US20180094318A1 - Methods, tools and systems for the assessment, prevention, management and treatment selection for type 2 diabetes - Google Patents

Methods, tools and systems for the assessment, prevention, management and treatment selection for type 2 diabetes Download PDF

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US20180094318A1
US20180094318A1 US15/559,236 US201615559236A US2018094318A1 US 20180094318 A1 US20180094318 A1 US 20180094318A1 US 201615559236 A US201615559236 A US 201615559236A US 2018094318 A1 US2018094318 A1 US 2018094318A1
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Laureano Simón Buela
Mirella G. Zulueta
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Patia Biopharmia SA de CV
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/172—Haplotypes

Definitions

  • the present invention relates to methods and products, in particular arrays and related systems, for in vitro genotyping of type 2 diabetes (T2D) associated genetic variations and to methods for assessment of T2D risk, prevention, management, and treatment selection, including one or more of: assessment of genetic risk for T2D, assessment of sensitivity to diabetes medications, and interventions for supporting diabetes prevention, delay and management, such as mobile individual casual games for reinforcing effective eating habits, virtual longitudinal interactive games, and casual games with real-life behavior assessment, social media networks, and education systems.
  • T2D type 2 diabetes
  • T2D Type 2 diabetes
  • T2D is a non-insulin-dependent diabetes that is characterized by hyperglycemia due to impaired insulin secretion and insulin resistance in target tissues. T2D is typically diagnosed after age 40 years and is caused by the combined action of genetic susceptibility and environmental factors. T2D is associated with obesity, and it is also a polygenic disease.
  • GWAS Genome-wide association studies
  • Diabetes Self-Management Education is a technique that involves the diabetic learning the skills needed to manage his/her diabetes and control his/her blood sugar level daily.
  • DSME is a preventive care solution that can help manage diabetes-related complications and reduce overall health costs.
  • Existing diabetes management strategies recognize the need for regular contact, community support, encouragement, and regular monitoring. Most efforts support preventive care with weekly, monthly, or even less frequent contact because frequent contact requires the time of expensive medical professionals.
  • Other methods of controlling diabetes include medication, community health programs, and Internet-based programs to help people manage diabetes.
  • SNPs single nucleotide polymorphisms
  • T2D type 2 diabetes
  • SNPs single nucleotide polymorphisms
  • combinations of SNPs selected for particular suitability to Mexican and Latin American populations, among others, have been identified herein.
  • the SNPs selected for analysis include variations associated to a diversity of ancestral lineage, such diversity being prevalent in the Mexican population.
  • the Mexican population is ethnically diverse, comprising individuals of Mestizo, European descent, Asian Mexican, Afro-Mexican and Native peoples of Mexico.
  • Tools and associated systems have been developed for use in methods of the invention, including for the prediction of T2D susceptibility, treatment selection, management and in some cases prevention of T2D.
  • the present invention provides a method of assessing type 2 diabetes susceptibility and/or predicting treatment responsiveness in a human subject, the method comprising determining the identity of at least one allele at each of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16 positions of single nucleotide polymorphism (SNP) selected from:
  • the SNPs may be as disclosed in the NCBI dbSNP, Homo sapiens genome build 37.
  • the method comprises determining the identity of at least one allele at each of the following SNPs:
  • SLC16A11 rs75493593
  • HNF1A rs483353044
  • KCNQ1 rs2237897
  • TCF7L2 rs7903146
  • the four SNPs rs75493593, rs483353044, rs2237897, and rs7903146 represent a compact and efficient SNP set for prediction of T2D susceptibility and sulfonyl therapy response well-adapted to use in an ethnically diverse population, such as a Mexican population.
  • SLC16A11—rs75493593 SLC16A11 has been found to be associated with risk of T2D is Mexican and Latin American individuals.
  • the risk haplotype is present at ⁇ 50% frequency in Native American samples and ⁇ 10% in east Asian, but is rare in European and African samples.
  • KCNQ1 (including rs2237897) has been implicated as a T2D susceptibility gene in populations of Korean, Chinese and European ancestry as well as in two independent Japanese populations.
  • HNF1A—rs483353044 (E508K) is implicated not only as a T2D susceptibility in a Latin American population, but also predicts great hypoglycaemic response to sulfonylurea drugs, such as glipizide among those carrying the E508K variant.
  • the combination of the four SNPs rs75493593, rs483353044, rs2237897, and rs7903146 on the SNP genotyping tool of some embodiments of the present invention advantageously provides useful T2D susceptibility and treatment response information across a wide and ethnically diverse population of subjects.
  • the method comprises the method comprises determining the identity of at least one allele at each of the following SNPs:
  • SLC16A11 rs75493593
  • HNF1A rs483353044
  • KCNQ1 rs2237897
  • TCF7L2 rs7903146
  • FTO rs9936385
  • PPARG rs1801282.
  • the method comprises the method comprises determining the identity of at least one allele at each of the following SNPs:
  • allele determination is carried out at not more than 50, 40, 30, 25, 20, 19, 18, 17 or not more than 16 SNP positions.
  • presence of one or more of the following risk alleles indicates that the subject has greater susceptibility to type 2 diabetes:
  • the method comprises determining the identity of both alleles at each SNP thereby obtaining the genotype of the subject at each SNP.
  • the subject is determined to be heterozygous or to be homozygous for the risk allele at at least one of said SNPs.
  • the subject may be classified as being at greater risk of type 2 diabetes in comparison with a subject having none of said risk alleles or having fewer of said risk alleles.
  • the method comprises 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 faeces.
  • a particularly preferred sample is whole blood, from which has been isolated genomic DNA.
  • the method comprises isolating and/or amplifying genomic DNA from said subject.
  • 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.
  • the method employs a plurality of oligonucleotide probes, which plurality includes a pair of allele-specific oligonucleotide probes for each SNP, said allele-specific oligonucleotide probes each spanning the polymorphic position as set forth in the context sequence column of Table 2.
  • oligonucleotides will be of length 10-50 nucleotides, preferably 12-20 nucleotides, and more preferably 13-18 nucleotides.
  • the skilled person is readily able to design probes that span the SNPs, e.g. making use of the sequence context shown in Table 2.
  • an oligonucleotide probe will comprise of consist of a contiguous sequence of the above-mentioned lengths of the sequence context shown in Table 2 with the polymorphic position typically being located at a central position in each of the allele-specific probes, or its reverse complement or which hybridizes thereto (e.g. under conditions of high stringency).
  • determining the identity of said at least one allele at each SNP comprises TaqMan® SNP genotyping.
  • the method may employ TaqMan® OpenArray® SNP genotyping.
  • determining the identity of said at least one allele at each SNP comprises the use of a platform based in an integrated fluidic circuits (IFCs) system, for genotyping.
  • IFCs integrated fluidic circuits
  • platforms are available from, e.g., Fluidigm.
  • the platform is a Dynamic Array IFC Genotyping Platform.
  • the method comprises determining the number of and identity of SNP risk alleles, and wherein the method further comprises computing a type 2 diabetes risk score for said subject.
  • the method comprises inputting the SNP risk allele determinations into a probability function to compute said risk score.
  • said SNPs include the E508K polymorphism in HNF1A, and the presence of at least one A allele at said E508K polymorphism, rs483353044, indicates that the subject will have a greater hypoglycaemic response to antidiabetic therapy with a sulfonylurea as compared with a biguanide.
  • the sulfonylurea may be Glipizide, Gliclazide, Glibenclamide, Glyburide (Micronase), Glibornuride, Gliquidone, Glisoxepide, Glyclopyramide, Glimepiride (Amaryl), Carbutamide, Acetohexamide, Chlorpropamide or Tolbutamide.
  • the sulfonylurea is Glipizide.
  • the method is a method of treatment or treatment selection, and further comprises administering, or recommending administration of, sulfonylurea therapy to a type 2 diabetic subject who carries at least one A allele of the E508K polymorphism in HNF1A.
  • the sulfonylurea may be glipizide.
  • the subject is of Mexican or Latino American origin or ancestry. In some cases the subject is of European, East Asian, African or Native Mexican origin or ancestry.
  • the subject has at least one first degree relative who has, or has previously been diagnosed with, type 2 diabetes.
  • the subject has one or more clinical risk factors for type 2 diabetes selected from: body mass index>30, waist circumference>80 cm for female or >94 cm for male, age>40, impaired glucose regulation, raised fasting blood glucose, and insulin resistance.
  • the subject is determined to carry one or more of said risk alleles at one or more (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of said SNPs and therefore to be at greater risk of type 2 diabetes, the method further comprising an intervention selected from the group consisting of:
  • the video game comprises a player character presented with food and/or drink choices, wherein the game rewards selection of healthy food and/or drink by the player character.
  • the movement activity monitor comprises a pedometer that communicates or can be configured to communicate to a computer or mobile electronic device (e.g. a mobile telephone such as a smart phone).
  • a computer or mobile electronic device e.g. a mobile telephone such as a smart phone.
  • the calorific counter device comprises a mobile electronic device (e.g. a mobile telephone running an application for tracking calorie intake) programmed to receive information on dietary intake of the subject and to display calorific values corresponding the dietary intake.
  • a mobile electronic device e.g. a mobile telephone running an application for tracking calorie intake
  • the subject is classified into one of the following three risk categories according to the fold-change (FC) value:
  • FC low risk: FC between 0 and 1.00 moderate risk: FC between 1.01 and 1.5; intermediate risk: FC between 1.51 and 2.0; and high risk: FC greater than 2.0.
  • the fold-change value for the subject may be calculated by assigning a risk score for each SNP, wherein the fold-change value is given by multiplying the risk scores of each of the SNPs together.
  • a risk score for each SNP the risk score may be assigned as follows:
  • the fold-change value is derived by multiplying the risk scores for each SNP together and then dividing by the mean odds ratio (OR) for a control group, i.e. a group of patients previously determined not to have T2D.
  • the OR for the risk allele at each SNP is as follows:
  • OR may be as set forth in Table 3.
  • the present invention provides a genotyping tool for use in a method of the first aspect of the invention, 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 three SNPs selected from the group consisting of:
  • the genotyping tool is enriched for probes that interrogate SNPs informative for type 2 diabetes risk prediction in Mexican and Latino American populations.
  • the genotyping tool of the present invention may provide a more efficient tool for assessment of type 2 diabetes risk prediction whereby use of unnecessary probes and other reagents is minimized.
  • said plurality of oligonucleotide probe pairs comprises probe pairs that interrogate at least:
  • SLC16A11 rs75493593
  • HNF1A rs483353044
  • KCNQ1 rs2237897
  • TCF7L2 rs7903146
  • said plurality of oligonucleotide probe pairs comprises probe pairs that interrogate at least:
  • SLC16A11 rs75493593
  • HNF1A rs483353044
  • KCNQ1 rs2237897
  • TCF7L2 rs7903146
  • FTO rs9936385
  • PPARG rs1801282.
  • said plurality of oligonucleotide probe pairs comprises probe pairs that interrogate at least:
  • SLC16A11 rs75493593; HNF1A—rs483353044; TCF7L2—rs7903146; CDKN2A/B—rs10811661; CDKAL1—rs7756992; SLC30A8—rs3802177; IGF2BP2—rs4402960; FTO—rs9936385; PPARG—rs1801282; HHEX/IDE—rs1111875; ADCY5—rs11717195; JAZF1—rs849135; WSF1—rs4458523; INS—IGF2—rs149483638; KCNQ1—rs2237897; and KCNJ11—rs5219.
  • the total number of different SNPs for which allele-specific probes are provided does not exceed 50, 40, 30, 25, 20, 19, 18, 17 or 16.
  • the allele-specific oligonucleotide probes are each covalently attached to a fluorophore, to a quencher and/or to a minor groove binding domain (MGB).
  • MGB minor groove binding domain
  • each member of an allele-specific probe pair is conjugated to a different fluorophore enabling specific detection of the probe pair members by fluorescence wavelength.
  • nucleotide sequence of each of the allele-specific probes is:
  • the array further comprises a primer pair for each 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 tool further comprises one or more reagents for amplification of DNA comprising said SNPs and/or for detection of said allele-specific probes.
  • the tool reagents may include Taq DNA polymerase.
  • the array comprises an OpenArray® of between 1000 and 10000 array positions.
  • the array may comprise 3072 through-holes, each acting as a nanoliter-scale reactor (e.g. 33 nL).
  • the tool is in the form of a TaqMan® OpenArray® SNP genotyping platform or an integrated fluidic circuits (IFC) genotyping platform.
  • IFC integrated fluidic circuits
  • the present invention provides type 2 diabetes risk assessment system for use in a method of the first aspect of the invention, the system comprising a genotyping tool of the second aspect of the invention and a computer programmed to compute a type 2 diabetes risk score from the genotype data of the subject at each of at least three SNPs selected from the SNPs set forth in Table 1 and/or Table 2.
  • the computer computes the risk score from the genotype data by applying a weighting or coefficient to each SNP risk allele found to be present such that the contribution of to the risk score is proportional to that SNP's contribution to type 2 diabetes risk, e.g. a weighting commensurate with an odds ratio for the association of the SNP to type 2 diabetes, as set forth in Table 1.
  • the method of the invention may employ a genotyping tool of the second aspect of the invention or a type 2 risk assessment system of the third aspect of the invention.
  • FIG. 1 shows allelic discrimination plots for each of 16 SNPs employed in the DIABETESpredict SNP genotyping array.
  • the SNPs genotyped are (clockwise from top-left): A) SLC16A11, INS—IGF2, KCNJ11, HHEX/IDE; B) HNF1A, WFS1, TCF7L2, KCNQ1; C) FTO, CDKN2A/B, ADCY5, CDKAL1; and D) PPARG, IGFBP2, SLC30A8, JAZF1.
  • FIG. 2 shows a flow-chart decision tree indicating the application of the DIABETESpredict tool to determine type 2 diabetes genetic risk and therefore the appropriate level of intervention, including diabetes management self care.
  • FIG. 3 shows A) a receiver operating characteristic (ROC) curve in which susceptibility (y-axis) is plotted against 1-specificity (x-axis) for FC1 (blue curve) and FC2 (green curve). A reference line is shown in yellow. The diagonal segments are produced by the draws. B) Area under the curve (AUC) is determined for FC1 and FC2. C) the FC1 risk classifications are shown 0.5, 0.51-1.0 (lower than population mean), 1.01-1.5 low risk, 1.51-2.0 medium risk and >2.0 high risk.
  • ROC receiver operating characteristic
  • FIG. 4 shows a genetic risk spectrum low ( ⁇ 1), moderate (1-1.5), intermediate (1.5-2) and high (>2).
  • An example score “Your score” is depicted by the arrow head pointing to the high genetic risk region on the right hand side of the genetic risk spectrum.
  • SNPs Single Nucleotide Polymorphisms
  • 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/.
  • rs numbers refer to the dbSNP Homo sapiens build 37.1 available from 2 Feb. 2010.
  • SNPs in linkage disequilibrium with the SNPs associated with the invention are useful for obtaining similar results.
  • linkage disequilibrium refers to the non-random 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.
  • 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.
  • linkage disequilibrium is a phenomenon in genetics whereby two or more mutations or polymorphisms are in such close genetic proximity that they are co-inherited. This means that in genotyping, detection of one polymorphism as present infers the presence of the other.
  • a polymorphism or alteration in such linkage disequilibrium acts as a surrogate marker for a polymorphism or alteration as disclosed herein.
  • LD is preferably determined in a Mexican or Latino American population.
  • the HNF1A E508K SNP (rs483353044) is believed to be in strong LD with the SNP rs143592417 (which encodes Q511R), such that rs143592417 may in some cases be used, in accordance with any aspect of the present invention, as a proxy SNP for rs483353044.
  • 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.
  • 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).
  • RFLP restriction fragment length polymorphism
  • SSCP single strand conformation polymorphism analysis
  • CCM chemical cleavage of mismatch
  • DPLC denaturing high performance liquid chromatography
  • a SNP is detected through PCR amplification and sequencing of the DNA region comprising the SNP.
  • 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 cm 2 or more can be typically achieved.
  • 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.
  • 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.
  • the fragment has been amplified, e.g. by using the polymerase chain reaction (PCR), and labelled e.g. with a fluorescent molecule.
  • PCR polymerase chain reaction
  • 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 risk allele.
  • the amplification reaction and/or extension reaction is carried out on the microarray or bead itself.
  • methods described herein may involve hybridization.
  • differential hybridization based methods there are a number of methods for analysing hybridization data for genotyping:
  • 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.
  • oligonucleotide a minimum of “2n” oligonucleotides that overlap with the previous oligonucleotide in all the sequence except in the nucleotide are necessary.
  • the size of the oligonucleotides is about 25 nucleotides.
  • the oligonucleotide can be any length that is appropriate as would be understood by one of ordinary skill in the art.
  • the use of a minor groove binding domain (MBD) permits shorter probe sequences while retaining high discrimination between the perfect match and the mismatch.
  • MBD minor groove binding domain
  • the increased number of oligonucleotides used to reconstruct the sequence reduces errors derived from fluctuation of the hybridization level.
  • this method is combined with sequencing to identify the mutation.
  • 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.
  • 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.
  • the starting material can be either an RNA sample or a DNA product amplified by PCR.
  • Tag arrays strategy an extension reaction is carried out in solution with specific primers, which carry a determined 5′ sequence or “tag”.
  • 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).
  • the need for amplification and purification reactions presents disadvantages for the on-chip or on-bead extension/amplification methods compared to the differential hybridization based methods.
  • the techniques may still be used to detect and diagnose conditions according to the invention.
  • Microarray or bead analysis is carried out using differential hybridization techniques.
  • differential hybridization does not produce as high specificity or sensitivity as methods associated with amplification on glass slides.
  • mathematical algorithms which increase specificity and sensitivity of the hybridization methodology, are needed (Cutler D J, Zwick M E, Carrasquillo M N, Yohn C T, Tobi K P, Kashuk C, Mathews D J, Shah N, Eichler E E, Warrington J A, 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.
  • the genotyping platform for use in the methods of the present invention may be based on the Dynamic Array IFCs Genotyping System from Fluidigm. Further details of the Dynamic Array IFCs Genotyping System are available from Fluidigm webpage.
  • Example 1 Selection of 16 Single Nucleotide Polymorphisms (SNPs) for a Type 2 Diabetes Genetic Prediction Tool in Mexican (and Latino) Populations
  • SNPs were prioritized from the largest meta-analyses of extant genome-wide association studies (GWAS) performed in European and other populations, under the assumptions that: (1) their effects in other populations were generalizable and (2) the largest sample sizes available provided the most robust estimates of the true effect size.
  • the SIGMA1 GWAS dataset was used to ensure that the proposed SNPs have consistent effects in the Mexican population. Consideration was made of allele frequencies in Mexicans, to maximize the predictive power of a SNP at the population level (effect size ⁇ allele frequency). To maximize flexibility and minimize cost, a single array of 16 SNPs was chosen. It was decided that the array would not include ancestry informative markers.
  • SNPs were ranked by their odds ratio in the largest dataset available (Morris et al., Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nature Genetics (2012) 44:981-990), followed by their P value for association in that GWAS dataset. They were aligned to the same SNPs or to proxies at the same loci in the other two GWAS datasets. Concordance was sought with the transethnic data set from the DIAGRAM Consortium (DIAGRAM Consortium. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nature Genetics (2014) 46:234-244).
  • HNF1A c.1522G>A[p.E508K]
  • HNF1A c.1522G>A[p.E508K]
  • SIGMA Type 2 Diabetes Consortium JAMA, 2014; 311(22), pp. 2305-2314—the entire contents of which are expressly incorporated herein by reference).
  • SNPs were rejected if they had allele frequencies ⁇ 8% in Mexicans (with the exception of HNF1A E508K), or a divergent direction of effect in the SIGMA GWAS (The SIGMA Consortium: Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico. Nature (2014) 506:97-101—the entire contents of which are expressly incorporated herein by reference); conversely, they were promoted if they had a P ⁇ 0.003 (liability model without BMI adjustment) with a constant odds ratio in SIGMA (The SIGMA Consortium, Nature (2014) 506:97-101).
  • the SNP associated with T2D in East Asians and Mexicans was chosen instead of the European SNP because the European SNP was not a good proxy for the East Asian/Mexican SNP.
  • Table 2 shows target genes, SNP rs identifiers, chromosome and nucleotide location (build 37) and SNP context sequence for each of the 16 SNPs.
  • SNP Context Sequence SEQ ID Target rs ID Location (b. 37) SNP Context Sequence NO: TCF7L2 rs7903146 ch. 10: 114758349 TAGAGAGCTAAGCACTTTTTAGATA[C/T]TATATAATTTAATTGCCGTATGAGG 1 — rs10811661 ch. 9: 22134094 CAGCTCACCTCCAGCTTTAGTTTTC[C/T]CATGACAGTAAGTCTATTACCCTCC 2 CDKAL1 rs7756992 ch.
  • the TaqMan® OpenArray® genotyping system (Life Technologies Corp., Calsbad, Calif.) can be employed as a high-throughput platform for genotyping subject-derived DNA samples at each of the 16 SNPs identified in Table 1.
  • each SNP two allele-specific probes were provided.
  • Each of the allele-specific probes is conjugated to a fluorescent dye, a quencher and a minor groove binding (MGB) domain.
  • the fluorescent reporter dyes are chosen so that the probe specific for the risk allele is distinguishable from the probe specific for the non-risk allele at the SNP in question.
  • the fluorophores VIC and 6-FAM were employed and were covalently attached to the 5′ end of the respective allele-specific probe. Near the 3′ end of the allele-specific probe, a non-fluorescent quencher was attached.
  • the MGB increases the melting temperature (Tm) of the probes providing great separation between matched and mismatched probes and thereby increasing genotyping accuracy.
  • forward and reverse primers that flank the SNP of interest.
  • TaqMan genotyping reagents for rs7903146 in TCF7L2 are available from Life Technologies under the product code C_29347861_10.
  • 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.
  • the 3072 through-hole OpenArray® format was chosen, each through-hole acting as a 33 nL reactor. This plate format allows examination of 144 samples against the 16 unique SNPs per plate.
  • Genotyping results shown in FIG. 1 Each of homozygous allele 1, heterozygous and homozygous allele 2 is distinguished demonstrating that the TaqMan® OpenArray® SNP Genotyping platform is able to discriminate the different possible genotypes at each of the 16 SNP targets.
  • HNF1A HNF1A
  • E508K exon 8
  • This finding is consistent with that has been observed in other described mutations of exon 8; and is associated with late-onset diabetes.
  • the initial clinical and biochemical characterization of subjects with the HNF1A E508K mutation showed that carriers of the variant have a clinical profiles indistinguishable from those of patients with Type 2 Diabetes.
  • Pharmacogenetic studies are required to demonstrate that the carriers with the E508K variant selectively respond to sulfonylureas. Testing of this hypothesis has practical implications because it would be possible to propose a simple and inexpensive genetic test that helps clinicians select the appropriate pharmacological treatment in a significant number of cases.
  • Acute and medium-term response to glipizide in patients with and without the E508K variant of HNF1A will be measured.
  • the study will consist of two phases. Patients will be invited to participate in both, but the research subject will have the freedom to choose only one of the stages of the study.
  • Secondary objectives are: (1) to compare the change of the plasma insulin concentration between carriers and non-carriers after a single oral administration of 5 mg of glipizide and (2) to compare the number of patients suffering from symptomatic hypoglycemia between carriers and non-carriers after a single oral administration of 5 mg of glipizide.
  • Noncarriers This will be a single center, open study. Approximately 100 eligible subjects with type 2 diabetes in Mexico will be included in the study in two groups. In the first group, 50 patients carrying the p.E508K mutation in HNF1A will be included (“carriers”). Patients will be recruited from existing databases in which they have already been genotyped as carriers of HNF1A p.E508K. In the second group, 50 patients will be invited that do not present the mutation in HNF1A p.E508K (“noncarriers”). The inclusion of non-carrier group will begin after the carrier group completes the study procedures, in order to select the controls taking into account case characteristics of sex, BMI, age, and age of diabetes onset.
  • Patients in the group of non-carriers can be identified from the same database or other sources.
  • An additional group of 25 patients with MODY3 loss of function in HNF1A will be recruited from Norway and will serve as a positive control for the experimental condition (an increased sensitivity to sulfonylurea).
  • Patients will be eligible for the study if they have: (1) Type 2 Diabetes; (2) treat themselves with less than two oral antidiabetic agents; (3) can safely undergo a 7-day washout of antidiabetic drugs; (4) have HbA1c ⁇ 7.5%; and (5) do not have an allergy or intolerance to glipizide or other medications with homology to sulfonylureas.
  • Eligible patients will discontinue antidiabetic therapy and initiate a washout period of 7 days, during which they will be monitored for hyperglycemia requiring re-initiation of antidiabetic therapy. If the fasting blood glucose is ⁇ 250 mg/dl (13.9 mmol/1) during the washout period, the patient should contact the study center within 24 hours, and the investigator will determine whether the participant must try to improve diet and exercise to maintain glycemic control or if the participant has to end their participation in the study and restart treatment. The patient will be excluded from further participation if more than 2 blood glucose values in consecutive fasting are 250 mg/dl. Patients who do not receive the drug treatment for hyperglycemia will skip the washout period and will start the study treatment.
  • Patients will be admitted to the clinical research center after fasting for 8 hours.
  • An intravenous angiocatheter 20g or 22g will be inserted for blood draws at multiple time points. Samples will be obtained during fasting periods.
  • Patients with a fasting blood glucose (based on the assessment made using a glucometer) of >80 mg/dl will receive glipizide 5 mg orally.
  • Subjects with blood glucose ⁇ 80 mg/dl will not receive the dose of glipizide.
  • the concentrations of glucose, insulin and other hormones (GLP1) and metabolites will be measured at 15, 30, 60, 90, 120, 180, and 240 minutes after administration of 5 mg of glipizide. In the event of developing symptoms of hypoglycemia, blood glucose will be measured immediately.
  • the patient will receive hypoglycemia intervention by ingesting carbohydrates if blood glucose is ⁇ 50 mg/dL with or without symptoms of hypoglycemia, or if blood glucose is ⁇ 70 with symptoms of hypoglycemia. All patients will be given a meal high in carbohydrates and fat at the end of the study visit. The visit will end when: 1) the patient completes an observation period of 240 minutes or has been intervened due to hypoglycemia and 2) the value of blood glucose 30 minutes after meal completion of the study is >80 mg/dl. Participants will be instructed to eat more carbohydrates, if blood glucose values ⁇ 80 mg/dl. After completion of the study protocol, patients will be advised to resume their antidiabetic therapy.
  • the primary objective is to compare the glucose response between carriers and noncarriers after administration of glipizide.
  • the primary endpoint will be the delta of the glucose concentration during the test. Secondary endpoints will be the lesser of glucose (with or without adjustment for baseline glucose) and the area under the glucose curve during curve (adjusted for baseline glucose). Other secondary endpoints include the change in the concentration of insulin and the number of patients suffering from symptomatic hypoglycemia. The insulin peak value (adjusted for basal insulin) to serve as the final evaluator of the effect of insulin secretion.
  • Secondary objectives are to compare the following variables between treatment groups and between carriers and non-carriers: fasting blood glucose, postprandial blood glucose and insulin, fructosamine, number of symptomatic hypoglycemias, adverse effects and cases that did not tolerate the maximum dose given.
  • Patients will be eligible for the study if they: (1) have Type 2 Diabetes; (2) are treated by less than two oral antidiabetic agents; (3) can safely undergo a wash of 6 weeks of antidiabetic drugs; (3) have HbA1c between 7-10%; (4) have estimated glomerular filtration rate greater than 60 ml/min/1.73 m 2 and have AST and ALT ⁇ 2.5 times higher than normal limit; (5) no allergy or intolerance to glipizide, metformin or other medications with homology to sulfonylureas.
  • Eligible patients will suspend their antidiabetic therapy and have a washout period of 6 weeks, during which they will be monitored in a timely manner to detect the presence of hyperglycemia requiring re-initiation of antidiabetic therapy. All patients will be given a diet and exercise plan at the beginning of the washout period. Patients will be instructed to measure their capillary fasting plasma glucose. If blood glucose is 250 mg/dl (13.9 mmol/l) during the washout period, the patient should contact the study center within 24 hours, and the investigator will determine if the participant should begin treatment with a pharmacological inhibitor of DPP-IV for glucose control.
  • the patient will be excluded from further participation if, after starting therapy with DPP-IV, they have more than 2 blood glucose values at consecutive fasting ⁇ 250 mg/dl during the washout period. Patients with blood glucose ⁇ 250 mg/dl (13.9 mmol/L) and symptoms of hyperglycemia will resume their prior antidiabetic treatment in accordance with the standard of care. Patients who do not receive drug treatment for hyperglycemia will skip the washout period and will start the study treatment.
  • the first dose of study medication will be administered at the research center. Patients will receive glipizide (5 mg, orally) or metformin (500 mg orally). Patients will have an assessment of capillary glucose 1 hour after the high carbohydrates and fat meal; and will be discharged if their blood glucose is above 80 mg/dl.
  • Patients will then begin the outpatient phase of the study treatment on Day 2.
  • patients will initiate a dose of 500 mg twice daily for 1 week, then 500 mg every morning and 500 mg every night for one week, and then 1 g twice daily for the remaining 10 weeks.
  • Patients will begin taking glipizide at 5 mg twice daily for 1 week, followed by 10 mg twice a day for 1 week, and 20 mg twice daily for the remaining 10 weeks.
  • the treatment dose will be reduced to half if there are more than 2 episodes of symptomatic hypoglycemia or any other side effects related to the study drugs administered.
  • Patients will be asked to measure their fasting capillary blood glucose at least two days a week.
  • Patients will return to the clinic after 6 weeks and 12 weeks of treatment for a blood sample to be taken under fasting conditions and review of medication compliance and tolerance. Patients with more than 2 values in consecutive blood glucose fasting 250 mg/dl or severe signs and symptoms of hyperglycemia during the main treatment will begin additional antidiabetic therapy. Therapies other than metformin or sulfonylureas during the main treatment for the control of severe hyperglycemia agents will be allowed. After 12 weeks of treatment a test of mixed meal will be performed, similar to the one conducted at the beginning of the treatment. Intravenous angiocatheter 20g or 22g will be placed for blood draws at multiple time points, with samples to be obtained during fasting.
  • Patients will undergo a test of mixed meal tolerance, to be completed 15 minutes after starting the meal. They will take the appropriate dose assigned at the beginning of the test treatment. Blood glucose, insulin, and the sample for further investigative measures will be assessed at 15, 30, 60, 90, 120, 180, and 240 minutes after starting the meal. At the end of the observation period of 240 min, patients will receive a diet high in carbohydrates and fat. After completion of the study protocol, patients will resume using the antidiabetic therapy prior to study.
  • the primary objective will be to compare the reduction in HbA1c at 12 weeks of glipizide or metformin.
  • the reduction in HbA1c at week 12 will be compared between baseline glipizide and metformin using analysis of covariance (ANCOVA), adjusted for stratification factors at randomization (state at the start of the treatment period, and HbA1c main treatment).
  • ANCOVA analysis of covariance
  • the analysis with respect to the primary endpoint will be conducted focusing on an intention to treat approach and one that includes only those cases who completed the study.
  • the secondary objective is to compare the changes in fasting glucose, changes in post-prandial glucose and insulin, fructosamine change, weight, rates of hypoglycemia, general safety, and the number of patients not achieving dose maximum treatment. These criteria will be exploratory and not adjusted for multiplicity.
  • the power of the study is 80% with 45 subjects in each group. Fifty patients will be enrolled in each group to ensure a sufficient number of evaluable subjects.
  • Glipizide was selected because it has a high oral absorption rate, it starts acting within 30 minutes and its life is short (2-8 hours).
  • the proposed dose (5 mg) is usually the starting dose used in patients with Type 2 Diabetes.
  • the risk of inducing severe hypoglycemia is low (0.19-2.5 episodes per 1000 patient-years).
  • Metformin is the only biguanide available. It is the basis of the pharmacological treatment of diabetes.
  • the proposed dose is associated with a low rate of gastrointestinal adverse events. The drugs will be provided to participants at no cost.
  • Expected discomfort is hypoglycemia symptoms caused by venipuncture, gastrointestinal effects (diarrhea and flatulence) caused by metformin and stress to participate in a research study or the knowledge of the existence of abnormalities in carbohydrate metabolism.
  • capillary glucose measurements will be taken and the protocol in place reduces exposure of patients to medicines if their blood sugar is considered to be at risk (see previous paragraphs).
  • Resources required to treat hypoglycemia by administering glucose orally or intravenously will be available.
  • the total blood volume at visit 1 is 167 ml and 118 ml at visit 2.
  • the samples will be obtained by trained personnel.
  • the expected discomforts are skin lesions, bruising and pain. Metformin can cause bloating or diarrhea. If diarrhea is intolerable, the patient will be excluded from the study.
  • the game would be developed for the Android platform using a touch-based interface.
  • the instructional goal would be to reinforce the benefits of eating the right types of foods.
  • a key intent would be to develop a gameplay style that is “addictive” to encourage long-term gameplay.
  • food options and bonuses offered in the gameplay can be chosen by the player based on selecting from available options (e.g., by selecting certain levels of the game to play—e.g., play a “high carb” level—or via selection of an “eating profile”). These options would be pre-determined to reflect typical and desired eating habits/foods in the target population. Players may choose based on what they like to eat or have eaten recently, or simply explore the impact of different preferences. These would not be “self-report” quality, but would be designed to educate/reinforce the pros and cons of different choices (e.g., choosing a high sugar/carb diet may allow them to go faster in the game but make few bonus options offered in the game and make it harder to make it all the way through the level).
  • the game would likely be developed for the personal computer to enable interrupts to the individual's normal activities (it is hard for one process to interrupt another on android, though it can send simple notifications).
  • the instructional goal would be to reinforce the long-term effects of good or bad eating.
  • An assessment goal would be to collect some self-report information on their eating habits and diabetes knowledge.
  • a key intent would be to develop an interaction that the player would want to engage with in brief stints over a long period of time.
  • a virtual creature friend semi-randomly “pops up” on the player's device begging to be fed. From example, this friend could be an alien, weird monster, lego-type character, a virtual human, etc. (Not an animal since that would send mixed messages and have kids feeding their real pets human food that may be bad for them).
  • the choice of avatar would be made from a set of pre-determined options.
  • the friend pops up, the player has to choose some foods to give it.
  • we use this action as an opportunity to collect data on what the kids have been eating recently.
  • the virtual friend is a copycat and always wants to eat what the child did. When fed well, it is “happy”.
  • a diabetes-related question e.g., the friend asks “is this good for me to eat” or “how come I can't do xxx”?), or because it wants to play/get attention (e.g., to be petted).
  • the friend gets sad (e.g., whimpers) and doesn't want to play (i.e., even if requested by the player—the player must now act to rectify the situation by feeding them well).
  • the gameplay would be similar to the Option 2 game.
  • the eating options and bonuses available in the game would be generated based on the pictures taken by the player.
  • a number of back-end elements would be developed to support the data collection and assessment.
  • the system would involve multiple game-server interactions, with the server providing data that influences the game, as well as collecting self-reports and other data generated by the game.
  • GUI graphical user interface
  • Interaction Elements We will explore multiple methods of sharing among players to encourage an excitement towards eating in a healthier manner. Methods may include: Rating systems for favorite meals, regional competitions for best eating habits (e.g., highest rated meal picture), pair-wise matching for enhanced collaborative learning and perhaps even rescue operations where players can swoop in to help a player figure out how to bring their unhappy/sick avatar back to full health.
  • the level of effort includes multiple rounds of design and multiple development and deployment iterations to ensure the methods used have appeal and are effective at encouraging long-term participation.
  • This effort would involve the creation of a rich set of interactions among players outside the game. For privacy and security reasons, chat capabilities are not anticipated. However, sharing of basic player “handles”/nicknames and rough geographical area are anticipated. No self-taken images will be posted without vetting by a moderator to ensure appropriateness and privacy.
  • FC Low risk: FC between 1.01 and 1.5 Intermediate risk: FC between 1.51 and 2.0 High risk: FC>2.0
  • VPP positive predictive value
  • VPN negative predictive value
  • FC Sensibilidad Especificidad VPP VPN >1 71.8 51.3 23.2 89.9 >1.5 35.9 82.3 29.3 86.2 >2 25.3 92.5 41.0 85.8

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