Composition for predicting sulfonylurea dependence by using genetic marker, and prediction method therefor

Abstract

The present invention relates to a composition and a method capable of predicting sulfonylurea dependence in diabetic patients by identifying the variants of: rs777077261(C>A) of KCNB2(Potassium Voltage-Gated Channel Subfamily B Member 2); rs202112906(C>T) of GHSR(Growth hormone secretagogue receptor); rs28944173(T>C) of NOS2(Nitric oxide synthase 2); rs3792268(C>A) of CAPN10(Calpain 10); rs199670165(C>A), rs17848945(C>T), rs755372392(G>A), rs200840955(G>A), rs200842352(C>A) or rs45489599(C>T) of FASN(Fatty acid synthase); rs185358392(G>A), rs769120237(G>A), rs201884071(G>A) or rs766766661(C>T) of KCNH6(Potassium Voltage-Gated Channel Subfamily H Member 6); rs14847401(G>A), rs37551700(G>A) or rs774656238(A>G) of DIS3L2(DIS3 like 3'-5' exoribonuclease 2); or rs74315349(G>A) or rs150546920(C>G) of NR0B2(Nuclear Receptor Subfamily 0 Group B Member 2).

Description

Composition for predicting sulfonylurea dependence using genetic markers and prediction method thereof

[0001] The present invention relates to a composition for predicting sulfonylurea dependence and a method for predicting sulfonylurea dependence.

[0002]

[0003] Compared to research on markers associated with diabetes susceptibility, research on markers associated with the effectiveness of diabetes treatments is minimal. Consequently, competing technologies for genetic markers with clinical significance for diabetes treatment use exist, and prescriptions are based on the experience of medical professionals and the patient's clinical characteristics.

[0004] Currently, there are nine classes of diabetes medications used clinically, and there are prescription guidelines recommended by academic societies in Korea, the United States, and Europe (Standards of Medical Care in Diabetes. Diabetes Care Jan 2022, Korean Diabetes Association Diabetes Practice Guidelines 2021), but these guidelines are not based on predictions of blood sugar-lowering effects and do not contribute to predictions.

[0005] Sulfonylureas are among the most potent hypoglycemic agents with a long history. However, compared to other classes of drugs, they carry a higher risk of hypoglycemia. Furthermore, with the development of other oral hypoglycemic agents with proven cardiovascular protective effects, their overall prescription priority has declined. However, a significant reliance on sulfonylureas for glycemic control has been identified in some patient populations, suggesting a need for preferential treatment with sulfonylureas in these specific patients. However, no clinical characteristics or markers are known to predict this preference.

[0006] Therefore, it is necessary to discover markers related to sulfonylurea dependence for diabetes treatment and develop technologies to confirm sensitivity to sulfonylureas based on these markers.

[0007]

[0008] The present invention seeks to discover a gene that determines sulfonylurea dependence and provide a composition for predicting sulfonylurea dependence.

[0009] The present invention seeks to provide a method for providing information for predicting sulfonylurea dependence.

[0010]

[0011] 1. rs777077261(C>A) of KCNB2 (Potassium Voltage-Gated Channel Subfamily B Member 2); rs202112906(C>T) of GHSR (Growth hormone secretagogue receptor); rs28944173(T>C) of NOS2 (Nitric oxide synthase 2); CAPN10 (Calpain 10) or rs45489599(C>T);rs185358392(G>A), rs769120237(G>A), rs201884071(G>A), or rs766766661(C>T) of Potassium Voltage-Gated Channel Subfamily H Member 6 (KCNH6);DIS3L2(DIS3) like rs14847401 (G>A), rs37551700 (G>A) or rs774656238 (A>G) of 3'-5' exoribonuclease 2); A composition for predicting sulfonylurea dependence in a diabetic patient, comprising a preparation that identifies a mutation of rs74315349 (G>A) or rs150546920 (C>G) in NR0B2 (Nuclear Receptor Subfamily 0 Group B Member 2).

[0012] 2. A composition for predicting sulfonylurea dependence in a diabetic patient, wherein the preparation is a primer or probe that specifically binds to at least one of the genes in the above 1.

[0013] 3. rs777077261(C>A) of KCNB2 (Potassium Voltage-Gated Channel Subfamily B Member 2); rs202112906(C>T) of GHSR (Growth hormone secretagogue receptor); rs28944173(T>C) of NOS2 (Nitric oxide synthase 2); CAPN10 (Calpain 10) or rs45489599(C>T);rs185358392(G>A), rs769120237(G>A), rs201884071(G>A), or rs766766661(C>T) of Potassium Voltage-Gated Channel Subfamily H Member 6 (KCNH6);DIS3L2(DIS3) like rs14847401 (G>A), rs37551700 (G>A) or rs774656238 (A>G) of 3'-5' exoribonuclease 2); A method for providing information for predicting sulfonylurea dependence in a diabetic patient, comprising the step of identifying a mutation of rs74315349 (G>A) or rs150546920 (C>G) in NR0B2 (Nuclear Receptor Subfamily 0 Group B Member 2).

[0014] 4. A method for providing information for predicting sulfonylurea dependence in diabetic patients, wherein in the above 3, the confirmation is performed by adding a primer or probe that specifically binds to at least one of the genes and confirming the sequence thereof.

[0015] 5. A method for providing information for predicting sulfonylurea dependence in a diabetic patient, further comprising: a step of providing information that an individual having at least one of the mutations in the above 3 has sulfonylurea dependence.

[0016]

[0017] The present invention can be used to predict whether a diabetic patient is highly susceptible to the blood sugar lowering effect of sulfonylureas by using genes such as KCNB2, GHSR, NOS2, CAPN10, FASN, KCNH6, DIS3L2, and NR0B2 as markers, and to provide information therefor.

[0018]

[0019] Figure 1 is a schematic diagram of a method for deriving a sulfonylurea-dependent associated mutation of the present invention using a retrospective method.

[0020] DM, diabetes mellitus; FPG, fasting plasma glucose; MAF, minor allele frequency; SNV, single nucleotide variant; SU, sulfonylurea; WES, whole exome sequencing

[0021] Figure 2 shows the blood concentration of HbA1c (glycated hemoglobin) or FPG (fasting plasma glucose) after discontinuation of sulfonylurea administration in a retrospective study of the present invention.

[0022] Values ​​are expressed as mean ± standard deviation.

[0023] ***, p < 0.001 between the 2 groups by 2-way repeated measures ANOVA

[0024] FPG, fasting plasma glucose; peak, highest measures within 6 months after SU discontinuation; SU, sulfonylureas

[0025] Figure 3 is a schematic diagram of a method for prospectively verifying candidate mutations derived from a retrospective study of the present invention.

[0026] FPG, fasting plasma glucose; SU, sulfonylurea; WES, whole exome sequencing

[0027] Figure 4 shows the blood concentration of HbA1c (glycated hemoglobin) or FPG (fasting plasma glucose) after discontinuation of sulfonylurea administration in a prospective study of the present invention.

[0028] Values ​​are expressed as mean ± standard deviation.

[0029] **, p < 0.01 between the 2 groups by 2-way ANOVA

[0030] FPG, fasting plasma glucose; peak, highest measures within 20 weeks after SU discontinuation; SU, sulfonylureas

[0031] Figure 5 is a schematic diagram of a method for extracting UK Biobank registrants to verify mutations in the derived genes of the present invention in a large population.

[0032] eGFR, estimated glomerular filtration calculated by the CKD-EPI method; SU, sulfonylureas

[0033] Figure 6 is a schematic diagram of a method for extracting variants from a public genetic database to verify the candidate genes of the present invention in a large-scale population group, UK Biobank.

[0034] SU, sulfonylureas; WES, whole exome sequencing

[0035] dbNSFP v4. (http: / / database.liulab.science / dbNSFP)

[0036] genomAD exomes (https: / gnomad.broadinstitute.org)

[0037] Figure 7 summarizes the literature supporting the hypothesis that loss-of-function mutations in NR0B2 regulate the SU response in insulin-secreting cells through the transcription factors HNF1A and HNF4A. References include: EMBO J. 1998; 17(22):6701; Diabetes. 2022; 71(4):862; Cell Death Dis. 2022; 13(1):89; Proc Natl Acad Sci US A. 2001; 98(2):575; Diabetes. 2013; 62(11):3909.

[0038] GSIS, glucose-stimulated insulin secretion; HNF, hepatocyte nuclear factor; NR0B2, Nuclear Receptor Subfamily 0 Group B Member 2; SU, sulfonylureas

[0039]

[0040] The present invention will be described in detail below. Unless otherwise defined, all terms used in this specification have the same general meaning as those skilled in the art to which the present invention pertains. If there is a conflict between the meaning of a term used in this specification and the meaning used in this specification, the meaning used in this specification shall prevail.

[0041]

[0042] The present invention is rs777077261 (C>A) of KCNB2 (Potassium Voltage-Gated Channel Subfamily B Member 2); rs202112906 (C>T) of GHSR (Growth hormone secretagogue receptor); rs28944173 (T>C) of NOS2 (Nitric oxide synthase 2); CAPN10 (Calpain 10) or rs45489599(C>T);rs185358392(G>A), rs769120237(G>A), rs201884071(G>A), or rs766766661(C>T) of Potassium Voltage-Gated Channel Subfamily H Member 6 (KCNH6);DIS3L2(DIS3) like rs14847401 (G>A), rs37551700 (G>A) or rs774656238 (A>G) of 3'-5' exoribonuclease 2); Or, it relates to a composition for predicting sulfonylurea dependence in a diabetic patient, comprising a preparation that identifies rs74315349 (G>A) or rs150546920 (C>G) mutation of NR0B2 (Nuclear Receptor Subfamily 0 Group B Member 2).

[0043] In the present invention, the sulfonylurea dependence refers to the degree to which a diabetic patient must take only a sulfonylurea or take a sulfonylurea preferentially over other drugs to lower blood sugar levels. That is, when taking a conventionally known hypoglycemic agent other than a sulfonylurea, such as Metformin, Gliptin, or Pioglitazone, blood sugar levels do not decrease or the effect is not as expected, but when taking a sulfonylurea, the expected blood sugar level effect can be achieved. Therefore, this means that a sulfonylurea should be taken preferentially.

[0044] In the present invention, the composition for predicting sulfonylurea dependence refers to a composition capable of predicting whether a diabetic patient is dependent on sulfonylureas by using a drug that detects the mutation. Specifically, a patient with at least one of the mutations described above is predicted to have a high dependence on sulfonylureas. However, this is not limited thereto.

[0045] Each of the above genes may have a sequence in the predicted target. The above sequences for each species are all known.

[0046] In the present invention, the agent for identifying the mutation may be used without limitation in any type as long as it can identify a genetic sequence or the like. Examples include, but are not limited to, primers, probes, antibodies, or antisense nucleic acids. The primers, probes, or antisense nucleic acids can be used to amplify or confirm the presence of a nucleic acid sequence having a specific allele at the mutation site.

[0047] In the present invention, the diabetic patient may be included within the scope of the present invention regardless of the type of diabetes. Preferably, the patient may have type 2 diabetes mellitus (T2DM), but is not limited thereto.

[0048]

[0049] In addition, the present invention provides a method for detecting, in a biological sample isolated from an individual, rs777077261 (C>A) of KCNB2 (Potassium Voltage-Gated Channel Subfamily B Member 2); rs202112906 (C>T) of GHSR (Growth hormone secretagogue receptor); rs28944173 (T>C) of NOS2 (Nitric oxide synthase 2); rs3792268 (C>A) of CAPN10 (Calpain 10); rs199670165 (C>A), rs17848945 (C>T), rs755372392 (G>A), rs200840955 (G>A), rs200842352 (C>A) of FASN (Fatty acid synthase) or rs45489599(C>T);rs185358392(G>A), rs769120237(G>A), rs201884071(G>A), or rs766766661(C>T) of Potassium Voltage-Gated Channel Subfamily H Member 6 (KCNH6);DIS3L2(DIS3) like rs14847401 (G>A), rs37551700 (G>A) or rs774656238 (A>G) of 3'-5' exoribonuclease 2); Or, it relates to a method for providing information for predicting sulfonylurea dependence in a diabetic patient, including a step of identifying a mutation of rs74315349 (G>A) or rs150546920 (C>G) in NR0B2 (Nuclear Receptor Subfamily 0 Group B Member 2).

[0050] In the present invention, the method for providing information for predicting sulfonylurea dependence in diabetic patients may further include a step of obtaining genotype information of the individual by amplifying and sequencing DNA from a biological sample isolated from the individual. Any method known to those skilled in the art for DNA amplification, sequencing, and obtaining genotype information may be used without limitation.

[0051] In addition, the information providing method of the present invention may further include a step of providing information that an individual having at least one of the mutations has sulfonylurea dependence.

[0052]

[0053] Hereinafter, the present invention will be described in detail by way of examples to specifically explain the present invention.

[0054]

[0055] Experimental Example 1. Identification of SU-dependent patients through retrospective medical record search.

[0056] A retrospective observational study was conducted on adult patients with type 2 diabetes who visited Seoul National University Hospital from 2009 to 2015 and who had stopped taking low-dose sulfonylureas (SU, glimepiride equivalent ≤ 2 mg / day).

[0057] SU dependence was defined as meeting all of the following criteria: (1) HbA1c ≤7.0% or HbA1c ≤7.5% at risk of hypoglycemia for at least 6 months while taking low-dose SU (glimepiride equivalent ≤2 mg / day), (2) HbA1c increase of ≥1.2% within 3 months or ≥1.5% within 6 months after discontinuation of SU, regardless of whether other oral antidiabetic agents were added or administered, (3) SU re-introduction, and (4) HbA1c reduction of ≥0.8% or fasting blood glucose reduction of ≥40 mg / dL within 3 months of SU re-introduction.

[0058] Patients were considered SU-independent if HbA1c remained below 7.5% after SU discontinuation and SU was not restarted by the final data collection in 2019. SU-independent controls were selected from patients with diabetes for more than 10 years and were clinically matched to SU-dependent patients by age, sex, and antidiabetic medication. Patients were excluded from the analysis if they had impaired renal function (serum creatinine >1.4 mg / dL or estimated glomerular filtration rate [eGFR] <50 mL / min / 1.73 m²), clinically significant liver disease, were taking medications that could affect glycemic control, such as glucocorticoids, or developed serious medical problems during the change in SU use.

[0059] As a result, 21 patients with SU dependence and 19 control subjects without SU dependence were selected. Patients with SU dependence showed a significant increase in blood glucose after SU discontinuation (HbA1c increased from 6.6 ± 0.4% to 8.8 ± 1.0% after 20 weeks) and a significant decrease in blood glucose after SU re-intake (HbA1c 6.9 ± 0.5% after 12 weeks). Fasting blood glucose levels also showed a similar trend, confirming a drug response that was significantly different from that of the SU-nondependent control group (Fig. 2). However, because the clinical characteristics and antidiabetic agent administration before SU discontinuation were similar in both groups (Table 1), it was suggested that it was difficult to distinguish the responsiveness to SU before SU discontinuation.

[0060]

[0061] Clinical characteristics before SU discontinuation and comparison of antidiabetic agents after discontinuation

[0062]

[0063] Data are presented as mean ± standard deviation or median (range).

[0064] eGFR is estimated glomerular filtration rate; SU is sulfonylurea; T2DM is type 2 diabetes mellitus.

[0065] *, analyzed by Student's t-test, Mann Whitney test, and Fisher's exact test

[0066]

[0067] Experimental Example 2. Whole-exome sequencing (WES)

[0068] Therefore, we aimed to analyze the genetic characteristics of patients with SU dependence. Because the sample size was small, we planned to perform whole-exome sequencing to identify rare variants with large effect sizes. Among 21 patients with SU dependence, blood samples were collected for DNA extraction from 17 patients who voluntarily provided written informed consent. This study was conducted in accordance with the principles of the Declaration of Helsinki and Good Clinical Practice, and the institutional review board approved the study (#1407-103-596). Exome sequencing was performed at Macrogen (Seoul, Korea). Briefly, DNA was extracted from blood leukocytes, exomes were captured using SureSelect v4+UTR (Agilent Technologies, Santa Clara, CA, USA), and sequenced at 100x coverage using the Hiseq2,000 sequencing system (Illumina Inc., San Diego, CA, USA). Sequence reads were aligned to the UCSC genome assembly hg19 data using BWA, and variants were called and matched using SAMtoll and ANNOVAR software.

[0069]

[0070] Experimental Example 3. Derivation of Candidate Mutations (Figure 1)

[0071] For gene-based analysis, 260 target genes reported to be associated with beta cell function and beta cell mass were selected, and single nucleotide variants (SNVs) that change the amino acid sequence in these genes were identified through whole-exome sequencing. Type 2 diabetes is a complex disease significantly affected by rare variants (Am J Hum Genet. 2001;69:124), and as previously described, due to the small sample size of this study, the analysis was limited to uncommon variants (minor allele frequency <5%, 1000 Genomes Phase 1). Meanwhile, it is known that mutations in drug response genes have large racial differences (Genome Med. 2017;9(1):117.). The fact that SU use is particularly high in East Asians (EAS) suggests that East Asian patients have a good SU response (Diabetes Obes Metab. 2023 Jan;25(1):208), so mutations with a higher minor allele frequency in East Asians (EAS) than the global average frequency were selected.

[0072] Next, to select variants densely clustered in SU-dependent patients, we compared the odds ratio (OR) of variants ≥2 with two control groups: East Asian and Korean T2DM patient cohorts (SNUH project, http: / / koex.snu.ac.kr, Experimental & Molecular Medicine (2017) 49, e356). As a result, 94 single nucleotide variants (SNVs) in 70 genes were selected as candidates for SU-dependent-associated variants (Table 2).

[0073]

[0074] Uncommon / Amino Acid Change / East Asian Concentration / SNV Candidates Concentrated in SU-Dependent Patients 1ABCC8:NM_000352:exon12:c.G1678A:p.V560M,51KCNQ1:NM_000218:exon10:c.C1343G:p.P448R,2ACLY:NM_198830:exon18:c.C2093T:p.P698L,ACLY:NM_001096:exon19:c.C2123T:p.P708L,52LBR:NM_002296:exon12:c.G1528A:p.A510T,LBR:NM_194442:exon12:c.G1528A:p.A5 10T,3AKAP5:NM_004857:exon2:c.G871A:p.G291R,53LRP5:NM_002335:exon2:c.C290T:p.A97V,4APOA5:NM_052968:exon4:c.G538C:p.V180L ,APOA5:NM_001166598:exon4:c.G538C:p.V180L,54LRP5:NM_002335:exon9:c.G1871A:p.R624Q,5APOA5:NM_052968:exon4:c.G553T:p.G185C ,APOA5:NM_001166598:exon4:c.G553T:p.G185C,55MARCKS:NM_002356:exon2:c.C821T:p.A274V,6ARAP1:NM_001040118:exon18:c.G2518A: p.E840K,ARAP1:NM_015242:exon16:c.G1783A:p.E595K,ARAP1:NM_001135190:exon15:c.G1600A:p.E534K,56MC4R:NM_005912:exon1:c.G914 A:p.R305Q,7ARAP1:NM_001040118:exon7:c.G883A:p.G295R,ARAP1:NM_015242:exon5:c.G148A:p.G50R,ARAP1:NM_001135190:exon5:c.G14 8A:p.G50R,57MGEA5:NM_012215:exon1:c.G137A:p.G46E,MGEA5:NM_001142434:exon1:c.G137A:p.G46E,8BLK:NM_001715:exon4:c.G252C:p.K84N,58MICU1:NM_001195519:exon3:c.T215G:p.L72R,MICU1:NM_001195518:exon8:c.T809G:p.L270R,9CACNA1A:NM_001127222:exon19:c.C2759G:p.A920G,CACNA1A:NM_001127221:exon19:c.C2762G:p.A921G,59MYO5A:NM_000259:exon20:c.C2491T:p.R831C,MYO5A:NM_001142495:exon20:c.C2491T:p.R831C,10CACNA1D:NM_001128839:exon30:c.C3854T:p.A1285V,CACNA1D:NM_001128840:exon30:c.C3854T:p.A1285V,CACNA1D:NM_000720:exon31:c.C3914T:p.A1305V,60NNT:NM_182977:exon14:c.C1987T:p.L663F,NNT:NM_012343:exon14:c.C1987T:p.L663F,11CACNA1E:NM_001205293:exon20:c.C2731T:p.R911W,CACNA1E:NM_000721:exon20:c.C2731T:p.R911W,CACNA1E:NM_001205294:exon19:c.C2674T:p.R892W,61NNT:NM_182977:exon3:c.A188G:p.K63R,NNT:NM_012343:exon3:c.A188G:p.K63R,12CACNA1H:NM_021098:exon10:c.C2030T:p.S677L,CACNA1H:NM_001005407:exon10:c.C2030T:p.S677L,62NOS2:NM_000625:exon19:c.A2239G:p.T747A,13CAPN10:NM_023083:exon4:c.C598A:p.P200T,CAPN10:NM_023085:exon4:c.C598A:p.P200T,63NR0B2:NM_021969:exon1:c.C100T:p.R34X,14CASR:NM_000388:exon4:c.G1192A:p.D398N,CASR:NM_001178065:exon4:c.G1192A:p.D398N,64OXCT1:NM_000436:exon2:c.C173T:p.T58M,15CASR:NM_000388:exon7:c.G2824A:p.E942K,CASR:NM_001178065:exon7:c.G2854A:p.E952K,65PAM:NM_138822:exon19:c.T2153C:p.F718S,PAM:NM_001177306:exon19:c.T2153C:p.F718S,PAM:NM_000919:exon19:c.T2153C:p.F718S,PAM:NM_138766:exon19:c.T2153C:p.F718S,PAM:NM_138821:exon18:c.T1832C:p.F611S,16CCKAR:NM_000730:exon2:c.A305G:p.N102S,66PAX4:NM_006193:exon1:c.G92A:p.R31Q,17CD38:NM_001775:exon3:c.C418T:p.R140W,67PC:NM_001040716:exon15:c.A1702G:p.T568A,PC:NM_000920:exon14:c.A1702G:p.T568A,PC:NM_022172:exon13:c.A1702G:p.T568A,18DIS3L2:NM_152383:exon13:c.G1448A:p.R483Q,68PCK2:NM_004563:exon9:c.C1379T:p.P460L,19DIS3L2:NM_152383:exon13:c.G1600A:p.G534R,69PDE4C:NM_001098818:exon1:c.C88T:p.L30F,20EIF2AK3:NM_004836:exon12:c.G2014A:p.E672K,70PDE8B:NM_001029853:exon18:c.C2084T:p.T695M,PDE8B:NM_003719:exon19:c.C2144T:p.T715M,PDE8B:NM_001029852:exon18:c.C1979T:p.T660M,PDE8B:NM_001029851:exon16:c.C1853T:p.T618M,PDE8B:NM_001029854:exon18:c.C2003T:p.T668M,21EPHA5:NM_004439:exon17:c.A2876G:p.H959R,EPHA5:NM_182472:exon16:c.A2810G:p.H937R,71PRKCE:NM_005400:exon9:c.G1130C:p.R377P,22FAM3D:NM_138805:exon5:c.C178T:p.P60S,72PSMD9:NM_002813:exon1:c.G31A:p.G11S,23FASN:NM_004104:exon22:c.G3557T:p.C1186F,73RAPGEF3:NM_001098531:exon2:c.C79T:p.R27W,24FASN:NM_004104:exon24:c.C4127T:p.A1376V,74REST:NM_001193508:exon4:c.A2241G:p.I747M,REST:NM_005612:exon4:c.A2241G:p.I747M,25FASN:NM_004104:exon34:c.G5809A:p.V1937I,75RYR2:NM_001035:exon46:c.G7076A:p.R2359Q,26FASN:NM_004104:exon42:c.C7301T:p.T2434I,76RYR2:NM_001035:exon56:c.A8419G:p.I2807V,27FASN:NM_004104:exon42:c.G7192T:p.A2398S,77RYR3:NM_001243996:exon39:c.A6125C:p.N2042T,RYR3:NM_001036:exon39:c.A6125C:p.N2042T,28FOXA2:NM_021784:exon2:c.G274A:p.A92T,FOXA2:NM_153675:exon3:c.G256A:p.A86T,78SCN9A:NM_002977:exon15:c.A2359G:p.M787V,29GAL:NM_015973:exon6:c.C368T:p.S123F,79SIRT4:NM_012240:exon3:c.G703A:p.G235R,30GHSR:NM_198407:exon2:c.G1070A:p.R357Q,80SLC18A2:NM_003054:exon10:c.C899G:p.S300C,31GIPR:NM_000164:exon11:c.G947T:p.R316L,81SLC24A6:NM_024959:exon11:c.C1072T:p.L358F,32GIPR:NM_000164:exon6:c.C406T:p.R136W,82SLC2A2:NM_000340:exon3:c.G301A:p.V101I,33GIPR:NM_000164:exon9:c.C843A:p.Y281X,83THADA:NM_022065:exon11:c.G1326T:p.E442D,THADA:NM_001083953:exon11:c.G1326T:p.E442D,34GLIS3:NM_152629:exon3:c.G951T:p.E317D,GLIS3:NM_001042413:exon4:c.G1416T:p.E472D,84THADA:NM_022065:exon11:c.T1295A:p.V432D,THADA:NM_001083953:exon11:c.T1295A:p.V432D,35GLIS3:NM_152629:exon7:c.G1819A:p.A607T,GLIS3:NM_001042413:exon8:c.G2284A:p.A762T,85THADA:NM_022065:exon20:c.C2992G:p.R998G,THADA:NM_001083953:exon20:c.C2992G:p.R998G,36GLIS3:NM_152629:exon8:c.A2006G:p.H669R,GLIS3:NM_001042413:exon9:c.A2471G:p.H824R,86THADA:NM_022065:exon29:c.C4150G:p.R1384G,THADA:NM_001083953:exon29:c.C4150G:p.R1384G,37GLP1R:NM_002062:exon2:c.G131A:p.R44H,87THADA:NM_022065:exon32:c.C4631G:p.S1544C,THADA:NM_001083953:exon32:c.C4631G:p.S1544C,38GPLD1:NM_001503:exon21:c.T2081C:p.M694T,88THADA:NM_022065:exon9:c.T776C:p.I259T,THADA:NM_001083953:exon9:c.T776C:p.I259T,39HLA-DRB5:NM_002125:exon2:c.C165A:p.F55L,89TRPM2:NM_003307:exon11:c.A1748G:p.N583S,40HNF4A:NM_001030003:exon2:c.C83T:p.A28V,HNF4A:NM_178850:exon2:c.C149T:p.A50V,HNF4A:NM_178849:exon2:c.C149T:p.A50V,HNF4A:NM_000457:exon2:c.C149T:p.A50V,HNF4A:NM_175914:exon2:c.C83T:p.A28V,HNF4A:NM_001030004:exon2:c.C83T:p.A28V,90TRPM2:NM_003307:exon26:c.C3830T:p.T1277M,41HTT:NM_002111:exon12:c.G1652A:p.G551E,91TRPM4:NM_017636:exon4:c.C290T:p.T97M,TRPM4:NM_001195227:exon4:c.C290T:p.T97M,42INHBB:NM_002193:exon1:c.G157A:p.D53N,92TRPM5:NM_014555:exon5:c.G655C:p.G219R,43IRS1:NM_005544:exon1:c.G2164A:p.G722S,93TRPV3:NM_145068:exon5:c.G443A:p.R148Q,44KANK1:NM_015158:exon3:c.C1991T:p.A664V,KANK1:NM_153186:exon2:c.C1517T:p.A506V,94WFS1:NM_001145853:exon2:c.A41G:p.Q14R,WFS1:NM_006005:exon2:c.A41G:p.Q14R,45KANK1:NM_015158:exon3:c.G1196A:p.R399Q,KANK1:NM_153186:exon2:c.G722A:p.R241Q,--46KANK1:NM_015158:exon3:c.G1915A:p.V639I,KANK1:NM_153186:exon2:c.G1441A:p.V481I,--47KCNB2:NM_004770:exon3:c.C2279A:p.T760N,--48KCNH6:NM_030779:exon6:c.G1280A:p.R427Q,--49KCNH6:NM_030779:exon6:c.G1354A :p.D452N,--50KCNJ15:NM_170736:exon3:c.A88C:p.M30L,KCNJ15:NM_002243:exon4:c.A88C:p.M30L,KCNJ15:NM_170737:exon3:c.A88C:p.M30L,--.

[0075]

[0076] Example 1. Genetic testing using WES in prospectively identified SU-dependent patients.

[0077] To validate the candidate variants identified from the retrospective study, we conducted a prospective clinical trial in which patients with SU dependence were selected and genetically tested (IRB No. H 1906 070 1041).

[0078] In Experimental Example 1, approximately 20% of the enrolled patients were calculated to be SU dependent. Therefore, 50 patients were needed to screen 10 SU dependent patients, and considering a 15% dropout rate, a total of 58 patients were planned to be recruited (Fig. 3). The enrollment criteria were type 2 diabetic patients taking low-dose SU (2 mg / day or less of glimepiride equivalent) for more than 6 months and HbA1c ≤7.5%. After obtaining written informed consent from the patients, SU was discontinued and replaced with other oral antidiabetic agents (gliptins or glitazones). Blood glucose and HbA1c were monitored for up to 20 weeks to determine SU dependence. If glycemic control worsened significantly (one of the following: HbA1c increase of ≥1% within 12 weeks, HbA1c increase of ≥1.5% within 20 weeks, or fasting glucose increase of ≥80 mg / dL within 20 weeks), the patient was determined to be SU-dependent and SU intake was restarted. As a result of the study, two patients withdrew consent and were dropped out, 7 patients were SU-dependent, and 43 patients were not. The other 6 patients did not restart SU during the 20-week study period but did restart SU later (mean SU withdrawal period 40 weeks), leaving the dependence ambiguous (Figure 3). The glycemic profiles of patients with prospectively confirmed SU dependence are presented in Figure 4 , and their clinical characteristics did not differ statistically significantly, as in the retrospective study (Table 3 ).

[0079] Genetic information was obtained by performing exome sequencing on seven prospectively proven SU-dependent patients (Macrogen, Korea; see Experimental Example 2).

[0080]

[0081] Clinical characteristics of SU-dependent patients identified in a prospective clinical study

[0082]

[0083]

[0084] Example 2. Verification of candidate mutations_Recurrent mutations

[0085] Among the 94 candidate mutations in Table 2, a total of 9 mutations were reproduced in the prospectively identified SU-dependent patients in Example 1 (serial numbers 13, 23, 25, 30, 47, 62, 85, 86, 88) and are listed in Table 4. Considering the low frequency of these mutations found in the KCNB2, GHSR, NOS2, CAPN10, THADA, and FASN genes, the fact that they were repeatedly observed in a small number of SU-dependent patients suggests that these mutations may be associated with these clinical features.

[0086]

[0087] Information on candidate mutations associated with SU dependence

[0088]

[0089] B, start; D, deleterious; EAS, East Asian; ND, not detected; PB, probably damaging; PS, possibly damaging; rsID, variant identification; SU, sulfonylureas; T, tolerated; T2D, type 2 diabetes

[0090] allele frequency and functional prediction source: https: / gnomad.broadinstitute.org, genomeAD exomes v2.1.1;http: / koex.snu.ac.kr / koex_main.php?section=search(Experimental & Molecular Medicine (2017) 49, e356)

[0091]

[0092] Example 3. Verification of candidate genes_multiple mutations

[0093] Most of the candidate genes in Table 2 derived from retrospectively recruited patients contained only one candidate mutation, whereas multiple mutations were observed in THADA, FASN, GIPR, KCNH6, and DIS3L2. When the filtering process of Experimental Example 3 was performed on the exome data of prospectively recruited patients, additional mutations were found in some of the genes (1 in FASN, 62 in KCNH, 21 in DIS3L, Table 4). In the case of the NR0B2 gene, although only one mutation was derived retrospectively, additional mutations were observed in prospectively recruited patients, and all of these are rare mutations with an incidence of less than 0.1% in East Asians and are predicted to have a high risk of affecting protein function (PB, D, stop-gain, Table 4). That is, in THADA, FASN, GIPR, KCNH6, DIS3L2, and NR0B2, despite their very low mutation frequencies (mostly less than 1% in East Asians), there are mutations observed in a large number of SU-dependent cohorts, and some of these mutations have a very high risk of affecting protein function, suggesting that these genes and their mutations may be associated with SU dependence.

[0094]

[0095] Example 4. Validation of SU drug effects according to candidate gene mutations in a large population.

[0096] Next, to analyze whether the derived candidate variants and candidate genes were associated with SU response in a large population, we used the UK Biobank data. The UK Biobank is a community cohort in the UK that enrolled approximately 500,000 adults between 2006 and 2010 (Nucleic acids research 47.D1 (2019): D1005-D1012). Of these, 23,104 patients were coded as type 2 diabetes. To exclude type 1 diabetes or youth-onset diabetes with an atypical course, patients with an onset age of less than 30 years were excluded, and a total of 1,991 patients were excluded, including those without genetic information. In addition, renal function decline (GFR 40 ml / min / 1.73 m), which significantly affects diabetes and drug metabolism, was excluded. 2 After excluding 1,213 patients with accompanying (less than 1%), 19,900 patients were ultimately used in the analysis (Fig. 5), and their clinical characteristics are shown in Table 5.

[0097]

[0098] Clinical characteristics of patients analyzed in the UK biobank

[0099]

[0100] BMI, body mass index; eGFR, estimated glomerular filtration rate

[0101] Mean ± SD or median [interquartile range]

[0102] P, Student's t-test between the participants divided by DM duration

[0103]

[0104] At the time of UK Biobank registration, the three most common diabetes medications in Europe were metformin, sulfonylureas, and insulin. Therefore, we expected that patients who responded well to sulfonylureas would have lower insulin use rates. Therefore, we aimed to compare the insulin use rates of patients harboring the candidate variants with those who did not harbor the candidate variants.

[0105] However, in Experimental Example 3, the candidate mutation derivation process selected mutations specific to East Asians. Therefore, among the 19,900 individuals in the UK Biobank, which is primarily Caucasian, only 148 individuals harbored the 28 candidate mutations in Table 4. Furthermore, we anticipated that there would be mutations that were not yet selected in the candidate mutation derivation targeting a small number of Korean patients. Therefore, we added mutations predicted to affect protein function from the 10 genes in Table 4 from the publicly available SNV database dbNSFP (Genome Medicine. (2020) 12:103). The criteria for selecting additional variants were as follows: 1) if the variant frequency (global allele frequency) is less than 5% and the in silico prediction using Polyphen or SIFT predicts a change in protein function, 2) for the gene FASN: a variant with a frequency of less than 5% in a specific domain (PF00975, PF08659, PF08242: Pfam), and 3) for the gene KCNH6: a variant with a frequency of less than 5% in a specific domain (PF00027, PF00520: Pfam). Additionally, pLoF (predicted loss of function) variants were selected using the gnomAD browser, which include not only coding region SNVs but also multiple nucleotide variants in nearby locations, causing the following changes: transcript_ablation, splice_acceptor_variant, splice_donor_variant, stop_gained, and frameshift_variant (Fig. 6).

[0106] Among the selected mutations, a total of 3,930 mutations were discovered in the UK Biobank. Patients with at least one selected mutation were assigned to the test group, and other patients were assigned to the control group. Analysis was performed by gene. As a result, the FASN, NOS2, and CAPN10 genes were excluded because the insulin use rate did not decrease in mutation carriers (Tables 6-15), and an integrated analysis was performed on the remaining 7 genes.

[0107]

[0108] Characteristics of HbA1c and drug use according to the presence of selected variants in 10 candidate genes (CAPN10, DIS3L2, FASN, GHSR, GIPR, KCNB2, KCNH6, NOS2, NR0B2, THADA)

[0109]

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120] BMI, body mass index; eGFR, estimated glomerular filtration rate; MTF, metformin; SU, sulfonylurea; PSM, propensity score matching

[0121] Mean ± SD

[0122] P, Student's t-test or chi-squared test between the participants with and without the variants

[0123]

[0124] The study group, which had at least one selected mutation in one of the seven genes, consisted of 4,857 subjects, and their clinical characteristics are shown in Table 16. Compared to the control group, the subjects were older, had lower age at diagnosis, had lower renal function, had fewer males, and had poorer glycemic control.

[0125]

[0126] Clinical characteristics according to the presence of selected mutations in seven candidate genes (THADA, GIPR, KCNH6, KCNB2, DIS3L2, GHSR, NR0B2)

[0127]

[0128] BMI, body mass index; eGFR, estimated glomerular filtration rate

[0129] Mean ± SD

[0130]

[0131] These variant carriers were compared with 9,610 controls matched 1:2 for age, sex, body mass index (BMI), disease duration, and estimated glomerular filtration rate (eGFR) (Table 17). Compared to the controls, variant carriers had significantly worse glycemic control (HbA1c), and thus significantly higher rates of metformin (MTF) use as a first-line drug and a tendency toward higher rates of sulfonylurea (SU) use (p=0.0993). However, insulin use was significantly lower (p=0.0233). In other words, it can be interpreted that variant carriers responded well to SU, resulting in better glycemic control and thus slower insulin prescription.

[0132] Meanwhile, the prescription rate of SU, a second-line drug, increases in patients with a longer disease duration, and it can be inferred that as the disease duration increases, feedback on drug effects is reflected, and the prescription pattern is tailored to genetic susceptibility. Therefore, if mutation carriers have a good glycemic response to SU, as the disease duration increases, the rate of SU use will increase, leading to improved glycemic control and a significant reduction in insulin use. When limited to patients with a disease duration of 72 months or longer, there were 1,673 mutation carriers, and the rate of SU use was over 40% (Table 8). When compared with the 3,346 control subjects matched to the PSM as described above, the rate of insulin use was significantly lower (p=0.0191, Table 17), despite a one-third reduction in the number of patients compared to the entire patient population. However, there were no differences in glycemic control or use of other medications. That is, it can be interpreted that mutation carriers responded well to SU and had good blood sugar control, so not only was insulin prescription slowed, but significant hyperglycemia observed in the early stage of diabetes (less than 72 months of disease) also disappeared.

[0133]

[0134] Characteristics of HbA1c and drug use according to the presence of selected variants in seven candidate genes (THADA, GIPR, KCNH6, KCNB2, DIS3L2, GHSR, NR0B2)

[0135]

[0136] BMI, body mass index; eGFR, estimated glomerular filtration rate; MTF, metformin; SU, sulfonylurea; PSM, propensity score matching

[0137] Mean ± SD

[0138]

[0139] To summarize the results of Example 4, using the UK Biobank data, we analyzed the characteristics of carriers of selected variants in seven candidate genes (THADA, GIPR, KCNH6, KCNB2, DIS3L2, GHSR, NR0B2). As a result, they were younger and had fewer males. When other clinical factors were adjusted, they showed more use of metformin and sulfonylureas due to poor glycemic control. However, they used less insulin when sulfonylureas failed. This was more pronounced in the later stages when sulfonylurea use increased, and at the same time, hyperglycemia observed in the early stage of the disease disappeared. This suggests that carriers of the variants respond well to sulfonylureas.

[0140]

[0141] Example 5. Validation of candidate genes: Rare mutations with a high probability of altering protein function.

[0142] Among the 28 candidate mutations in Table 4, one stop-gained variant was found in each of GIPR, KCNH6, and NR0B2. It is well known that the interruption of peptide translation due to stop-gained variants suppresses mRNA transcription of the corresponding gene through a mechanism called nonsense-mediated decay (NMD) (J Biol Chem. 2022 Nov;298(11):102592). In other words, these mutations theoretically suppress the expression of GIPR, KCNH6, and NR0B2, respectively. In particular, the NR0B2rs74315349 (G>A) mutation causes a premature stop at the 34th amino acid position of a 207-amino acid peptide, and thus, it is expected that the NR0B2 protein expression level will be minimal. Although the mutation was not reproduced in Example 1, which prospectively demonstrated SU dependence, the rs150546920 (C>G) mutation was additionally observed in NR0B2, and the in silico prediction model predicted that this mutation also has a high probability of affecting protein function (Table 4).

[0143] Nuclear receptor subfamily 0 group B member 2 (commonly referred to as small heterodimer partner, SHP), a protein expressed in NR0B2, is an orphan nuclear receptor that binds to and influences various proteins. Among them, it is well known that it reacts with the transcription factors HNF1 and HNF4 to inhibit their activity. Mutations in HNF1A and HNF4A cause maturity onset diabetes of the young (MODY) (MODY-1, MODY-3), in which case the response to sulfonylureas is prominent, so SU is used as the first-line drug. In insulin-secreting cells, mutations in HNF1A and HNF4A or their suppression of expression decrease cellular glucose sensing, glycolysis, and ATP content (EMBO J. 1998;17(22):6701-13; Diabetes. 2022;71(4):862). Conversely, treatment of hepatocytes with N-trans-caffeoyltyramine (NCT), a HNF4A agonist, enhanced mitochondrial function, the main ATP producer (Cell Death Dis. 2022;13(1):89). Since the rs74315349 (G>A) mutation in NR0B2 in insulin-secreting cells causes a loss of inhibitory activity against HNF4A (Proc Natl Acad Sci US A. 2001;98(2):575), it can be inferred that the resulting enhanced HNF4A activity will increase the insulin secretory effect of ATP-dependent sulfonylureas through increased mitochondrial function and ATP (Diabetes. 2013;62(11):3909). In fact, patients with the rs74315349 (G>A) mutation in NR0B2 have been reported to have increased insulin secretion (Proc Natl Acad Sci US A. 2001;98(2):575). The above is diagrammed in Figure 7.

[0144] In summary of Example 5, considering previous reports that functional loss of NR0B2 protein enhances the activity of transcription factors involved in ATP production, it is possible that the NR0B2 functional loss mutations found in a small number of SU-dependent patients with a frequency of less than 0.1% may increase the ATP-dependent SU response.

[0145]

[0146] In summary, a number of candidate genes and candidate mutations were derived from retrospectively screened SU-dependent patients (Fig. 1, Fig. 2, Table 2). In addition, some uncommon mutations were reproduced or added in 7 patients with prospectively confirmed SU dependence, resulting in the screening of 28 mutations in 10 genes (Example 1: Fig. 3, Fig. 4, Table 4). Considering the frequency and function of these mutations (Table 4, Fig. 7), they suggest a possible association with SU dependence.

[0147] In addition, when variants predicted to change protein function in 7 genes (THADA, GIPR, KCNH6, KCNB2, DIS3L2, GHSR, NR0B2) among these were selected as candidates and analyzed with data from a large cohort, the UK Biobank, it was found that variant carriers had a low frequency of insulin use despite poor glycemic control, suggesting a good response to SU, a drug used immediately before insulin. When the disease duration was long, the genetic sensitivity to the drug was reflected in the prescription pattern and the use of SU increased, it was observed that the poor glycemic control of the variant carriers became similar to that of the control group, and the insulin use rate was still low (Example 4: Figs. 5, 6, Table 17).

[0148]

[0149] Pharmacogenetics for T2DM is not currently applied clinically, but it is a promising area for subgroups such as patients with SU dependence. This study identified novel nucleotide variants associated with SU responsiveness in type 2 diabetes, which may contribute to predicting SU response through genetic analysis.

Claims

1. A composition for predicting sulfonylurea dependence in type 2 diabetes patients, comprising a preparation that identifies rs74315349 (G>A) mutation of NR0B2 (Nuclear Receptor Subfamily 0 Group B Member 2).

2. A composition for predicting sulfonylurea dependence in type 2 diabetes patients, wherein the preparation is a primer or probe that specifically binds to the gene according to claim 1.

3. A method for providing information for predicting sulfonylurea dependence in a type 2 diabetes patient, comprising the step of identifying a mutation of rs74315349 (G>A) in NR0B2 (Nuclear Receptor Subfamily 0 Group B Member 2) in a biological sample isolated from the subject.

4. A method for providing information for predicting sulfonylurea dependence in type 2 diabetes patients, wherein the confirmation is performed by adding a primer or probe that specifically binds to the gene and confirming its sequence.

5. A method for providing information for predicting sulfonylurea dependence in a type 2 diabetes patient, further comprising the step of providing information that an individual having the mutation has sulfonylurea dependence in claim 3.

Images