IL10, PTPRN2, SLC36A1, LEP, ADIPOQ, IGF2, INSR, and SYN1. In various embodiments, if the maternal fluid 110 is blood, the fourth marker 240 corresponds to the presence of at least one methylation marker selected from a list of ten methylation markers comprising markers indicating methylation of the genes HSD2, RASSF1, CYP19A1, IGF2R, SLC16A10, IRS1, IL10, PTPRN2, SLC36A1, LEP.Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 The presence of at least one methylation marker can be detected by any means known to one of ordinary skill in the art. For example, the presence of at least one methylation marker can be measured using Methylation-Sensitive Restriction Enzyme qPCR (MSRE-qPCR). Primers for the exemplary qPCR assays described herein are provided in Table 1.

[0042] In various exemplary embodiments the statistical analysis 300 incorporates the first marker 210, the second marker 220, the third marker 230, and the fourth marker 240, and can comprise statistical methods known to one of ordinary skill in the art such as principal component analysis and the construction of generalized logistic models. In various exemplary embodiments, an exemplary generalized logistic model can be constructed according to one or more of the following comparisons: i) AGA vs. LGA / SGA pregnancies, ii) AGA vs. SGA pregnancies, iii) AGA vs. LGA pregnancies, and iv) SGA vs. LGA pregnancies. In various exemplary embodiments, the statistical analysis 300 incorporates one or more machine learning algorithms.

[0043] Treatment options vary depending on the diagnosed FGD. For diagnoses of SGA, viable treatment options include but are not limited to adjusting patient nutrition to encourage baby growth, maternal rest to improve blood flow to the fetus, and corticoids treatment to induce lung maturation. For diagnoses of LGA, viable treatment options include but are not limited to adjusting patient nutrition to control maternal glucose levels and surgical procedure to avoid shoulder dystocia. In instances of SGA or LGA, another viable treatment option is early delivery by induction or emergency c-section. EXAMPLES EXAMPLE 1

[0044] In this Example, cirDNA from maternal blood was analyzed for diagnosing SGA or LGA. Further, cirDNA methylation of 10 candidate genes was analyzed in this assessment.Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 Materials and Methods

[0045] Pregnant women were prospectively recruited at initial Obstetrics visit, blood samples were collected by a certified phlebotomist during first 9-13 weeks of gestation and plasma fraction separated as detailed below. Maternal demographic and clinical information, as well as ultrasound data from prenatal controls was retrieved from existing records (i.e., PowerChart). After birth, prospectively recruited subjects were separated into three groups (SGA, LGA and AGA) according to the outcome of the pregnancy. SGA and LGA pregnancies were defined taking into account gestational age and weight at delivery, according to the revised reference chart for the US. Pregnancies with babies above 90% and below 10% percentiles will be regarded as LGA and SGA, respectively.

[0046] Plasma and cellular fractions from whole blood samples were separated by centrifugation at 1,800 rpm for 20 minutes. cirDNA from the plasma fraction was isolated using the Circulating Nucleic Acids kit (Qiagen, Valencia, CA), according to manufacturer’s instructions. Isolated cirDNA was stored at -80°C until use.

[0047] cirDNA amounts and fragmentation for each sample was quantitively assessed using SYBR-green based qPCR assays. In brief, total cirDNA amount was assessed by single-locus (i.e., KRAS) amplification and extrapolating to calibration curves. cirDNA fragmentation was assessed using a combination of three qPCR assays targeted to the same locus (i.e., KRAS). These assays share the same reverse primer, but the length of the assay differs due to the forward primer expanding to i) 357 bp representing long DNA fragments, ii) 145 bp representing nucleosome-sized (e.g., apoptotic) DNA fragments, and iii) 600 bp representing highly fragmented DNA. The relative enrichment of these three fractions was calculated as the ratio between the amplification (i.e., DDCt) and the DNA Fraction Index (DFI) was calculated for each sample. Mitochondrial DNA release into maternal blood was quantified by the Mitochondrial-Nuclear DNA ratio (MNR) according to the methods of Otandault, et. al. cirDNA methylation was measured using Methylation- Sensitive Restriction Enzyme qPCR (MSRE-qPCR) in 10 candidate genes selectedAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 according to previous reports on their involvement in placental homeostasis and fetal development (HSD2, RASSF1, CYP19A1, IGF2R, SLC16A10, IRS1, IL10, PTPRN2, SLC36A1, LEP). Primers for all qPCR assay are provided in Table 1.Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 Table 1: qPCR primers for cirDNA characterization 1. DNA quantification primers Locus Fragment F primer sequence R primer sequenceAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 SLC16A 115 GCTGGTAACTCGCGT AGCGGCTGCGCCTCG BstUI 10 CCCT (SEQ ID NO:18) CT (SEQ ID NO:19) I. . . analysis and variable plots were conducted using the factoextra package version 1.0.7. The molecular signature was extracted cirDNA markers and clinical attributes, and combined in generalized logistic models (GLM), according the following comparisons: i) AGA vs. LGA / SGA pregnancies, ii) AGA vs. SGA pregnancies, iii) AGA vs. LGA pregnancies, and iv) SGA vs. LGA pregnancies (Table 2). GLMs were built using the riskRegression (version 1.3.7) package. Empirical, binormal, and non-parametric statistical methods, eachAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 using randomForest (version 4.7.1), were the parameters leveraged in the construction of three ROC curves using R-package ROCit (version 2.1.1) Table 2: Generalized logistic models for AGA, SGA and LGA molecular signatures in maternal plasma samples AGA compared with SGA and LGA pregnancies combined Variable Coefficient (Intercept) 4.385498009 DNA.conc -0.01279025 frag.357.60 -2.44131494 frag.357.145 1.24497366 frag.145.60 -1.60747438 HSD2.methylation -2.89507858 RASSF1.methylation 0.146718432 CYP19A1.methylation 11.58241807 IGF2R.methylation 0.585137948 SLC16A10.methylation -2.153820021 IRS1.methylation -5.270003935 IL10.methylation -10.51680143 PTPRN2.methylation 9.572904547 SLC36A1.methylation 11.94714501 LEP.methylation -4.792703454 AGA compared with SGA pregnancies Variable Coefficient (Intercept) -393.9689287 DNA.conc -4.139110129 frag.357.60 39.31930104 frag.357.145 -11.69993123 frag.145.60 165.5102135 HSD2.methylation 212.466937 RASSF1.methylation 768.4719706Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 CYP19A1.methylation -1349.656813 IGF2R.methylation -68.14517047 SLC16A10.methylation 267.6507411 IRS1.methylation 46.95964117 IL10.methylation -173.7575997 PTPRN2.methylation -152.2931863 SLC36A1.methylation 88.03329019 LEP.methylation -146.9948932 AGA compared with LGA pregnancies Variable Coefficient (Intercept) -511.6783041 DNA.conc -0.943862588 frag.357.60 46.96788678 frag.357.145 -9.292666181 frag.145.60 140.8999876 HSD2.methylation 116.9013711 RASSF1.methylation 12.17467052 CYP19A1.methylation -1.23183611 IGF2R.methylation -92.58511867 SLC16A10.methylation 46.36299955 IRS1.methylation 67.56356887 IL10.methylation -123.1021541 PTPRN2.methylation 17.62002955 SLC36A1.methylation 168.2267322 LEP.methylation -15.45504754 SGA compared with LGA pregnancies Variable Coefficient (Intercept) -393.9689287 DNA.conc -4.139110129 frag.357.60 39.31930104Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 frag.357.145 -11.69993123 frag.145.60 165.5102135 HSD2.methylation 212.466937 RASSF1.methylation 768.4719706 CYP19A1.methylation -1349.656813 IGF2R.methylation -68.14517047 SLC16A10.methylation 267.6507411 IRS1.methylation 46.95964117 IL10.methylation -173.7575997 PTPRN2.methylation -152.2931863 SLC36A1.methylation 88.03329019 LEP.methylation -146.9948932

[0049] To study whether early gestation FGD pregnancies are associated with changes in cirDNA in maternal plasma, a comprehensive molecular characterization in a pilot set of SGA (n= 11), AGA (n=27), and LGA (n= 18) pregnancies was conducted. Blood samples were prospectively collected at first-trimester control visit and samples were later grouped according to weight and gestational age registered at delivery. cirDNA was isolated from the plasma fraction and specific qPCR assays designed for quantification of total plasma cirDNA were used, apoptotic (i.e., fragmented) cirDNA, and mitochondrial DNA, along with cirDNA methylation of 10 candidate genes, which have been previously reported as related to placenta homeostasis. Machine learning approaches were applied to build a molecular signature for the prediction of LGA and SGA occurrence. Prediction accuracy was assessed by Receiving-Operating Curve (ROC) analysis and Positive and Negative Predictive values (PPV and NPV, respectively) were calculated.

[0050] An increase in the total concentration of plasma cirDNA in SGA (mean cirDNA=9.40 ± 5.49 ng / mL) and LGA (mean cirDNA=12.36 ± 4.24 ng / mL) compared to AGA pregnancies (mean cirDNA=8.48 ± 3.16 ng / mL) was observed, although theAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 differences were not statistically significant (p=0.281; Kruskal-Wallis test; post-hoc Dunn test LGA-AGA p=0.788, SGA-AGA p=0.377, SGA-LGA p=1) (FIG.2A). The fraction of fragmented DNA was significantly increased in LGA (mean DFI=2.78 ± 1.46) and in SGA (mean DFI=2.37 ± 0.45) compared to AGA pregnancies (mean DFI=1.56 ± 0.71) (p=0.001; Kruskal-Wallis test; pLGA-AGA=0.004, and pSGA-AGA=0.019, Dunn’s test) (FIG.2B). Noteworthy, significant differences (p=0.005, Kruskal-Wallis test) in the ratio of mitochondrial / nuclear DNA among SGA, LGA and AGA pregnancies (FIG. 2C) were detected. Compared with AGA pregnancies, post hoc analysis revealed a significant increase in LGA (mean MNR=1.20 ± 1.18 and 0.23 ± 0.22, for LGA and AGA pregnancies, respectively, p=0.004, Dunn’s test), and a non-significant increase in SGA (mean MNR=0.78 ± 1.02 and 0.23 ± 0.22, for SGA and AGA pregnancies, respectively, p=0.004, Dunn’s test).

[0051] cirDNA methylation of 10 candidate genes, selected according to previous reports on their involvement in placental homeostasis and fetal development (HSD2, RASSF1, CYP19A1, IGF2R, SLC16A10, IRS1, IL10, PTPRN2, SLC36A1, LEP) (FIG. 2D), was studied. When comparing SGA, LGA and AGA pregnancies, significant differences in 5 out of the 10 genes (pHSD2=0.003, pRASSF1=0.018, pCYP19A1=0.008, pIL10=0.017 and, pLEP=0.002; Kruskal-Wallis test) were observed (FIG. 2E). Post hoc analysis revealed significant differences between SGA and AGA in 4 genes (pHSD2=0.034, pRASSF1=0.0182, pCYP19A1=0.013, and pLEP=0.004, Dunn’s test), between LGA and AGA in 2 genes (pHSD2=0.005, and pLEP=0.028, Dunn’s test), and between SGA and LGA in the IL10 gene (p=0.013, Dunn’s test). However, in the other 5 out of the ten genes, no significant differences were observed (pIGF2R=0.237, pSLC16A10=0.182, pIRS1=0.082, pPTPRN2=0.429, pSLC36A1=0.193) (FIG.2F).

[0052] Principal Component Analysis (PCA; FIGS. 3A-3B) showed that samples corresponding to SGA, LGA and AGA pregnancies clustered separately based upon the variables assessed in cirDNA isolated from first trimester maternal blood (FIG.3A). PCAAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 biplot of variables (FIG. 3B) revealed that decreases in cirDNA methylation in IL10, SLC36A1, PTPRN2 and LEP were the main variables dragging the sample distribution towards the SGA samples. Conversely, decreased cirDNA methylation in the HSD2 gene and increased DNA fragmentation dragged sample distribution towards LGA samples. Correlation with clinical variables also demonstrated a positive correlation between the levels of mitochondrial DNA in plasma and the presence of maternal hypertension. Remarkably, cirDNA methylation in SLC16A10, IRS1, PTPRN2 and SLC36A1 was positively correlated with the maternal age, but not with other clinical variables (FIG.3C).

[0053] Based on the observed potential discriminatory power for cirDNA profiles in the first gestational trimester, whether the variables assessed in cirDNA samples could be combined as a marker panel for early detection of FGD pregnancies was investigated (FIGS 4A-4D). Using generalized logistic models (GLM) (Table 2), models were created based on the molecular signature for distinguishing between AGA and LGA / SGA pregnancies in maternal plasma samples. The performance of each model was evaluated using three statistical methods (i.e., empirical, binomial, and non-parametric) and a cross- validation strategy. The first model (FIG. 4A) has a high power (87% accuracy, 80% sensitivity and 92% specificity) to discriminate FGDs in general, including both SGA and LGA, from AGA pregnancies (AUCs=0.96, 0.98, and 0.96, for empirical, binomial and non-parametric ROCs, respectively). The performance of the model was further evaluated according to positive predictive and negative predictive values for FGDs (PPV and NPV, respectively), achieving 89% PPV and 86% NPV. A second model (FIG. 4B) has high power (87% accuracy, 75% sensitivity and 92 % specificity) to differentiate specifically SGA pregnancies (AUCs=0.95, 0.96, and 0.93 for empirical, binomial, and non-parametric ROCs, respectively; PPV=75% and 92 % NPV). The third model (FIG.4C) has high power (93% accuracy, 67% sensitivity, and 100 % specificity) to differentiate specifically LGA pregnancies (AUCs=0.97, 0.98, and 0.96 for empirical, binomial, and non-parametric ROCs, respectively; PPV=100% and 92 % NPV). Lastly, a fourth model (FIG. 4D) with high power (81 % accuracy, 75 % sensitivity, and 83 % specificity) was generated toAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 differentiate between SGA and LGA pregnancies (AUCs=0.88, 0.92, and 0.87 for empirical, binomial, and non-parametric ROCs, respectively; PPV=60% and 83 % NPV).

[0054] Further exemplary diagnostic models were developed. A fifth model (FIG. 5A) demonstrates the highly sensitive, if less specific capacity (90% accuracy, 100% sensitivity, 81% specificity) to differentiate AGA pregnancies from LGA pregnancies (AUCs = 0.91, 0.88, and 0.88 for empirical, binomial, and non-parametric ROCs, respectively; PPV=84% and NPV=100%). A sixth model (FIG. 5B) demonstrates the highly specific capacity (87% accuracy, 77% sensitivity, 91% specificity) to differentiate AGA pregnancies from SGA pregnancies (AUCs = 0.94, 0.94, and 0.91 for empirical, binomial, and non-parametric ROCs, respectively; PPV=77% and NPV=91%). A seventh model (FIG. 5C) demonstrates the highly sensitive, specific, and accurate capacity (95% accuracy, 100% sensitivity, 93% specificity) to differentiate SGA pregnancies from LGA pregnancies (AUCs = 0.96, 0.95, and 0.93 for empirical, binomial, and non-parametric ROCs, respectively; PPV=87% and NPV=100%).

[0055] A two-way analysis of variance (ANOVA) test was performed to determine the effects of group differences for the groups SGA, AGA, and LGA and the presence of hypertension and diabetes, as well as their interactions, on the circulating DNA markers disclosed herein as studied in maternal plasma. As shown in Table 3, the mitochondrial DNA ratio and IL10 methylation markers showed significant correlation with variation in groups, with p-values of 0.002 and 0.001 respectively. Also of note are the methylation of IRS1 and LEP, which showed group interaction at p values of 0.071 and 0.074, respectively. Additionally, the interaction between group and hypertension was significant in the measured methylation of IL10 (p=0.035).

[0056] Furthermore, in the group / diabetes analysis, as shown in Table 4, the mitochondrial DNA ratio and IL10 methylation markers again showed significant group effects with p-values of 0.001 and 0.002 respectively. Also of note are the methylation of IRS1 and LEP, which showed group interaction at p values of 0.086 and 0.060, respectively. Methylation of IGF2R showed interaction with the diabetes condition at a pAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 value of 0.97. However, none of the markers shown in Table 4 demonstrated significant interaction between both group and diabetes.

[0057] In both Tables 3 and 4, very significant values are bolded, and moderately significant values are italicized.

[0058] Graphical representations of a multivariate analysis of the correlation between group, hypertension, and IL10 methylation, as indicated in Table 3, are provided in FIGS. 6A and 6B. There is a notable difference in IL-10 methylation levels between hypertensive and normotensive groups; specifically, the hypertensive group in the AGA group shows a higher IL-10 methylation level on average with a wider range of variability. However, the opposite is true in the SGA group. Hypertensive SGA samples showed much lower IL-10 methylation levels compared to the normotensive SGA samples, and with minimal variability.

[0059] Table 5 below provides the genomic context for the assays listed in Table 1. Specifically, Table 5 provides the genomic coordinates and the sequence of the assays listed, along with the primer sequences listed in Table 2. Table 5 also provides the length of the fragment, the corresponding restriction enzymes, and the sequence of probes in case the assays are running in a TaqMan instead of being SYBR-green based.Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 Table 3 -- Group / Hypertension Two-way ANOVA Test Group: Hypertension Hypertension GroupAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 Table 4 -- Group / Diabetes Two-way ANOVA Test Group: Diabetes Diabetes GroupAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 Table 35 -- Genomic Context for Assays DNA: : K :Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 fragmAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 fragm fragm(S Q O:36)Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 nucleAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 HSD2 RASS nO:5)Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 IGF2 SLC1 tionO:55)Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011(S Q O:58)Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 EXAMPLE 2

[0060] In this Example, cirDNA from the cellular fraction of maternal urine was analyzed for diagnosing SGA or LGA. Further, cirDNA methylation of 13 candidate genes was analyzed in this assessment. Materials and Methods Sample collection and storage:

[0061] Maternal urine samples were collected in a sterile collection cup at the initial visit during the first trimester and transferred to a 50 mL tube containing urine preservative (Urine Conditioning Buffer, Zymo Research Irvine, CA, Ca.t # D3061-1-8) at –80 °C until processing. Fraction separation and DNA isolation:

[0062] After thawing urine samples using a water bath at 37 °C, the cellular and fluid fractions for each sample were separated by centrifugation at 1,800 rpm for 20 minutes. DNA from the cellular and fluidic fractions were processed using the Quick Urine Kit (Zymo Research, Cat. #D3061) and the Quick cfDNA Serum and Plasma Kit (Zymo Research, Cat. #D4076), respectively, according to manufacturer instructions. DNA isolated from both fractions was stored at –80 °C until processing. Characterization of DNA isolated from the urine fluidic and cellular fractions:

[0063] DNA amounts and fragmentation for each sample was quantitively assessed using SYBR-green based qPCR assays as described in Almendros I, et al., Tumor circulating DNA profiling in xenografted mice exposed to intermittent hypoxia. Oncotarget. 2015;6(1):556-569. doi:10.18632 / oncotarget.2785. In brief, genomic DNAAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 and total cirDNA amounts were assessed by single-locus (i.e., KRAS) amplification and extrapolation to calibration curves. cirDNA fragmentation was assessed using a combination of three qPCR assays targeted to the same locus (i.e., KRAS). These assays share the same reverse primer, but the length of the assay differs due to the forward primer expanding to i) 357 bp representing long DNA fragments, ii) 145 bp representing nucleosome-sized DNA fragments, and iii) 60 bp representing highly fragmented DNA. The relative enrichment of these three fractions was calculated as the ratio between the amplification (i.e., ΔΔCt) and the DNA Fragmentation Index (DFI) was calculated for each sample. Mitochondrial DNA released into maternal blood and detected in maternal urine was quantified by the Mitochondrial-Nuclear DNA ratio (MNR) according to the methods of Otandault, et. al., Hypoxia differently modulates the release of mitochondrial and nuclear DNA. Br J Cancer. 2020;122(5):715-725. doi:10.1038 / s41416-019-0716-y. cirDNA methylation was measured using Methylation-Sensitive Restriction Enzyme qPCR (MSRE-qPCR) as described in Cortese et al., Epigenetic markers of prostate cancer in plasma circulating DNA. Hum Mol Genet. 2012;21(16):3619-3631. doi:10.1093 / hmg / dds192, and encompassed a variety of candidate genes selected according to previous reports on their involvement in placental homeostasis and fetal development. Primers for all qPCR assays are provided in Table 1. Building of molecular signature.

[0064] All data analysis was conducted in R (version 4.2.2). Principal component analysis and variable plots were conducted using the factoextra package version 1.0.7. The molecular signature was extracted based on cirDNA markers and clinical attributes, and combined in generalized logistic models (GLM), according to the following comparisons: i) AGA vs. LGA / SGA pregnancies, ii) AGA vs. SGA pregnancies, iii) AGA vs. LGA pregnancies, and iv) SGA vs. LGA pregnancies (Table 2). GLMs were build using the riskRegression (version 1.3.7) package. Empirical, binormal, and non-parametric statistical methods, each using randomForest (version 4.7.1), were the parameters leveraged in the construction of three ROC curves using R-package ROCit (version 2.1.1).Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 Results

[0065] FIGS.7A-7P provide bar plots with dot overlay for the markers including cirDNA concentration, cirDNA fragmentation, mitochondrial / nuclear DNA ratio, and methylation levels for each of the candidate genes studied.

[0066] A decrease in the total concentration of cellular cirDNA in LGA (mean cirDNA=1.153 ± 1.100 ng / mL) compared to AGA pregnancies (mean cirDNA=3.831 ± 3.961 ng / mL) and SGA pregnancies (mean cirDNA=3.473 ± 2.502 ng / mL) was observed, although the differences were not statistically significant (p=0.1011; Kruskal-Wallis test) (FIG. 7A). The fraction of fragmented DNA was significantly lower in LGA (mean DFI=0.3050 ± 0.3841) compared to AGA (mean DFI=1.144 ± 0.5772) pregnancies; SGA (mean DFI=0.6583 ± 0.6540) was also lower compared to AGA, but not significantly so (p=0.0258; Kruskal-Wallis test) (FIG.7B). Noteworthy, significant differences (p=0.0172, Kruskal-Wallis test) in the ratio of mitochondrial / nuclear DNA among SGA, LGA and AGA pregnancies (FIG.7C) were detected.

[0067] cirDNA methylation of 13 candidate genes (i.e., ADIPOQ, IGF2, INSR, SYN1, CYP19A1, HSD2, IGF2R, IL10, IRS1, LEP, PTPRN2, SLC16A10, and SLC36A1) selected according to previous reports on their involvement in placental homeostasis and fetal development were studied. When evaluated individually in the cellular fraction of the maternal urine, a decrease in HSD2 methylation was observed in SGA (mean=1.1950 ± 1.517) and LGA (mean=2.616 ± 1.320) compared to AGA (mean=6.417 ± 2.696) pregnancies, but this was only significant with respect to the SGA pregnancies (p- value=0.0296) (FIG.7D). A non-significant increase in CYP19A1 methylation was found in SGA (mean=10.14 ± 7.536) and LGA (mean=11.78 ± 4.980) compared to AGA (mean=5.991 ± 6.724) pregnancies (p-value=0.0931) (FIG. 7F). A non-significant decrease in SLC16A10 methylation was found in SGA (mean=111.9 ± 61.42) and LGA (mean=95.45 ± 21.21) compared to AGA (mean=150.1 ± 116.6) pregnancies (p- value=0.8640) (FIG. 7F). A non-significant increase in IRS1 methylation was found in SGA (mean=68.20 ± 32.49) and LGA (mean=63.50 ± 26.24) compared to AGAAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 (mean=51.23 ± 32.49) pregnancies (p-value=0.3152) (FIG.7G). A non-significant increase in IL10 methylation was found in SGA (mean=81.46 ± 34.02) and LGA (mean=73.87 ± 33.27) compared to AGA (mean=60.78 ± 17.52) pregnancies (p-value=0.3172) (FIG.7H). A non-significant increase in PTPRN2 methylation was found in SGA (mean=79.25 ± 34.61) and LGA (mean=84.82 ± 17.20) compared to AGA (mean=71.87 ± 49.54) pregnancies (p-value=0.7268) (FIG. 7I). A significant increase in SLC36A1 methylation was found in SGA (mean=92.97 ± 35.73) and a non-significant increase for LGA (mean=74.19 ± 22.56) compared to AGA (mean=43.21 ± 42.12) pregnancies (p- value=0.0333) (FIG.7J). A non-significant decrease in LEP methylation was found in SGA (mean=20.52 ± 2.344) and LGA (mean=20.03 ± 7.654) compared to AGA (mean=35.83 ± 34.15) pregnancies (p-value=0.5419) (FIG. 7K). A non-significant increase in ADIPOQ methylation was found in SGA (mean=21.32 ± 13.54) and a significant increase for LGA pregnancies (mean=30.08 ± 22.04) compared to AGA (mean=4.516 ± 4.360) pregnancies (p-value=0.0057) (FIG.7L). A non-significant increase in IGF2 methylation was found in SGA (mean=59.00 ± 35.61) and LGA (mean=75.96 ± 29.04) compared to AGA (mean=26.95 ± 43.83) pregnancies (p-value=0.2048) (FIG. 7M). No significant differences were found in INSR methylation for SGA (mean=72.13 ± 43.70) and LGA (mean=72.82 ± 15.41) compared to AGA (mean=72.41 ± 36.92) pregnancies (p- value=0.8391) (FIG.7N). No significant differences were found in SYN1 methylation for SGA (mean=82.28 ± 12.77) and LGA (mean=99.21 ± 25.36) compared to AGA (mean=84.96 ± 45.90) pregnancies (p-value=0.1429) (FIG. 7O). A non-significant decrease in IGF2R methylation was found in SGA (mean=60.61 ± 39.42) and LGA (mean=67.09 ± 27.28) compared to AGA (mean=76.74 ± 39.42) pregnancies (p- value=0.9959) (FIG.7P).

[0068] Overall ability for the model described in this example to differentiate between AGA, SGA, and LGA groups is provided in the Receiver Operating Characteristic curves of FIGS. 8A-8D. FIG. 8A shows that that AGA can be differentiated from SGA / LGA groups with an area under the curve (AUC) score of 0.9740, or an accuracy of approximately 97%. FIG.8B shows that that the AGA group can be differentiated from theAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 LGA group with an AUC score of 0.8571, or an accuracy of approximately 86%. FIG.8C shows that that the AGA group can be differentiated from the SGA group with an AUC score of 0.9857, or an accuracy of approximately 99%. FIG.8D shows that that the SGA group can be differentiated from the LGA group with an AUC score of 0.9792, or a success rate of approximately 98%. EXAMPLE 3

[0069] In this Example, cirDNA from the fluidic fraction of maternal urine was analyzed for diagnosing SGA or LGA. Further, cirDNA methylation of 12 candidate genes was analyzed in this assessment. Using the methods as described above, maternal urine was collected during standard regular OBGYN visit or even at home and can be conserved / shipped at room temperature using a preservative until further processing or freezing for longer storage. The liquid and cellular fraction of the urine are separated by centrifugation. Circulating DNA is isolated from fluidic and cellular fractions of the maternal urine separately. The total amount of plasma cirDNA in each subject is quantified using qPCR. cirDNA fragmentation is assessed using specific qPCR assays corresponding to intact, nucleosome-size, and fragmented DNA, respectively. The mitochondrial / nuclear cirDNA is calculated using specific qPCR assays. cirDNA methylation profiles was studied in 14 genes by DNA methylation Sensitive Restriction qPCR (MSRE-qPCR).

[0070] Furthermore, machine learning approaches were applied as described herein to build a molecular signature for the prediction of LGA and SGA occurrence using first trimester samples. Prediction accuracy was assessed by Receiving-Operating Curve (ROC) analysis and Positive and Negative Predictive values (PPV and NPV, respectively) were calculated.

[0071] To study whether early gestation FGD pregnancies are associated with changes in cirDNA in maternal urine, a comprehensive molecular characterization in a pilot set of SGA (n= 11), AGA (n=27), and LGA (n= 18) pregnancies was conducted. UrineAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 samples were prospectively collected at first-trimester control visit and samples were later grouped according to weight and gestational age registered at delivery. cirDNA was isolated from the fluidic fraction and specific qPCR assays designed for quantification of total fluidic cirDNA were used, apoptotic (i.e., fragmented) cirDNA, and mitochondrial DNA, along with cirDNA methylation of 12 candidate genes (i.e., ADIPOQ, INSR, SYN1, CYP19A1, HSD2, IGF2R, IL10, IRS1, LEP, PTPRN2, SLC16A10, and SLC36A1). Machine learning approaches were applied to build a molecular signature for the prediction of LGA and SGA occurrence. Prediction accuracy was assessed by Receiving-Operating Curve (ROC) analysis and Positive and Negative Predictive values (PPV and NPV, respectively) were calculated.

[0072] An increase in the total concentration of fluidic cirDNA in SGA (mean cirDNA=0.7186 ± 0.6522 ng / mL) and LGA (mean cirDNA=0.8411 ± 1.083 ng / mL) compared to AGA pregnancies (mean cirDNA=0.3363 ± 0.3910 ng / mL) was observed, although the differences were not statistically significant (p=0.1619; Kruskal-Wallis test) (FIG. 9A). The fraction of fragmented DNA was increased in LGA (mean DFI=49.16 ± 65.76) and in SGA (mean DFI=60.05 ± 107.3) compared to AGA pregnancies (mean DFI=3.650 ± 4.451), although differences were not statistically significant (p=0.9465; Kruskal-Wallis test) (FIG. 9B). An increase on the ratio of mitochondrial / nuclear DNA was observed in SGA (mean MNR=612.8 ± 932.5) and LGA (mean MNR=208.3 ± 344.4) compared to AGA (mean MNR=218.6 ±835.3) pregnancies (FIG. 9O), although these differences were only significant between SGA and AGA groups (p=0.0358; Kruskal Wallis test).

[0073] cirDNA methylation of candidate genes (FIGS.9C-9N), was studied. When evaluated individually in the fluidic fraction of the maternal urine, no significant change in ADIPOQ methylation was observed in SGA (mean=52.95 ± 41.70) and LGA (mean=42.92 ± 27.71) compared to AGA (mean=56.00 ± 95.43) pregnancies (p-value=0.3458 (FIG.9C). A non-significant increase in INSR methylation was found in SGA (mean=74.06 ± 67.66) and LGA (mean=47.02 ± 25.62) compared to AGA (mean=40.13 ± 26.98) pregnancies (p-Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 value=0.91971) (FIG.9D). A non-significant decrease in SYN1 methylation was found in SGA (mean=85.68 ± 36.91) and LGA (mean=86.90 ± 30.59) compared to AGA (mean=245.0 ± 148.0) pregnancies (p-value=0.1159) (FIG.9E). No significant change in CYP19A1 methylation was found in SGA (mean=36.49 ± 18.36) and LGA (mean=40.00 ± 41.79) compared to AGA (mean=30.30 ± 26.42) pregnancies (p-value=0.6665) (FIG. 9F). No significant change in HSD2 methylation was found in SGA (mean=31.23 ± 40.42) and LGA (mean=25.41 ± 29.68) compared to AGA (mean=32.29 ± 61.57) pregnancies (p- value=0.9187) (FIG.9G). A non-significant increase in IGF2R methylation was found in SGA (mean=59.99 ± 17.54) and LGA (mean=85.21 ± 53.41) compared to AGA (mean=45.21 ± 38.39) pregnancies (p-value=0.3495) (FIG.9H). A non-significant increase in IL10 methylation was found in SGA (mean=247.6 ± 205.9) and a non-significant decrease for LGA (mean=133.7 ± 49.41) compared to AGA (mean=156.5 ± 78.98) pregnancies (p-value=0.6688) (FIG. 9I). A non-significant increase in IRS1 methylation was found in SGA (mean=104.9 ± 48.36) and LGA (mean=104.3 ± 39.77) compared to AGA (mean=75.41 ± 51.79) pregnancies (p-value=0.1731) (FIG. 9J). No significant change in LEP methylation was found in SGA (mean=43.22 ± 33.85) and a significant increase for LGA pregnancies (mean=33.97 ± 16.33) compared to AGA (mean=45.72 ± 54.22) pregnancies (p-value=0.9286) (FIG. 9K). A non-significant increase in PTPRN2 methylation was found in SGA (mean=224.7 ± 165.2) and LGA (mean=137.8 ± 75.24) compared to AGA (mean=113.5 ± 41.14) pregnancies (p-value=0.4671) (FIG. 9L). No significant differences were found in SLC16A10 methylation for SGA (mean=245.7 ± 269.0) and LGA (mean=110.8 ± 86.88) compared to AGA (mean=181.8 ± 250.6) pregnancies (p-value=0.7968) (FIG. 9M). No significant differences were found in SLC36A1 methylation for SGA (mean=205.1 ± 150.1) and LGA (mean=135.8 ± 47.83) compared to AGA (mean=148.7 ± 159.1) pregnancies (p-value=0.2978) (FIG.9N).

[0074] Overall ability for the model described in this example to differentiate between AGA, SGA, and LGA groups in the fluidic fraction is provided in the Receiver Operating Characteristic curves of FIGS.10A-10D. FIG.10A shows that that AGA can be differentiated from SGA / LGA groups with an area under the curve (AUC) score of 0.8958,Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 or an accuracy of approximately 90%. FIG. 10B shows that that the AGA group can be differentiated from the LGA group with an AUC score of 0.9490, or an accuracy of approximately 95%. FIG.10C shows that that the AGA group can be differentiated from the SGA group with an AUC score of 0.8333, or an accuracy of approximately 83%. FIG. 10D shows that that the SGA group can be differentiated from the LGA group with an AUC score of 0.9792, or an accuracy of approximately 98%.

[0075] A model built on the combined fluidic (circulating) and genomic (cellular) DNA data was also studied, and ROC curves demonstrating the efficacy of this model are provided in FIGS. 11A-11D. FIG. 11A shows that that AGA can be differentiated from SGA / LGA groups with an area under the curve (AUC) score of 0.8958, or an accuracy of approximately 90%. FIG. 1`B shows that that the AGA group can be differentiated from the LGA group with an AUC score of 0.8571, or an accuracy of approximately 86%. FIG. 11C shows that that the AGA group can be differentiated from the SGA group with an AUC score of 0.9857, or an accuracy of approximately 99%. FIG.11D shows that that the SGA group can be differentiated from the LGA group with an AUC score of 0.9167, or a success rate of approximately 92%.

Claims

Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 CLAIMS What is claimed is:

1. A set of marker panels comprising: a. a first marker panel for determining the total amount of circulating DNA (“cirDNA”) or genomic DNA in a DNA-containing sample, said DNA- containing sample including mitochondrial DNA and nuclear DNA; b. a second marker panel for determining at least one physical characteristic of the cirDNA and / or genomic DNA; c. a third marker panel for determining the ratio of mitochondrial DNA to nuclear DNA in the DNA-containing sample; and d. a fourth marker panel for assessing DNA methylation in a plurality of genes in the DNA-containing sample.

2. The set of marker panels of claim 1, wherein the plurality of genes in the fourth marker panel are selected from the group consisting of HSD2, RASSF1, CYP19A1, IGF2R, SLC16A10, IRS1, IL10, PTPRN2, SLC36A1, LEP, ADIPOQ, IGF2, INSR, SYN1, and any combination thereof.

3. The set of marker panels of claim 2, wherein the DNA-containing sample is from blood or urine.

4. The set of marker panels of claim 2, wherein the at least one physical characteristic in the second marker panel is a fragmentation analysis.

5. The set of marker panels of claim 2, wherein the plurality of genes in the fourth marker panel are selected from the group consisting of HSD2, RASSF1, CYP19A1, IGF2R, SLC16A10, IRS1, IL10, PTPRN2, SLC36A1, LEP, and any combination thereof.

6. The set of marker panels of claim 2, wherein the plurality of genes in the fourth marker panel are selected from the group consisting of ADIPOQ, INSR,Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 SYN1, CYP19A1, HSD2, IGF2R, IL10, IRS1, LEP, PTPRN2, SLC16A10, SLC36A1, and any combination thereof.

7. A method of determining at least one fetal growth disorder, comprising the steps of: obtaining a maternal fluid sample comprising DNA; measuring molecular markers, said markers comprising: a first marker that measures the amount of DNA; a second marker that is a fragmentation analysis of the DNA; a third marker that measures a ratio of mitochondrial DNA to nuclear DNA in the DNA; a fourth marker that assesses the DNA for the presence of at least one of the DNA methylation marker from the list consisting of: HSD2.methylation, RASSF1.methylation, CYP19A1.methylation, IGF2R.methylation, SLC16A10.methylation, IRS1.methylation, IL10.methylation, PTPRN2.methylation, SLC36A1.methylation, LEP.methylation, ADIPOQ.methylation, IGF2.methylation, INSR.methylation, and SYN1.methylation; and, integrating the first marker, the second marker, the third marker, and the fourth marker into a statistical analysis that correlates the markers with at least one fetal growth disorder and thereby predicts the presence or absence of at least one fetal growth disorder.

8. The method of claim 7 wherein the fragmentation analysis is performed using a set specific qPCR assays comprising assays that measure amounts of intact DNA, assays that measure amounts of nucleosome-sized DNA, and assays that measure amounts of fragmented DNA.

9. The method of claim 7 wherein the maternal fluid comprises blood or urine.Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 10. The method of claim 7 wherein the DNA comprises circulating DNA and / or genomic DNA.

11. A method of treatment for a fetal growth disorder comprising the steps of: diagnosing the fetal growth disorder, said diagnosis comprising the steps of: obtaining a maternal fluid sample comprising DNA; measuring molecular markers, said markers comprising: a first marker that measures the amount of DNA; a second marker that is a fragmentation analysis of the DNA; a third marker that measures a ratio of mitochondrial DNA to nuclear DNA in the DNA; a fourth marker that assesses the DNA for the presence of at least one diagnostic DNA methylation marker; and, integrating the first marker, the second marker, the third marker, and the fourth marker into a statistical analysis that correlates the markers with and thereby predicts the presence of the fetal growth disorder; selecting one or more appropriate treatments for the diagnosed fetal growth disorder; and, administering the one or more appropriate treatments.

12. The method of treatment of claim 11 wherein the diagnostic methylation marker is selected from the list consisting of: HSD2.methylation, RASSF1.methylation, CYP19A1.methylation, IGF2R.methylation, SLC16A10.methylation, IRS1.methylation, IL10.methylation, PTPRN2.methylation, SLC36A1.methylation, LEP.methylationAtty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 ADIPOQ.methylation, IGF2.methylation, INSR.methylation, and SYN1.methylation.

13. The method of treatment of claim 11 wherein the one or more appropriate treatments comprise nutritional adjustment for a mother during pregnancy.

14. The method of treatment of claim 11 wherein the one or more appropriate treatments comprise early delivery of a fetus by induction or emergency c- section.

15. The method of treatment of claim 11 wherein the maternal fluid comprises blood or urine.

16. The method of treatment of claim 11 wherein the DNA comprises circulating DNA and / or genomic DNA.

17. A multiplex PCR kit for the early detection of at least one fetal growth disorder from maternal fluid, comprising: a. a first set of primers corresponding to a first marker that measures the amount of DNA in the maternal fluid; b. a second set of primers corresponding to a second marker that is a fragmentation analysis of the DNA; c. a third set of primers corresponding to a third marker that measures a ratio of mitochondrial DNA to nuclear DNA in the DNA; and, d. a fourth set of primers corresponding to a fourth marker that assesses the DNA for the presence of at least one diagnostic DNA methylation marker.

18. The multiplex PCR kit of claim 17 wherein the fourth set of primers correspond to methylation markers selected from the list consisting of: HSD2.methylation, RASSF1.methylation, CYP19A1.methylation, IGF2R.methylation, SLC16A10.methylation, IRS1.methylation, IL10.methylation, PTPRN2.methylation, SLC36A1.methylation,Atty. Dkt. No.17193.0137; UMCO 40485WO Client Ref. No.24UMC011 LEP.methylation ADIPOQ.methylation, IGF2.methylation, INSR.methylation, and SYN1.methylation.

19. The multiplex PCR kit of claim 17 wherein the maternal fluid comprises blood or urine.

20. The multiplex PCR kit of claim 17 wherein the DNA comprises circulating DNA and / or genomic DNA.

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