WO2024240700A1 - Methods, tools and systems for the prediction and assessment of gestational diabetes - Google Patents

Abstract

A method of assessing Gestational Diabetes Mellitus (GDM) susceptibility in a female human subject. The method comprising determining the identity of at least one allele at each of at least two positions of single nucleotide polymorphism (SNP) selected from List 1 or List 2.

Description

Methods, Tools and Systems for the Prediction and Assessment of Gestational Diabetes

This application claims priority from US 63/467,826 filed 19 May 2023, the contents and elements of which are herein incorporated by reference for all purposes.

Field of the Invention

The present invention relates to methods and products, in particular arrays and related systems, for in vitro genotyping of Gestational Diabetes Mellitus (GDM) associated genetic variations and to methods for assessment of gestational diabetes risk among pregnant females.

Background to the Invention

Gestational diabetes mellitus (GDM) , defined as hyperglycemia with onset or first recognition during pregnancy, is associated with an increased risk of pregnancy complications and adverse perinatal outcomes, including preeclampsia, stillbirth, large for gestational age, neonatal hypoglycemia, preterm birth, low Apgar scores, and admission to neonatal intensive care. [1-3]

Fetal exposure to diabetes in utero has been linked to macrosomia and adiposity in newborns and impaired glucose tolerance and obesity in childhood, thereby increasing risks for adverse cardiometabolic outcomes later in life. [4,5]

While hyperglycemia commonly resolves post-partum, GDM can reoccur and is often associated with a subsequent diagnosis of type 2 diabetes (T2D) and coronary heart disease. [2,3] Although the global prevalence of GDM is increasing at a concerning rate, [6] it varies according to population characteristics (eg, maternal age, ancestry, and obesity rates) and the criteria used for screening and diagnosis. [7] In Mexico, the estimated prevalence of GDM in 2021 was 11.2%. [8] Unfortunately, GDM is detected in only about 1% of cases in Mexico, and glucometers and glucose strips are generally not available for glucose self-monitoring .

Early risk stratification by prediction modeling might offer opportunities to improve care for those women at high risk of developing GDM . As timely intervention is key to preventing adverse outcomes in GDM, [ 9 ] clinicians need simple prediction models that can be used in the first trimester of pregnancy . Clinical multivariate GDM risk prediction models have been proposed . [ 10-12 ] However , these novel measures of biochemical and clinical markers have not been thoroughly examined and the equations are complex, making these prediction models difficult to use in routine clinical practice .

In contrast to T2D, there are relatively few published studies on the genetic susceptibility to GDM, and despite the high incidence in Mexico , studies on the genetic architecture of GDM in the Mexican population are lacking . To our knowledge , only a single study by Huerta-Chagoya et al [ 13 ] has provided insight into the genetic factors of GDM in Mexican women, confirming that T2D and GDM share a common genetic background and suggesting that other genetic mechanisms may be in play for GDM . Before that , Watanabe et al identified the association between variants in TCF7L2 and GDM in a small study of 152 women of Mexican American origin . [ 14 ] A metaanalysis by Lowe et al [ 15 ] that included the original genome-wide association study ( GWAS ) of glycemic traits in pregnancy in the Hyperglycemia and Adverse Pregnancy Outcomes ( HAPO ) Study, multiple ethnic groups from the HAPO Study, and two other pregnancy cohorts [ 16 , 17 ] has significantly contributed to the identification of genetic variants associated with GDM . While these studies were significant and their scope was expansive , women of Hispanic ancestry represented <10% of the participants . More recently, within the GENetics of Diabetes In Pregnancy Consortium, Pervj akova et al conducted the largest and most ancestrally diverse GWAS metaanalysis for GDM that included a total of 5485 women with GDM and 347856 without ; of those , 2 . 8% were of Hispanic/Latino origin . [ 18 ]

Powe et al [ 19-21 ] have also provided insight into the genetic heterogeneity among women with GDM . They examined polygenic scores for T2D, fasting glucose , fasting insulin, insulin secretion, and insulin resistance among women with different physiologic subtypes of GDM . Their genotype-based approach to heterogeneity in GDM suggested that genetic data provide both information on GDM risk and distinct genetic information pointing to phenotypic data .

The Nurses ' Health Study II and the Danish National Birth Cohort were used by Ding et al [ 22 ] to identify eight novel genetic variants and create a genetic risk score based on those variants .

Furthermore , in genetic variant analysis of the Europe-wide vitamin D and lifestyle intervention randomized controlled trial , an interaction between MTNRIB variants and lifestyle intervention in regard to maternal and neonatal outcomes was identified . [ 23 ]

There remains an unmet need for reliable predictors of GDM susceptibility, particularly for use in ethnically diverse populations such as that of Mexico . There also is a need for identification of specific genetic factors that are indicative of a need for intensified supplementation regimes that can have help with treating GDM . The present invention address this and other needs .

Summary of the Invention

Broadly, the present inventors have found that certain combinations of polymorphisms , particularly single nucleotide polymorphisms ( SNPs ) , are associated with prediction of Gestational Diabetes Mellitus (GDM) risk . Further, combinations of SNPs selected for particular suitability to Mexican, Latin American, and European populations , among others , have been identified herein .

Additionally, clinical variables have been identified that are associated with GDM risk and can be used for prediction . Tools and associated systems have been developed for use in methods of the invention, including for the prediction of GDM susceptibility among pregnant women .

Accordingly, in a first aspect the present invention provides a method of assessing Gestational Diabetes Mellitus ( GDM) susceptibility in a female human subj ect , the method comprising determining the identity of at least one allele at each of at least 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , or 11 positions of single nucleotide polymorphism ( SNP ) selected from :

PROXI - RS340874 ; LOC10012835 RS1387153;

RSP03 RS2745353;

IRS1 RS2943634;

GCK RS4607517;

F0XA2 RS6048205;

RBMS1 RS6742799;

RREB1 RS9379084;

MTNRIB RS10830963;

AMT RS11715915;

CILP2 RS16996148.

The SNPs may be as disclosed in the NCBI dbSNP, Homo sapiens genome build 37.

The method may comprise determining the identity of at least one allele at all of the positions of single nucleotide polymorphism (SNP) of List 1.

In the method, the presence of one or more of the following risk alleles may indicate that the subject has greater susceptibility to GDM:

C at RS340874;

T at RS1387153;

T at RS2745353;

C at RS2943634;

A at RS4607517;

A at RS6048205;

A at RS6742799;

G at RS9379084;

G at RS10830963;

C at RS11715915; and

T at RS16996148.

The method may further comprise determining the identity of at least one allele at each of at least two positions of single nucleotide polymorphism (SNP) selected from List 2

List 2 CCND2 RS1106306;

LOC100128354/MTNR1B RS1387153

MTHFR RS1801133;

MTR RS1805087;

LEP RS2167270;

IRS1 RS2943634;

CUBN RS11254363;

ARAP1 RS 11605924; and

GPSM1 RS11787792; The method may comprise determining the identity of at least one allele at all of the positions of single nucleotide polymorphism (SNP) of List 2.

In the method, the presence of one or more of the following risk alleles may indicate that the ubject has greater susceptibility to GDM :

C at RS1106306;

T at RS1387153

A at RS1801133;

G at RS1805087;

G at RS2167270;

C at RS2943634;

G at RS11254363;

A at RS11605924; and

A at RS11787792;

Accordingly, in a second aspect the present invention provides a method of assessing Gestational Diabetes Mellitus (GDM) susceptibility in a female human subject, the method comprising determining the identity of at least one allele at each of at least two positions of single nucleotide polymorphism (SNP) selected from List 2 :

List 2

CCND2 - RS1106306;

LOC100128354/MTNR1B - RS1387153

MTHFR - RS1801133;

MTR - RS1805087; LEP - RS2167270;

IRS1 - RS2943634;

CUBN - RS11254363;

ARAP1 - RS11605924; and

GPSM1 - RS11787792; The method may comprise determining the identity of at least one allele at all of the positions of single nucleotide polymorphism (SNP) of List 2.

In the method, the presence of one or more of the following risk alleles may indicate that the subject has greater susceptibility to GDM :

C at RS1106306;

T at RS1387153

A at RS1801133;

G at RS1805087;

G at RS2167270;

C at RS2943634;

G at RS11254363;

A at RS11605924; and

A at RS11787792;

The method may further comprise determining the identity of at least one allele at each of at least two positions of single nucleotide polymorphism (SNP) selected from List 1:

List 1

PROXI - RS340874;

LOC10012835 - RS1387153;

RSPO3 - RS2745353;

IRS1 - RS2943634;

IGF2BP2 - RS4402960

F0XA2 - RS6048205;

RBMS1 - RS6742799;

RREB1 - RS9379084;

MTNRIB - RS10830963;

AMT - RS11715915; and

CILP2 - RS16996148. The method may comprise determining the identity of at least one allele at all of the positions of single nucleotide polymorphism (SNP) of List 1.

In the method the presence of one or more of the following risk alleles may indicate that the subject has greater susceptibility to GDM:

C at RS340874;

T at RS1387153;

T at RS2745353;

C at RS2943634;

A at RS4607517;

A at RS6048205;

A at RS6742799;

G at RS9379084;

G at RS10830963;

C at RS11715915; and

T at RS16996148.

The method may further comprise determining the identity of at least one allele of at least one position of SNP selected from:

SLC19A11 - RS1051266;

MTHFR - RS1801131; and

RREB1 - RS41302867.

RS1051266 is a SNP of SLC19A11 which is strongly associated with

GDM. RS1801131 is a SNP of MTHFR which is strongly associated with GDM. RS41302867 is a SNP of RREB1 and is in linkage disequilibrium with RS9379084 from List 1.

The method may further comprise assessing one or more of the following clinical variables associated with the subject: a) the age of the subject, b) the pregestational BMI of the subject, c) whether the subject has any family history of type 2 diabetes (T2D) , or d) whether the patient has previously been pregnant. In the method, allele determination may be carried out at not more than 50, 40, 30, 25, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, or 9 SNP positions .

The method may comprise determining the identity of both alleles at each SNP thereby obtaining the genotype of the subject at each SNP.

In the method the subject may be determined to be heterozygous or to be homozygous for said risk allele at at least one of said SNPs .

The method may comprise determining the identity of at least one allele at all of the listed positions of single nucleotide polymorphism (SNP) .

The method may comprise assaying a DNA-containing sample that has previously been obtained from said subject.

The sample may be selected from the group consisting of : blood, hair, skin, amniotic fluid, buccal swab, saliva, and feces.

The method may comprise isolating and/or amplifying genomic DNA from said subject.

In the method, determining the identity of said at least one allele at each SNP may comprise: probe hybridization, real time PCR, array analysis, bead analysis, primer extension, restriction analysis and/or DNA sequencing.

The method may comprise determining the number of and identity of SNP risk alleles, and wherein the method may further comprise computing a GDM risk score for said subject based on the number and identity of said SNP risk alleles.

The method may comprise inputting the SNP risk allele determinations into a probability function to compute said risk score.

In the method, the subject is a female of reproductive age. In particular, the subject may be pregnant (e.g. may have tested positive in a pregnancy test) . In some cases, the subject may be of < 20 gestational weeks. In the method, the subj ect may be of Mexican, Latino American, or European origin or ancestry . In some cases the subj ect may be of African-American, Afro-Caribbean, South Asian, Polynesian, Native American or Hispanic origin .

In the method, the subj ect may have at least one first degree relative who has , or has previously been diagnosed with, GDM and/or type 2 diabetes ( T2D) .

In the method, the subj ect may have one or more clinical risk factors for GDM selected from: pregestational body mass index ( BMI ) > 30 ; waist circumference > 80 cm; age > 35 ; diagnosis of polycystic ovary syndrome ; a diagnosis of GDM during a previous pregnancy; is a smoker; a previous pregnancy that resulted in a child with birth weight > 90^th centile ; and a previous diagnosis of prediabetes , impaired glucose tolerance or impaired fasting glycaemia .

In the method, the subj ect may be determined to carry one or more of said risk alleles at one or more of said SNPs and therefore to be at greater ris k of GDM, the method may further comprise administering to the subj ect a test selected from the group consisting of : an oral glucose tolerance test (OGTT ) ; a non-challenge blood glucose test ; a screening glucose challenge test ; and a urinary glucose test .

In the method, the subj ect may be determined to carry one or more of said risk alleles at one or more of said SNPs and therefore to be at greater ris k of GDM, the method further comprising an intervention selected from the group consisting of : a low glycaemic index (GI ) diet , increased exercise , insulin therapy, and anti-diabetic medication .

Accordingly, in a third aspect the present invention provides a method of treating or preventing Gestational Diabetes Mellitus ( GDM) in a female human subj ect , the method comprising determining the identity of at least one allele at each of at least two positions of single nucleotide polymorphism ( SNP ) selected from List 1 :

List 1

PROXI - RS340874 ; LOC10012835 - RS1387153;

RSPO3 - RS2745353;

IRS1 - RS2943634;

GCK - RS4607517;

FOXA2 - RS6048205;

RBMS1 - RS6742799;

RREB1 - RS9379084;

MTNRIB - RS10830963;

AMT - RS11715915; and

CILP2 - RS16996148; determining the subject to be at risk of GDM, and administering a medicament for treating or preventing GDM.

Accordingly, in a fourth aspect the present invention provides a method of treating or preventing Gestational Diabetes Mellitus (GDM) in a female human subject, the method comprising determining the identity of at least one allele at each of at least two positions of single nucleotide polymorphism (SNP) selected from List 2:

List 2

CCND2 - RS1106306;

LOC100128354/MTNR1B - RS1387153

MTHFR - RS1801133;

MTR - RS1805087;

LEP - RS2167270;

IRS1 - RS2943634;

CUBN - RS11254363;

ARAP1 - RS11605924; and

GPSM1 - RS11787792.

The method of treating or preventing GDM may further comprise determining the identity of at least one allele of at least one position of SNP selected from:

SLC19A11 - RS1051266;

MTHFR - RS1801131; and

RREB1 - RS41302867. The medicament for treating or preventing GDM may be selected from any one of the group consisting of : a) insulin, b) metformin, c) a sulphonylurea medicament, d) a meglitinide, e) an alpha-glucosidase inhibitor, f) a thiazolidinedione , g) a DPP-4 inhibitor, h) an incretin mimetic, and i) an amylin analogue.

In the method, the subject may be determined to carry one or more of said risk alleles at one or more of said SNPs and therefore to be at greater risk of GDM, the method may further comprise administering to the subject a test selected from the group consisting of: an oral glucose tolerance test (OGTT) ; a non-challenge blood glucose test; a screening glucose challenge test; and a urinary glucose test.

In the method, the subject is determined to carry one or more of said risk alleles at one or more of said SNPs and therefore to be at greater risk of GDM, the method further comprising an intervention selected from the group consisting of: a low glycaemic index (GI) diet, increased exercise, insulin therapy, and anti-diabetic medication .

Accordingly, in a fifth aspect the present invention provides a genotyping tool for use in a method according to first, second, third or fourth aspects; said tool comprising an array having a plurality of oligonucleotide probe pairs, each of said probe pairs comprising a first probe specific for a first allele of a single nucleotide polymorphism (SNP) and a second probe specific for a second allele of the SNP, wherein said plurality of oligonucleotide probe pairs comprises probe pairs that interrogate at least two SNPs selected from List 1 : List 1

PROXI RS340874;

LOC10012835 RS1387153;

RSPO3 RS2745353;

IRS1 RS2943634;

GCK RS4607517;

FOXA2 RS6048205;

RBMS1 RS6742799;

RREB1 RS9379084;

MTNRIB RS10830963;

AMT RS11715915; and

CILP2 RS16996148;

The genotyping tool may comprise probe pairs that interrogate all of the SNPs of List 1.

The genotyping tool may comprise further probe pairs that interrogate at least two SNPs selected from List 2 : List 2

CCND2 - RS1106306;

LOC100128354/MTNR1B - RS1387153

MTHFR - RS1801133;

MTR - RS1805087;

LEP - RS2167270;

IRS1 - RS2943634;

CUBN - RS11254363;

ARAP1 - RS11605924; and

GPSM1 - RS11787792;

The genotyping tool may comprise probe pairs that interrogate all of the SNPs of List 2.

Accordingly, in a sixth aspect the present invention provides a genotyping tool for use in a method according to first, second, third or fourth aspects; said tool comprising an array having a plurality of oligonucleotide probe pairs, each of said probe pairs comprising a first probe specific for a first allele of a single nucleotide polymorphism (SNP) and a second probe specific for a second allele of the SNP, wherein said plurality of oligonucleotide probe pairs comprises probe pairs that interrogate at least two SNPs selected from List 2 :

List 2

CCND2 RS1106306;

LOC100128354/MTNR1B RS1387153

MTHFR RS1801133;

MTR RS1805087;

LEP RS2167270;

IRS1 RS2943634;

CUBN RS11254363;

ARAP1 RS 11605924; and

GPSM1 RS11787792;

The genotyping tool may comprise probe pairs that interrogate all of the SNPs of List 2.

The genotyping tool may comprise further probe pairs that interrogate at least two SNPs selected from List 1 :

List 1

PROXI - RS340874;

LOC10012835 - RS1387153;

RSPO3 - RS2745353;

IRS1 - RS2943634;

GCK - RS4607517;

FOXA2 - RS6048205;

RBMS1 - RS6742799;

RREB1 - RS9379084;

MTNRIB - RS10830963;

AMT - RS11715915; and

CILP2 - RS16996148;

The genotyping tool may comprise probe pairs that interrogate all of the SNPs of List 1.

In the genotyping tool, the probe pairs that interrogate the SNPs selected from List 1 and/or List 2, make up at least 50% of the total number of nucleic acid probes in the array. The genotyping tool may further comprise probe pairs that interrogate at least one of the SNPs selected from :

SLC19A11 RS1051266 ;

MTHFR RS1801131 ; and

RREB1 RS41302867 .

In the genotyping tool , the total number of different SNPs for which allele-specific probes are provided may not exceed 50 , 40 , 30 , 25 , 20 , 19 , 18 , 17 , 16 , 15 , 14 , 13 , 12 , 11 , 10 , or 9 .

The genotyping tool may be in the form of a TaqMan® OpenArray® SNP genotyping platform, a Dynamic Array integrated fluidic circuits ( IFC ) genotyping platform, a next-generation sequencing system, or a Mass ARRAY® system.

In the genotyping tool , the allele-specific oligonucleotide probes may each be covalently attached to a fluorophore .

The genotyping tool may further comprise a primer pair for each of said SNPs , said primer pair for each SNP comprising an oligonucleotide primer that hybridizes to a target sequence upstream of the SNP and an oligonucleotide primer that hybridizes to a target sequence downstream of the SNP .

The genotyping tool may further comprise one or more reagents for amplification of DNA comprising said SNPs and/or for detection of said allele-specific probes .

Accordingly, in a seventh aspect the present invention provides a Gestational Diabetes Mellitus ( GDM) ris k assessment system for use in a method according to the first , second, third, or fourth aspect of the invention, the system comprising a genotyping tool as defined in the fifth or sixth aspect of the invention, and a computer programmed to compute a GDM ris k score from the genotype data of the subj ect at each of at least two SNPs selected from the SNPs set forth in List 1 or List 2 .

The method according to the first , second, third, or fourth aspect of the invention may employ a genotyping tool as defined in the fifth or sixth aspect of the invention and/or employ a GDM risk assessment system as defined in the seventh aspect of the invention.

Brief Description of the Figures

Figure 1 shows violin plots of genetic risk scores distribution incases and controls. The distribution of the risk values for the control and case groups is displayed.

Figure 2 shows violin plots of genetic risk scores distribution incases and controls. The distribution of the risk values for the control and case groups is displayed.

Detailed Description

Single nucleotide polymorphisms (SNPs)

SNPs are identified herein using the rs identifier numbers in accordance with the NCBI dbSNP database, which is publically available at: http://www.ncbi.nlm.nih.gov/projects/SNP/ . As used herein, rs numbers refer to the dbSNP Homo sapiens build 37.1 available from 2 February 2010.

Linkage diseguilibrium (LD)

In some embodiments, SNPs in linkage disequilibrium with the SNPs associated with the invention are useful for obtaining similar results . As used herein, linkage disequilibrium refers to the nonrandom association of SNPs at two or more loci. Techniques for the measurement of linkage disequilibrium are known in the art. As two SNPs are in linkage disequilibrium if they are inherited together, the information they provide is correlated to a certain extent.

SNPs in linkage disequilibrium with the SNPs included in the models can be obtained from databases such as HapMap or other related databases, from experimental setups run in laboratories or from computer-aided in silico experiments. Determining the genotype of a subject at a position of SNP as specified herein, e.g. as specified by NCBI dbSNP rs identifier, may comprise directly genotyping, e.g. by determining the identity of the nucleotide of each allele at the locus of SNP, and/or indirectly genotyping, e.g. by determining the identity of each allele at one or more loci that are in linkage disequilibrium with the SNP in question and which allow one to infer the identity of each allele at the locus of SNP in question with a substantial degree of confidence . In some cases , indirect genotyping may comprise determining the identity of each allele at one or more loci that are in sufficiently high linkage disequilibrium with the SNP in question so as to allow one to infer the identity of each allele at the locus of SNP in question with a probability of at least 90% , at least 95 % or at least 99% certainty .

As will be appreciated by the reader , in some cases one or more polymorphisms or alterations in linkage disequilibrium with a polymorphism or alteration disclosed herein may find use the methods of the present invention . Linkage disequilibrium (LD ) is a phenomenon in genetics whereby two or more mutations or polymorphisms are in such close genetic proximity that they are coinherited . This means that in genotyping, detection of one polymorphism as present infers the presence of the other . Thus , a polymorphism or alteration in such linkage disequilibrium acts as a surrogate marker for a polymorphism or alteration as disclosed herein . Preferably, reference herein to a polymorphism or alteration in linkage disequilibrium with another means that r² > 0 . 8 , preferably r² > 0 . 9 , more preferably r² > 0 . 95 or even r² > 0 . 99 . In particularly preferred embodiments , an SNP is considered to be in LD with an SNP set forth in Table 1 if it exhibits r² = 1 . 0 and D' = 1 . 0 .

As used herein, LD is preferably determined in a Mexican, Latino American, or European population .

In one example , the SNPs rs 9379084 and rs41302867 form an LD block, such that rs 9379084 may in some cases be used, in accordance with any aspect of the present invention, as a proxy SNP for rs41302867 . In particular, rs 9379084 -rs 41302867 constitutes a GDM haplotype G-A . Thus , presence of the allele G at rs7903146 may be inferred from a determination that the subj ect has A at rs41302867 .

Genotyping Assays

Aspects of the invention relate to determining the presence of SNPs through obtaining a patient DNA sample and evaluating the patient sample for the presence of two or more SNPs . It should be appreciated that a patient DNA sample can be extracted, and a SNP can be detected in the sample, through any means known to one of ordinary skill in art. Some non-limiting examples of known techniques include detection via restriction fragment length polymorphism (RFLP) analysis, planar microarrays, bead arrays, sequencing, single strand conformation polymorphism analysis (SSCP) , chemical cleavage of mismatch (CCM) , and denaturing high performance liquid chromatography (DHPLC) .

In some embodiments, a SNP is detected through PCR amplification and sequencing of the DNA region comprising the SNP. In some embodiments SNPs are detected using microarrays. Microarrays for detection of genetic polymorphisms, changes or mutations (in general, genetic variations) such as a SNP in a DNA sequence, comprise a solid surface, typically glass, on which a high number of genetic sequences are deposited (the probes) , complementary to the genetic variations to be studied. Using standard robotic printers to apply probes to the array a high density of individual probe features can be obtained, for example probe densities of 600 features per cm² or more can be typically achieved. The positioning of probes on an array is precisely controlled by the printing device (robot, inkjet printer, photolithographic mask etc) and probes are aligned in a grid. The organisation of probes on the array facilitates the subsequent identification of specific probe-target interactions. Additionally it is common, but not necessary, to divide the array features into smaller sectors, also grid-shaped, that are subsequently referred to as sub-arrays . Sub-arrays typically comprise 32 individual probe features although lower (e.g. 16) or higher (e.g. 64 or more) features can comprise each subarray.

In some embodiments, detection of genetic variation such as the presence of a SNP involves hybridization to sequences which specifically recognize the normal and the risk allele in a fragment of DNA derived from a test sample. Typically, the fragment has been amplified, e.g. by using the polymerase chain reaction (PCR) , and labelled e.g. with a fluorescent molecule. A laser can be used to detect bound labelled fragments on the chip and thus an individual who is homozygous for the normal allele can be specifically distinguished from heterozygous individuals ( in the case of autosomal dominant conditions then these individuals are referred to as carriers ) or those who are homozygous for the ris k allele . In some embodiments , the amplification reaction and/or extension reaction is carried out on the microarray or bead itself .

In some embodiments , methods described herein may involve hybridization . For differential hybridization based methods there are a number of methods for analysing hybridization data for genotyping :

Increase in hybridization level : The hybridization levels of probes complementary to the normal and mutant alleles are compared .

Decrease in hybridization level : Differences in the sequence between a control sample and a test sample can be identified by a decrease in the hybridization level of the totally complementary oligonucleotides with a reference sequence . A loss approximating 100% is produced in mutant homozygous individuals while there is only an approximately 50% loss in heterozygotes . In Microarrays for examining all the bases of a sequence of "n" nucleotides ( "oligonucleotide" ) of length in both strands , a minimum of "2n" oligonucleotides that overlap with the previous oligonucleotide in all the sequence except in the nucleotide are necessary . Typically the size of the oligonucleotides is about 25 nucleotides . However it should be appreciated that the oligonucleotide can be any length that is appropriate as would be understood by one of ordinary s kill in the art . In particular, the use of a minor groove binding domain (MBD) permits shorter probe sequences while retaining high discrimination between the perfect match and the mismatch . The increased number of oligonucleotides used to reconstruct the sequence reduces errors derived from fluctuation of the hybridization level . However , the exact change in sequence cannot be identified with this method; in some embodiments this method is combined with sequencing to identify the mutation .

Where amplification or extension is carried out on the microarray or bead itself , three methods are presented by way of example : In the Minisequencing strategy, a mutation specific primer is fixed on the slide and after an extension reaction with fluorescent dideoxynucleotides , the image of the Microarray is captured with a scanner .

In the Primer extension strategy, two oligonucleotides are designed for detection of the wild type and mutant sequences respectively . The extension reaction is subsequently carried out with one fluorescently labelled nucleotide and the remaining nucleotides unlabelled . In either case the starting material can be either an RNA sample or a DNA product amplified by PCR .

In the Tag arrays strategy, an extension reaction is carried out in solution with specific primers , which carry a determined 5 ' sequence or "tag" . The use of Microarrays with oligonucleotides complementary to these sequences or "tags" allows the capture of the resultant products of the extension . Examples of this include the high density Microarray "Flex-flex" (Affymetrix ) .

For cost-effective genetic diagnosis , in some embodiments , the need for amplification and purification reactions presents disadvantages for the on-chip or on-bead extension/amplif ication methods compared to the differential hybridization based methods . However the techniques may still be used to detect and diagnose conditions according to the invention .

Typically, Microarray or bead analysis is carried out using differential hybridization techniques . However, differential hybridization does not produce as high specificity or sensitivity as methods associated with amplification on glass slides . For this reason the development of mathematical algorithms , which increase specificity and sensitivity of the hybridization methodology, are needed (Cutler DJ, Zwick ME , Carrasquillo MN, Yohn CT , Tobi KP, Kashuk C, Mathews DJ, Shah N, Eichler EE , Warrington JA, Chakravarti A . Genome Research; 11 : 1913-1925 ( 2001 ) . Methods of genotyping using microarrays and beads are known in the art .

The genotyping platform for use in the methods of the present invention may be based on the TaqMan® OpenArray® SNP Genotyping system available from Life Technologies . Further details of the TaqMan® genotyping system and OpenArray® format are available from the Life Technologies , Applied Biosystems , webpage , e . g . , the TaqMan® OpenArray® Genotyping Getting Started Guide , © 2010 Life Technologies Corporation .

Alternatively or additionally, the genotyping platform for use in the methods of the present invention may be based on the Dynamic Array I FCs Genotyping System from Fluidigm . Further details of the Dynamic Array I FCs Genotyping System are available from Fluidigm webpage .

Examples

Example 1 — Mexico GPM Algorithm

A ris k assessment model to identify Mexican women at high ris k of GDM was developed and validated, by using an algorithm that integrates genetic and clinical variables .

Example 1-1 - Study populations

Development Cohort : CME Cohort

Data from the "Cuido mi Embarazo" (CME " I Care for my Pregnancy" ) study was used, which utilized an oral glucose tolerance test (OGTT ) and supplied glucose screening through Medicion Integrada para la Deteccion Oportuna, (MIDO ) Embarazo®, a module of the Integrated Monitoring for Early Detection system .

The CME study was aimed to study early detection of GDM and to provide early care for GDM see Research Registry 7405 .

576 women were recruited from the CME study between 8 May 2019 and 18 May 2021 from six participating healthcare facilities in Mexico : three primary healthcare facilities in Hidalgo , two in Guanaj uato , and one in Mexico City . The CME cohort included pregnant women without T2D who were less than less than 28 gestational weeks and had performed a 2-hour 75 g OGTT between gestational weeks 24 and 28 . Those diagnosed with pre-gestational diabetes , had multiple pregnancies , or had a previous chronic disease that required their pregnancy to be monitored by secondary care were excluded . All participants received primary antenatal care ( standardized prenatal care was offered) and fasting plasma and capillary glucose measurements were performed at any time during the first 28 weeks of gestation .

Information on maternal age , ethnicity, gestational week at the time of the OGTT , body mass index ( BMI ) , family history of T2D, medical history of GDM, obstetric history and parity, gestational weight gain, associated comorbidities , and newborn birth weight was collected .

Baseline characteristics of this cohort are described in Table 1 below and in Martinez- Juarez et al [ 8 ] .

External validation cohort : Monica Pretelini Saenz" Maternal

Perinatal Hospital (HMPMPS) cohort

For external validation, a second cohort was used, the "Monica Pretelini Saenz" Maternal Perinatal Hospital ( HMPMPS ) cohort . HMPMPS included 231 women who were recruited by 'Monica Pretelini Saenz ' Maternal Perinatal Hospital , Toluca , State of Mexico . Participants were recruited between February 1 and September 30 , 2018 , and the eligibility criteria for the HMPMPS cohort were the same as that of the CME cohort . This study is registered in ClinicalTrials . gov ( ID : NCT01649167 ) .

Baseline characteristics of the HMPMPS are also described in Table 1 below .

Table 1 Baseline characteristics of CME and HMPMPS cohorts

Characteristics Cases Controls p-value

CME cohort

Number of 107 469 participants

Age at baseline 28.64 ± 6.56 26.06 ± 5.98 <0.001*

( years )

Pre-pregnancy 28.05 ± 4.76 25.52 ± 4.88 <0.001*

BMI (kg/m²)

Previous GDM 16 (14.95%) 2 (0.43%) <0.001^b

(yes)

Family history 73 (68.22%) 217 (46.27%) <0.001* of T2D (yes)

Previous 72 (67.29%) 266 (56.72%) 0.0580* pregnancies (yes)

HMPMPS cohort

Number of 32 199 participants

Age at baseline 29.13 ± 6.61 25.60 ± 6.72 0.0076*

( years )

Pre-pregnancy 30.67 ± 6.10 25.00 ± 4.54 <0.001*

BMI (kg/ m²)

Previous GDM 0 0 -

(yes)

Family history 25 (78.13%) 113 (66.83%) 0.0366* of T2D (yes)

Previous 27 (84.38%) 126 (63.32%) 0.0327* pregnancies (yes)

Data are presented as n (%) or mean ± standard deviation unless otherwise indicated.

*p-values obtained using Student's t-test. bp-values obtained using Fisher's exact test. cp-values obtained using the chi-square test. These two study cohorts were used to develop and validate an algorithm that could predict the risk of GDM in Mexican women during the early stages of pregnancy or before pregnancy . The cohorts were divided as follows for this purpose .

Table 2 - Cohorts used for algorithm development and validation

The study was approved by the Research Ethics Committee of the State of Hidalgo ( FSSAA2018076 ) and by the Research Ethics Committee of the State of Guanaj uato ( HGP/138 /2020 ) . All participating women provided written informed consent .

Example 1-2 - Diagnosis of gestational diabetes

GDM was diagnosed as per the International Association of Diabetes and Pregnancy Study Groups criteria [ 1 ] . A 2-hour OGTT with 75 g glucose was performed at 24-28 weeks of gestation . Plasma glucose levels were determined by the glucose oxidase method using fresh plasma samples . A diagnosis of GDM was confirmed if glucose levels were abnormal at one of the three time points ( fasting , or 1 hour or 2 hours post-glucose ingestion ) . The assessment of the GDM outcome was blinded to predictors . Similarly, investigators were blinded to GDM assessment during predictor assessment .

Example 1-3 - Clinical variables selection

Maternal age , pre-gestational BMI , family history of T2D and previous pregnancies were chosen as the clinical parameters for the model as these parameters have been described as strong risk factors and predictors in the development of GDM . [ 24 -27 ] Although other clinical variables such as glucose measurement have also been studied as predictors for the development of GDM [24] , variables were selected for which information could be easily obtained from the initial antenatal care questionnaire and that did not require further laboratory testing or specialized trained personnel .

Example 1-4 - Singl -nucleotide polymorphism selection and genotyping

One hundred and fourteen single-nucleotide polymorphisms (SNPs) were selected based on their predictive power as reported in previously published studies. [13,16,17,19,22,28-35]

Specifically, SNPs were prioritized according to the results of a large meta-analysis of GWAS, with the assumption that their effects can be extrapolated and generalized and that large sample sizes allow solid estimations of the true size effect. In addition, significant SNPs that were identified in smaller association studies were also included. The 114 selected SNPs are listed in the following Table 3.

Table 3 - List of 114 SNPs examined

Example 1-5 — SNP Genotyping

Genomic DNA was extracted from EDTA-stabilized blood samples taken during the OGTT using the Maxwell RSC instrument ( Promega,

Dubendorf , Switzerland) . Genotyping was performed by iPLEX MassARRAY PGR using the Agena platform (Agena Bioscience , San Diego , California, USA) . iPLEX MassARRAY PGR and extension primers were designed from sequences containing each target SNP and 150 upstream and downstream bases with AssayDesign Suite software (Agena Bioscience ) using the default settings . Single-base extension reactions were performed on the PCR reactions with the iPLEX Gold Kit (Agena Bioscience ) and 0 . 8 pL of the custom unique variant pool . PCR reactions were dispensed onto SpectroChipArrays with a Nano dispenser (Agena Bioscience ) . An Agena Bioscience Compact MassArray Spectrometer was used to perform matrix-assisted laser desorption/ionization-time of flight mass spectrometry according to the iPLEX Gold Application Guide . [ 35 ] The Typer 4 software package , V . 4 (Agena Bioscience ) was used to analyze the resulting spectra, and the composition of the target bases was determined from the mass of each extended oligo . Genotyping was performed using the Agena platform located at the Epigenetics and Genotyping Laboratory, Central Unit for Research in Medicine , Faculty of Medicine , University of Valencia, Valencia , Spain .

Example 1-6 — Data quality control and statistical analysis Quality control steps included the exclusion of variables with a high absence rate ( >30% ) to identify attributes and samples that did not provide sufficient information . The remaining missing data were estimated by the most common values of each attribute . The resulting database consisted of 576 samples and 139 attributes .

A correlation analysis was performed with the aim of reducing possible redundancies ; the similarity between the variables was analyzed by measuring Pearson' s correlation coefficient . The decision was made to consider one predictor for each pair/group of highly correlated variables ( >0 . 90 ) . Therefore , the attribute with the lowest ratio of missing values at the beginning of the study was analyzed . Comparisons between control and case samples were conducted using the X² and Fisher' s exact test for qualitative data and Student' s t- test for quantitative data (mean ± SD) . Sample sizes were not calculated or confirmed prior to modeling .

The CME cohort was randomly divided into a training dataset ( 70% of the cohort ) for algorithm development and a testing dataset ( 30% of the cohort ) for validation . The prediction model was trained for GDM using a 10-fold cross-validation logistic regression. In this analysis, the entire cohort was randomly divided into 10 subgroups;

9 of them were used to build the predictive model and 1 was used to validate it. The model was then further validated using the testing dataset (30% of the cohort) and the HMPMPS cohort. Of note, the HMPMPS cohort (validation cohort) differed from the CME cohort in that it was a single-center study versus a multicenter study.

All statistical and model calculations were performed in Python V.3.6, using the scikit-learn package. To validate the performance of the model, a k-fold cross-validation procedure was used to estimate the mean and SD of the values computed in the loop.

Example 1-7 — Statistical analysis and results

The data quality control process retrieved a total of 107 cases and 469 controls from the CME cohort. Baseline characteristics of study participants are shown in Table 1 above. Mean age and BMI were higher in cases than controls (age: 28.64 vs 26.06 years, p=0.0003; BMI: 28.05 vs 25.52 kg/m², p=0.00000451 ) .

114 SNPs that were previously associated with the risk of T2D, GDM, high BMI, and adverse pregnancy traits associated with GDM were examined (Table 3) . A correlation analysis was performed to identify SNPs providing similar information.

The SNPs rs560887 and rs563694; rsl7085593 and rs6235; rsl3266634, rsll558471, and rs3802177; rsl0814916, rs7041847, and rs7034200; rs4402960 and rs7651090; rs8050136 and rsl421085; and rsl801282 and rsl7036328 were shown to be highly correlated. Further information on the output of the correlation analysis is available in Zulueta et al. [54] which is incorporated by reference.

Of the 114 SNPs, 105 provided unique information and were used for further analysis. Statistical analysis showed that a total of 19 attributes (5 clinical variables and 14 SNPs) had a significant association (p<0.05) with the outcome (Table 5 below) . Table 5 — Student' s t-test analysis of significant attributes

Attribute t p -value

Age -3.6327 0.0004

Pregestational BMI -4.7508 4.51E-06

Previous -2.4261 0.0165 pregnancies rsl0830962 -2.6602 0.0087 rsl387153 -3.6097 0.0004 rs4607517 -2.8110 0.0056 rsl799884 -2.3641 0.0194 rsl801278 2.7709 0.0056 rsl0830963 -3.6601 0.0004 rsll715915 -3.0158 0.0029 rs340874 -2.5366 0.0122 rs6048205 -3.1931 0.0015 rs9379084 3.2286 0.0014 rsl6996148 -2.9484 0.0037 rs2943634 -2.3930 0.0177 rs6742799 -2.6378 0.0090 rs2745353 -2.5321 0.0123

Family history of T2D and personal history of GDM were selected using X² test analysis (p=0.0000656 and p=0.0000000000000714 , respectively) ; the other 17 variables were selected using Student's t-test analysis. Data from 70% of the pregnant women in the CME cohort, which included 75 cases and 328 controls with complete SNP genotype and clinical information available, were included in the development set.

Fifteen of the 19 attributes that were significantly associated (p<0.05) provided optimal logistic regression performance; of those, 11 were SNPs and 4 were clinical variables (Table 6) . Of the 11 SNPs selected by the analysis, rsl387153 in LOC100128354/MTNR1B, rs4607517 in GCK, rsl0830963 in MTNRIB, rsll715915 in AMT, rs340874 in PR0X1, rs6048205 in F0XA2, rsl6996148 in CILP2, rs2943634 in IRS1, rs6742799 in RBMS1, and rs2745353 in RSP03 correlated with a diagnosis of GDM and rs9379084 in RREB1 correlated with absence of GDM diagnosis (Table 6) . The four clinical attributes selected by the analysis were maternal age, pre- gestational BMI, family history of T2D, and previous pregnancies (Table 6 ) .

Table 6 — Attributes with optimal logistic regression performance using k-fold cross-validation

Logistic

Chromosome Effect regression

Closest gene (GRCh38.pl3) allele coefficient

(P)

Genetic attributes

(SNP)

LCC100128354/ rsl387153 chrll:92 940 662 T 0.4307

MTNRIB rs4607517 chr7:44 196 069 GCK A 0.5168 rsl0830963 chrll:92 975 544 MTNRIB G 0.5167 rsll715915 chr3:49 417 897 AMT C 0.7845 rs340874 chrl:213 985 913 PROXI C 0.3014 rs6048205 chr20:22 578 963 FOXA2 A 0.323 rs9379084 chr6:7 231 610 RREB1 A -0.5717 rsl6996148 chrl9:19 547 663 CILP2 T 0.2868 rs2943634 chr2:226 203 364 IRS1 C 0.4524 rs6742799 chr2:160 460 949 RBMS1 A 0.7422 rs2745353 chr6:127 131 790 RSPO3 T 0.254

Clinical attributes

Age - - - 0.0333

Pre-gestational BMI - - - 0.1039

Family history of T2D - - - 0.4811

Previous pregnancies - - - 0.0483 The 15 selected attributes were included in a GDM prediction regression model which was applied to the training dataset. The algorithm showed high predictive ability with an area under the receiver operating curve (AUG) of 0.7507, sensitivity of 79%, and specificity of 71%. The analysis of predictive values at different thresholds is shown in Table 7 below.

Table 7 — Analysis of predictive vales at different thresholds

Threshold Sensitivity Specificity PPV NPV

0.15 0.8598 0.6449 0.7077 0.8214

0.17 0.8131 0.6822 0.7191 0.7849

0.18 0.7944 0.7103 0.7328 0.7755

0.2 0.7477 0.7477 0.7477 0.7477

0.25 0.6542 0.7944 0.7609 0.6967

NPV, negative predictive value;

PPV, positive predictive value.

Figure 1 shows violin plots where the number of samples in each risk percentage is represented in terms of density. The algorithm showed adequate power to discriminate between controls and cases, as the area with major density in controls (median, 12.35%) was smaller than that of the cases (median, 31.20%) . The prediction model was then internally verified using the validation dataset of the CME cohort and externally validated using the HMPMPS cohort (Table 8 below) . The prediction algorithm showed an AUG of 0.8256 in the training dataset and of 0.8001 in the HMPMPS cohort.

Table 8 — Performance of GDM prediction algorithm in development and validation cohorts

Development Validation Validation

Cohort CME 70% CME 30% HMPMPS

No of cases 75 32 32

No of controls 328 141 199

Location Mexico Mexico Mexico

Diagnostic

IADPSG IADPSG IADPSG criteria

AUC 0.7507 0.8256 0.8001

Finally, the performance of the risk model was explored including the 11 genetic variables alone, the 4 clinical variables alone, and all 15 variables together. The risk algorithm with only SNPs performed better than the risk algorithm with only clinical factors (Table 9) , and the robustness of the model increased when all 15 variables were included.

Table 9 — Performance of GDM risk algorithm in development and validation sets

AUC

Variables Development Validation

SNPs only 0.7136 0.7694

Clinical variables only 0.6526 0.6824

SNPs+clinical variables 0.7507 0.8256

To use this model, clinicians can collect data regarding clinical variables from patient medical histories at the first prenatal examination, and SNP data either through the collection of an epithelial buccal swab sample or peripheral blood followed by DNA genotyping. Once the data are entered, the model can be used to determine an individual's risk of developing GDM. AUCs obtained during development were similar to those obtained after development (0.7507 and 0.8256, respectively) , supporting the validation of the model. The development of this model is important because early detection of women at high risk of GDM could catalyze timely intervention with the implementation of lifestyle changes prior to week 20 of pregnancy, or preferably before week 16, when interventions have been shown to be effective. [7, 36] The algorithm used in the model includes 11 SNPs and 4 clinical features.

It has been shown that the presence of the G allele at rsl0830963 in MTNRIB, and the T allele at rsl387153 in LOC100128354/MTNR1B are associated with an increased risk of GDM. The association of SNPs in MTNRIB with fasting glucose and insulin secretion is well established. [37] Melatonin is the primary hormone secreted by the pineal gland; it regulates sleep, circadian rhythm, and glucose metabolism. MTNRIB is highly expressed in both the placenta and pancreatic islets. Lyssenko et al have shown that genetic variants in this melatonin receptor correlate with impaired glucose- stimulated insulin secretion. [38] Furthermore, interactions between variants in MTNRIB, GDM risk, and physical activity and healthy eating interventions in pregnant women have been proposed. [22] MTNRIB regulates circadian rhythmicity and influences energy metabolism. [37] Furthermore, associations have been found between relative macronutrient intake, higher fasting plasma glucose, short sleep duration (<7 hours) , and MTNRIB genetic variants. [39] It has been proposed that lower carbohydrate intake and normal sleep duration may ameliorate cardiometabolic abnormalities conferred by common circadian rhythm-related genetic variants. [39] In addition, carriers of the CC genotype tend to respond more favorably to a hypocaloric diet enriched with monounsaturated fats. [40] Thus, recommendations regarding diet, particularly for carbohydrate and fat consumption, and sleep duration should be emphasized to women who are carriers of MTNRIB gene variants and at high risk of GDM.

Another variant included in the model was rsll715915 in AMT, a gene that encodes aminomethyltransferase, which is a critical component of the glycine cleavage system in mitochondria, where energy production occurs. [41] The breakdown of glycine produces a methyl group , which is added to and used by folate . Rsl l715915 is located either in the 3 ' untranslated region or within coding regions of AMT, depending on the transcript , and upstream of RHOA ( ras homolog family member A) . [ 41 ] RHOA is a signaling molecule that activates Rho kinase , a regulator of insulin transcription that is differentially regulated in T2D and thought to play a role in glucose homeostasis . [ 42 ]

This study also identified variants in genes encoding transcription factors ( F0XA2, PR0X1 , RBMS1 , and RREB1 ) that regulate basic processes in the embryonic development of pancreatic beta cells , cell cycle progression in the pancreas , and insulin response in peripheral tissues . F0XA2 encodes the forkhead box protein A2 , a member of the forkhead class of DNA- binding proteins . F0XA2 has been previously identified as a master regulator in pancreatic development and is involved in regulating both the glucose-sensing apparatus and insulin release . [ 43 ] In a study by Yu and Zhong , it was shown that the microRNA miR-141 , a post-transcriptional regulator in the pathophysiology of T2D, may lead to impaired glucose-stimulated insulin secretion and beta cell proliferation by targeting F0XA2 at the 3 ' untranslated region; a potential role for the antidiabetic drug pioglitazone in regulating the miR-141/F0XA2 axis was also identified . [ 44 ]

Another variant of interest identified in this study is the C allele at rs340874 in PR0X1 ( Prospero homeobox 1 ) , a transcription factor involved in the embryonic development of the pancreas , liver, and nervous system. Carriers of the CC genotype have been previously shown to have higher non-esterif ied fatty acid levels after a high- fat meal and lower glucose oxidation after a high-carbohydrate meal in comparison with subj ects who have other PR0X1 genotypes . [ 45 ] Subj ects with the CC variant also had higher accumulation of visceral fat and, surprisingly, lower daily food consumption .

Additionally, rs 6742799 , mapping to RBMS1 ( RNA binding motif , single-stranded interacting protein 1 ) was found to have a significant association with GDM . RBMS1 is expressed in the placenta and has a possible anti-inflammatory role . Al vine et al proposed that increased expression of placental RBMS1 in obese women may serve as an adaptive response to reduce oxidative stress in a maternal obesogenic environment . [ 46 ] Oxidative stress is now recognized as playing an essential role in certain pregnancy-related disorders such as GDM, pre-eclampsia , and intrauterine growth retardation . [ 47 ] The maternal obesity associated with metabolic alterations seems to lead to the appearance of an elevated placental oxidative stress , compromising both placental metabolism and antioxidant status . [ 48 ]

The A allele at rs 9379084 in RREB1 ( Ras-responsive element binding protein 1 ) was found to have a protective effect in this study . RREB1 is a member of zinc finger transcription factors and functions both as a transcriptional activator and repressor, and its role in target gene regulation may depend on its binding partner and the status of epigenetic modifications . [ 49 ] The cell cycle regulator CDKN2A increases susceptibility to T2D and is regulated by RREB1 . Furthermore , RREB1 also directly promotes the expression of insulin genes . [ 49 ]

The GDM risk algorithm also included genetic variants in genes with a signaling function and association with insulin resistance ( IRS1 , RSPO3 , CILP2) . IRS1 is a signaling intermediate downstream of activated cell-surface insulin receptors . [ 48 ] RSPO3 encodes R- Spondin-3 , which regulates Wnt and beta-catenin signaling pathways ; RSPO3 gene knockdown results in abnormal adipogenesis , lipid metabolism, and insulin signaling . [ 49 ] In addition, CILP2 encodes cartilage intermediate layer protein 2 , a glycoprotein initially identified in collagen . CILP2 is located in the NCAN-CILP2-PBX4 region, an intergenic region spanning 300 kb associated with serum cholesterol , low-density lipoprotein and triglyceride concentrations , cardiovascular disease , and non-alcoholic fatty liver disease . [ 50 ]

The 11 SNPs identified in this analysis are located in genetic loci that have been reported to participate in molecular processes related to fasting glucose (MTNRIB , GCK, AMT, PROXI , and FOXA2) , insulin resistance ( CILP2, IRS1 , and RBMS1 ) , insulin secretion (MTNRIB) , and fasting insulin ( IRS1 ) . Four of these SNPs have previously been associated with T2D ( LOC100128354/MTNR1B , PR0X1 , CILP2, and RBMS1 ) , while two other SNPs have previously been reported in GDM ( LOC100128354/MTNR1B and RREB1 ) . Overall , this initial annotation of potential genetic loci characteristics , as reported in the literature , is j ust an initial investigation into how genetic variants may contribute to GDM susceptibility .

The GDM ris k algorithm described in this study also included four phenotypic variables : maternal age , pre-gestational BMI , family history of T2D, and previous pregnancies .

The four phenotypic variables alone yielded an AUG of 0 . 65 and 0 . 68 in the development and validation sets , respectively . The 11 SNPs alone yielded respective AUCs of 0 . 71 and 0 . 77 . The additive contributions of phenotype and genotype increased the overall AUCs to a respective 0 . 75 and 0 . 83 . This is the highest performance for a genotype-informed GDM prediction algorithm reported in the literature to date . Although the current rise in GDM prevalence is driven mainly by changes in lifestyle , complex genetic determinants contribute to the inherent susceptibility of this disease . Inclusion of genotype-based susceptibility information will support the use of precision medicine , the identification of women at high ris k of GDM during the early stages of pregnancy, and the application of personalized preventive interventions .

Translation of new findings from genetic studies to the clinic is the most attractive aspect of genome research . One potential clinical application is the development of genetically informed personalized susceptibility profiles and lifestyle recommendations .

The strengths of this study include a robust modelling strategy for significant attributes , as well as the analysis of a carefully selected list of 114 SNPs according to their reported predictive value . Focus was not simply present on the correlation of each SNP with GDM, but rather on the combined effect of the significant SNPs . The analysis yielded both a combination and predictive weight of variables that were predictive of the population studied and therefore is expected to be predictive of the broader population . Example 2 — Europe GPM Algorithm

Example 2-1 — Study populations Three cohorts were chosen for analysis .

A retrospective cohort of 711 women from Hospital Clinico San Carlos ( HCSC , Madrid, Spain) , with 425 control pregnancies and 286 GDM cases diagnosed per The International Association of Diabetes and Pregnancy Study Groups ( IADPSG ) criteria .

A cohort of 157 women ( 89 controls , 68 cases diagnosed per the criteria of The National Diabetes Data Group (NDDG ) from Hospital Cruces ( Bilbao , Spain ) .

A cohort of 416 women ( 346 controls , 70 cases per IADPSG criteria from the "Monica Pretelini Saenz" Maternal Perinatal Hospital , Toluca, State of Mexico , Mexico ) . All studies were approved by the corresponding institutional ethics committees and in compliance with the Declaration of Helsinki ) . All women signed written informed consent .

Information was collected on maternal age , ethnicity, gestational week at the time of the OGTT ( Oral Glucose Tolerance Test ) , body mass index, family history of T2D, past medical history of GDM, past obstetric history and parity, gestational weight gain, associated comorbidities , and the newborn' s birth weight . Baseline characteristics of the cohorts are described in Table 10 and de la Torre et al . [ 28 ] .

Table 10 Baseline characteristics of CME and HMPMPS cohorts

Characteristics Cases Controls

HCSC cohort

Number of participants 286 425

Age at baseline (years) 33.58 ± 5.13 31.43 ± 5.50

Pre-pregnancy BMI (kg/m²) 24.04 ± 4.20 22.88 ± 3.67

Previous GDM (yes) 63 (22.03%) 60 (14.12%)

Family history of T2D (yes) 22 (7.69%) 44 (10.35%)

Previous pregnancies (yes) 163 (59.99%) 258 (60.71%)

CRUCES cohort

Number of participants 68 89

Age at baseline (years) 36.20 ± 4.96 33.83 ± 5.00

Pre-pregnancy BMI (kg/m²) 25.83 ± 5.27 23.58 ± 4.32

Previous GDM (yes) 10 (14.71%) 1 (1.12%)

Family history of T2D (yes) 28 (41.18%) 8 (8.99%)

Previous pregnancies (yes) Not available Not available

HMPMPS cohort

Number of participants 32 199

Age at baseline (years) 29.13 ± 6.61 25.60 ± 6.72

Pre-pregnancy BMI (kg/ m²) 30.67 ± 6.10 25.00 ± 4.54

Previous GDM (yes) 0 0

Family history of T2D (yes) 25 (78.13%) 113 (66.83%)

Previous pregnancies (yes) 27 (84.38%) 126 (63.32%)

The data quality control process retrieved a total of 286 cases and 425 controls out of the HCSC cohort. Mean age and BMI were higher in cases than in controls (age 33.5 years vs 31.4 years, p=0.00004; BMI 24.04 kg/m² vs 22.88 kg/m², p=0.058) .

These three study cohorts were used to develop and validate an algorithm that could predict the risk of GDM in European women during the early stages of pregnancy or before pregnancy. The cohorts were divided as follows for this purpose. Table 2 - Cohorts used for algorithm development and validation

Example 2-2 — SNP Genotyping

A total of 112 SNPs were selected for this analysis after exhaustive exploration of the databases published to date of SNPs associated with GDM [ 13 , 16 , 17 , 19 , 22 ] . Genotyping was performed using iPlex Gold-MassARRAY from Agena Bioscience as detailed in Example 1-5 .

Discrimination and calibration of risk scores were assessed using the receiver operating characteristic ( ROC ) curve in the internal and the external validation groups .

Example 2-3 — Correlation analysis

We examined 112 SNPs previously associated with the risk of T2D, GDM, high BMI and adverse pregnancy traits associated with GDM . A correlation analysis was performed to identify SNPs providing similar information . This is as described in Example 1- 6 . Of the 112 SNPs , 105 provided unique information and were used for further analysis .

Example 2-4 — Statistical analysis and results

The 105 SNPs and the clinical variables were analysed by sequence feature selection ( SFS ) and linear regression techniques as described in Examples 1- 6 and 1-7 . This analysis identified 10 SNPs and 2 clinical variables as having optimal logistic regression performance . These SNPs are shown in Table 11 below . Table 11 — Attributes selected by SFS

The 10 SNPs identified by SFS and logistic regression analysis reside in genetic loci which have been associated to molecular processes related to fasting glucose (LOC100128354/MTNR1B, CRY2, IGF2BP2) , insulin resistance (CCND2, GPSM1, IRS1) , insulin secretion (LEP) , fasting insulin (IRS1) , and folate and vitamin B12 metabolism (MTHFR, MTR, CUBN) .

The model showed satisfactory predictive ability with a ROC-AUC of 0.74, sensitivity of 70% and specificity of 69%. The analysis of sensitivity and specificity is shown in Table 12. Table 12 — Analysis of predictive values at different thresholds

Threshold Sensitivity Specificity PPV NPV

0.30 0.50 0.84 0.52 0.83

0.35 0.61 0.79 0.57 0.82

0.37 0.64 0.76 0.58 0.81

0.40 0.69 0.70 0.59 0.78

0.45 0.79 0.62 0.65 0.76

NPV, negative predictive value;

PPV, positive predictive value.

Figure 2 shows a violin plot where the number of samples in each risk percentage is represented in terms of density. Control and cases were discriminated, as the area with major density in controls (median: 30.02%) is smaller than the one of the cases (median: 49.62%) .

In the training dataset the AUC was 0.74, sensitivity of 77% and specificity of 64%. AUCs in the HCSC, UAEM and Cruces validation sets were 0.71, 0.70 and 0.62 respectively.

The performance of the GDM prediction algorithm in development and validation cohorts is shown in Table 13.

Table 13 — Performance of GDM prediction algorithm in development and validation cohorts

Development Validation

Cohort HCSC 70% HCSC 30% HMPMPS Cruces

Cases 194 92 32 68

Controls 303 122 199 89

Location Madrid, ES Madrid, ES Mexico Bilbao, ES

Diagnostic

IADPSG IADPSG IADPSG IADPSG criteria

AUC 0.7423 0.7000 0.7220 0.6224 References

A number of publications are cited above in order to more fully describe and disclose the invention and the state of the art to which the invention pertains . Full citations for these references are provided below. The entirety of each of these references is incorporated herein.

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Claims

Claims

1. A method of assessing Gestational Diabetes Mellitus (GDM) susceptibility in a female human subject, the method comprising determining the identity of at least one allele at each of at least two positions of single nucleotide polymorphism (SNP) selected from List 1 :

List 1

PROXI - RS340874;

LOC10012835 - RS1387153;

RSPO3 - RS2745353;

IRS1 - RS2943634;

IGF2BP2 - RS4402960

F0XA2 - RS6048205;

RBMS1 - RS6742799;

RREB1 - RS9379084;

MTNRIB - RS10830963;

AMT - RS11715915; and

CILP2 - RS16996148.

2. The method according to claim 1, wherein the method comprises determining the identity of at least one allele at all of the positions of single nucleotide polymorphism (SNP) of List 1.

3. The method according to claim 1 or 2 , wherein presence of one or more of the following risk alleles indicates that the subject has greater susceptibility to GDM:

C at RS340874;

T at RS1387153;

T at RS2745353;

C at RS2943634;

A at RS4607517;

A at RS6048205;

A at RS6742799;

G at RS9379084;

G at RS10830963;

C at RS11715915; and T at RS16996148.

4. The method according to any one of claims 1 to 3, wherein the method further comprises determining the identity of at least one allele at each of at least two positions of single nucleotide polymorphism (SNP) selected from List 2

List 2

CCND2 - RS1106306;

LOC100128354/MTNR1B - RS1387153

MTHFR - RS1801133;

MTR - RS1805087;

LEP - RS2167270;

IRS1 - RS2943634;

CUBN - RS11254363;

ARAP1 - RS11605924; and

GPSM1 - RS11787792;

5. The method according to claim 4, wherein the method comprises determining the identity of at least one allele at all of the positions of single nucleotide polymorphism (SNP) of List 2.

6. The method according to claim 4 or 5 , wherein presence of one or more of the following risk alleles indicates that the subject has greater susceptibility to GDM:

C at RS1106306;

T at RS1387153

A at RS1801133;

G at RS1805087;

G at RS2167270;

C at RS2943634;

G at RS11254363;

A at RS11605924; and

A at RS11787792;

7. A method of assessing Gestational Diabetes Mellitus (GDM) susceptibility in a female human subject, the method comprising determining the identity of at least one allele at each of at least two positions of single nucleotide polymorphism (SNP) selected from

List 2 :

List 2

CCND2 - RS1106306;

LOC100128354/MTNR1B - RS1387153

MTHFR - RS1801133;

MTR - RS1805087;

LEP - RS2167270;

IRS1 - RS2943634;

CUBN - RS11254363;

ARAP1 - RS11605924; and

GPSM1 - RS11787792;

8. The method according to claim 7, wherein the method comprises determining the identity of at least one allele at all of the positions of single nucleotide polymorphism (SNP) of List 2.

9. The method according to claim 7 or 8 , wherein presence of one or more of the following risk alleles indicates that the subject has greater susceptibility to GDM:

C at RS1106306;

T at RS1387153

A at RS1801133;

G at RS1805087;

G at RS2167270;

C at RS2943634;

G at RS11254363;

A at RS11605924; and

A at RS11787792;

10. The method according to any one of claim 7 to 9, the method further comprises determining the identity of at least one allele at each of at least two positions of single nucleotide polymorphism (SNP) selected from List 1:

List 1

PROXI RS340874;

LOC10012835 RS1387153; RSPO3 RS2745353;

IRS1 RS2943634;

IGF2BP2 RS4402960

FOXA2 RS6048205;

RBMS1 RS6742799;

RREB1 RS9379084;

MTNRIB RS10830963;

AMT RS11715915; and

CILP2 RS16996148.

11. The method according to claim 10, wherein the method comprises determining the identity of at least one allele at all of the positions of single nucleotide polymorphism (SNP) of List 1.

12. The method according to claim 10 or 11, wherein presence of one or more of the following risk alleles indicates that the subject has greater susceptibility to GDM:

C at RS340874;

T at RS1387153;

T at RS2745353;

C at RS2943634;

A at RS4607517;

A at RS6048205;

A at RS6742799;

G at RS9379084;

G at RS10830963;

C at RS11715915; and

T at RS16996148.

13. The method according to any one of the preceding claims, wherein the method further comprises determining the identity of at least one allele of at least one position of SNP selected from:

SLC19A11 - RS1051266;

MTHFR - RS1801131; and

RREB1 - RS41302867.

14. The method according to any one of the preceding claims, further comprising assessing one or more of the following clinical variables associated with the subject: a) the age of the subject, b) the pregestational BMI of the subject, c) whether the subject has any family history of type 2 diabetes (T2D) , or d) whether the patient has previously been pregnant.

15. The method according to any one of the preceding claims, wherein allele determination is carried out at not more than 25 SNP positions .

16. The method according to any one of the preceding claims, wherein the method comprises determining the identity of both alleles at each SNP thereby obtaining the genotype of the subject at each SNP.

17. The method according to claim 6, wherein the subject is determined to be heterozygous or to be homozygous for said risk allele at at least one of said SNPs .

18. The method according to any one of the preceding claims, wherein the method comprises determining the identity of at least one allele at all of the listed positions of single nucleotide polymorphism (SNP) .

19. The method according to any one of the preceding claims, wherein the method comprises assaying a DNA-containing sample that has previously been obtained from said subject.

20. The method according to claim 19, wherein said sample is selected from the group consisting of: blood, hair, skin, amniotic fluid, buccal swab, saliva, and faeces.

21. The method according to claim 19 or claim 20, wherein the method comprises isolating and/or amplifying genomic DNA from said subject .

22. The method according to any one of the preceding claims, wherein determining the identity of said at least one allele at each SNP comprises: probe hybridization, real time PCR, array analysis, bead analysis, primer extension, restriction analysis and/or DNA sequencing .

23. The method according to any one of the preceding claims, wherein the method comprises determining the number of and identity of SNP risk alleles, and wherein the method further comprises computing a GDM risk score for said subject based on the number and identity of said SNP risk alleles.

24. The method according to claim 23, wherein the method comprises inputting the SNP risk allele determinations into a probability function to compute said risk score.

25. The method according to any one of the preceding claims, wherein the subject is pregnant.

26. The method according to any one of the preceding claims, wherein the subject is of Mexican, Latino American, or European origin or ancestry.

27. The method according to any one of the preceding claims, wherein the subject has at least one first degree relative who has, or has previously been diagnosed with, GDM and/or type 2 diabetes (T2D) .

28. The method according to any one of the preceding claims, wherein the subject has one or more clinical risk factors for GDM selected from: pregestational body mass index (BMI) > 30; waist circumference > 80 cm; age > 35; diagnosis of polycystic ovary syndrome; a diagnosis of GDM during a previous pregnancy; is a smoker; a previous pregnancy that resulted in a child with birth weight > 90^th centile; and a previous diagnosis of prediabetes, impaired glucose tolerance or impaired fasting glycaemia.

29. The method according to any one of the preceding claims, wherein the subject is determined to carry one or more of said risk alleles at one or more of said SNPs and therefore to be at greater risk of GDM, the method further comprising administering to the subject a test selected from the group consisting of: an oral glucose tolerance test (OGTT) ; a non-challenge blood glucose test; a screening glucose challenge test; and a urinary glucose test.

30. The method according to any one of the preceding claims, wherein the subject is determined to carry one or more of said risk alleles at one or more of said SNPs and therefore to be at greater risk of GDM, the method further comprising an intervention selected from the group consisting of: a low glycaemic index (GI) diet, increased exercise, insulin therapy, and anti-diabetic medication.

31. A method of treating or preventing Gestational Diabetes Mellitus (GDM) in a female human subject, the method comprising determining the identity of at least one allele at each of at least two positions of single nucleotide polymorphism (SNP) selected from List 1 :

List 1

PROXI - RS340874;

LOC10012835 - RS1387153;

RSPO3 - RS2745353;

IRS1 - RS2943634;

GCK - RS4607517;

FOXA2 - RS6048205;

RBMS1 - RS6742799;

RREB1 - RS9379084;

MTNRIB - RS10830963;

AMT - RS11715915; and

CILP2 - RS16996148; determining the subject to be at risk of GDM, and administering a medicament for treating or preventing GDM.

32. A method of treating or preventing Gestational Diabetes Mellitus (GDM) in a female human subject, the method comprising determining the identity of at least one allele at each of at least two positions of single nucleotide polymorphism (SNP) selected from List 2 :

List 2

CCND2 RS1106306; LOC100128354/MTNR1B - RS1387153

MTHFR - RS1801133;

MTR - RS1805087;

LEP - RS2167270;

IRS1 - RS2943634;

CUBN - RS11254363;

ARAP1 - RS11605924; and

GPSM1 - RS11787792.

33. The method according to claim 31 or 32, wherein the method further comprises determining the identity of at least one allele of at least one position of SNP selected from:

SLC19A11 - RS1051266;

MTHFR - RS1801131; and

RREB1 - RS41302867.

34. The method of any one of claims 31 to 33, wherein the medicament for treating or preventing GDM is selected from any one of the group consisting of : a) insulin, b) metformin, c) a sulphonylurea medicament, d) a meglitinide, e) an alpha-glucosidase inhibitor, f) a thiazolidinedione , g) a DPP-4 inhibitor, h) an incretin mimetic, and i) an amylin analogue.

35. The method of any one of claims 31 to 34, wherein the subject is determined to carry one or more of said risk alleles at one or more of said SNPs and therefore to be at greater risk of GDM, the method further comprising administering to the subject a test selected from the group consisting of: an oral glucose tolerance test (OGTT) ; a non-challenge blood glucose test; a screening glucose challenge test; and a urinary glucose test.

36. The method according to any one of the preceding claims, wherein the subject is determined to carry one or more of said risk alleles at one or more of said SNPs and therefore to be at greater risk of GDM, the method further comprising an intervention selected from the group consisting of: a low glycaemic index (GT) diet, increased exercise, insulin therapy, and anti-diabetic medication.

37. A genotyping tool for use in a method of any one of the preceding claims, said tool comprising an array having a plurality of oligonucleotide probe pairs, each of said probe pairs comprising a first probe specific for a first allele of a single nucleotide polymorphism (SNP) and a second probe specific for a second allele of the SNP, wherein said plurality of oligonucleotide probe pairs comprises probe pairs that interrogate at least two SNPs selected from List 1:

List 1

PROXI - RS340874;

LOC10012835 - RS1387153;

RSPO3 - RS2745353;

IRS1 - RS2943634;

GCK - RS4607517;

F0XA2 - RS6048205;

RBMS1 - RS6742799;

RREB1 - RS9379084;

MTNRIB - RS10830963;

AMT - RS11715915; and

CILP2 - RS16996148;

38. The genotyping tool according to claim 37, wherein the tool comprises probe pairs that interrogate all of the SNPs of List 1.

39. The genotyping tool according to claim 37 or 38, wherein the tool comprises further probe pairs that interrogate at least two SNPs selected from List 2 :

List 2 CCND2 RS1106306;

LOC100128354/MTNR1B RS1387153

MTHFR RS1801133;

MTR RS1805087;

LEP RS2167270;

IRS1 RS2943634;

CUBN RS11254363;

ARAP1 RS 11605924; and

GPSM1 RS11787792;

40. The genotyping tool according to claim 39, wherein the tool comprises probe pairs that interrogate all of the SNPs of List 2.

41. A genotyping tool for use in a method of any one of the preceding claims, said tool comprising an array having a plurality of oligonucleotide probe pairs, each of said probe pairs comprising a first probe specific for a first allele of a single nucleotide polymorphism (SNP) and a second probe specific for a second allele of the SNP, wherein said plurality of oligonucleotide probe pairs comprises probe pairs that interrogate at least two SNPs selected from List 2 :

List 2

CCND2 RS1106306;

LOC100128354/MTNR1B RS1387153

MTHFR RS1801133;

MTR RS1805087;

LEP RS2167270;

IRS1 RS2943634;

CUBN RS11254363;

ARAP1 RS 11605924; and

GPSM1 RS11787792;

42. The genotyping tool according to claim 41, wherein the tool comprises probe pairs that interrogate all of the SNPs of List 2.

43. The genotyping tool according to claim 41 or 42, wherein the tool comprises further probe pairs that interrogate at least two SNPs selected from List 1 : List 1

PROXI - RS340874;

LOC10012835 - RS1387153;

RSPO3 - RS2745353;

IRS1 - RS2943634;

GCK - RS4607517;

FOXA2 - RS6048205;

RBMS1 - RS6742799;

RREB1 - RS9379084;

MTNRIB - RS10830963;

AMT - RS11715915; and

CILP2 - RS16996148;

44. The genotyping tool according to claim 43, wherein the tool comprises probe pairs that interrogate all of the SNPs of List 1.

45. The genotyping tool according to any one of claims 37 to 44, wherein the probe pairs that interrogate the SNPs selected from List 1 and/or List 2, make up at least 50% of the total number of nucleic acid probes in the array.

46. The genotyping tool according to any one of claims 37 to 45, wherein the tool further comprises probe pairs that interrogate at least one of the SNPs selected from:

SLC19A11 - RS1051266;

MTHFR - RS1801131; and

RREB1 - RS41302867.

47. The genotyping tool according to any one of claims 37 to 46, wherein the total number of different SNPs for which allele-specific probes are provided does not exceed 25.

48. The genotyping tool according to any one of claims 37 to 47, wherein the tools is in the form of a TaqMan® OpenArray® SNP genotyping platform, a Dynamic Array integrated fluidic circuits (IFC) genotyping platform, a next-generation sequencing system, or a Mass ARRAY® system.

49 . The genotyping tool according to any one of claims 37 to 48 , wherein the allele-specific oligonucleotide probes are each covalently attached to a fluorophore .

50 . The genotyping tool according to any one of claims 37 to 49 , wherein the tool further comprises a primer pair for each of said SNPs , said primer pair for each SNP comprising an oligonucleotide primer that hybridizes to a target sequence upstream of the SNP and an oligonucleotide primer that hybridizes to a target sequence downstream of the SNP .

51 . The genotyping tool according to any one of claims 37 to 50 , wherein the tool further comprises one or more reagents for amplification of DNA comprising said SNPs and/or for detection of said allele-specific probes .

52 . A Gestational Diabetes Mellitus ( GDM) ris k assessment system for use in a method according to any one of claims 1 to 36 , the system comprising a genotyping tool as defined in any one of claims 37 to 51 and a computer programmed to compute a GDM ris k score from the genotype data of the subj ect at each of at least two SNPs selected from the SNPs set forth in List 1 or List 2 .

53 . A method according to any one of claims 1 to 36 , wherein the method employs a genotyping tool as defined in any one of claims 37 to 51 and/or employs a GDM risk assessment system as defined in claim 52 .