Vol 73, No 5 (2022)
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Genome-wide association study of fasting proinsulin, fasting insulin, 2-hour postprandial proinsulin, and 2-hour postprandial insulin in Chinese Han people

Leweihua Lin1, Huibiao Quan2, Tuanyu Fang1, Lu Lin1, Qianying Ou1, Huachuan Zhang3, Kaining Chen1, Zhiguang Zhou2
Pubmed: 35971929
Endokrynol Pol 2022;73(5):856-862.

Abstract

Introduction: Fasting proinsulin (FPI) and fasting insulin (FI) have been demonstrated to be associated with impaired b cell function, T2DM, and insulin resistance. This genome-wide association study (GWAS) was performed to contribute to our understanding of the genetic basis of FPI, FI, 2-hour postprandial proinsulin (2hPI), and 2-hour postprandial insulin (2hI) of the pathophysiology of prediabetes in the Chinese population.

Material and methods: The levels of fasting plasma glucose (FPG), FPI, FI, 2hPI, and 2hI were examined by an automatic biochemical analyser. The Applied BiosystemsTM AxiomTM Precision Medicine Diversity Array, the Gene Titan Multi-Channel instrument, and Axiom Analysis Suite 6.0 Software were used for genotyping. Imputation was performed with IMPUTE 2.0 software from HapMap, 1000 Genomes Phase 3 as a reference panel.

Results: Six single nucleotide polymorphisms (SNPs) in DLG1-AS1, SORCS1, and CTAGE11P for FPI, and 27 SNPs in ZNF718, MARCHF2, and HNRNPM for 2hPI reached genome-wide significance. Genome-wide significance was reached for associations of 6 SNPs in KRT71 to FI. Also, 14 SNPs in UBE2U, ABO, and GRID1-AS1 were genome-wide significant in their relationship with 2hI. Among these, the genetic loci of CTAGE11P, MARCHF2, KRT71, and ABO have the strongest association with FPI, 2hPI, FI, and 2hI.

Conclusions: The genetic variants of CTAGE11P, MARCHF2, KRT71, and ABO are significantly correlated with FPI, 2hPI, FI, and 2hI, respectively, in Chinese Han people. These genetic variants may serve as new biomarkers for the prevention of prediabetes.

Original paper

Endokrynologia Polska

DOI: 10.5603/EP.a2022.0054

ISSN 0423–104X, e-ISSN 2299–8306

Volume/Tom 73; Number/Numer 5/2022

Submitted: 13.03.2022

Accepted: 28.04.2022

Early publication date: 28.07.2022

Genome-wide association study of fasting proinsulin, fasting insulin, 2-hour postprandial proinsulin, and 2-hour postprandial insulin in Chinese Han people

Leweihua Lin1Huibiao Quan1Tuanyu Fang1Lu Lin1Qianying Ou1Huachuan Zhang2Kaining Chen1Zhiguang Zhou3
1Department of Endocrinology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
2Laboratory of Endocrine, Hainan General Hospital, Haikou, Hainan Province, China
3Department of Endosecretory Metabolic Diseases, The Second Xiangya Hospital of Central South University, Changsha, Hunan Province, China

Huibiao Quan, #19 Xiuhua Road, Xiuying District, Haikou, Hainan Province, 570311, China, tel: +86 13876078153; e-mail: qhb13876078153@hainmc.edu.cn

This article is available in open access under Creative Common Attribution-Non-Commercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0) license, allowing to download articles and share them with others as long as they credit the authors and the publisher, but without permission to change them in any way or use them commercially

Abstract
Introduction: Fasting proinsulin (FPI) and fasting insulin (FI) have been demonstrated to be associated with impaired b-cell function, T2DM, and insulin resistance. This genome-wide association study (GWAS) was performed to contribute to our understanding of the genetic basis of FPI, FI, 2-hour postprandial proinsulin (2hPI), and 2-hour postprandial insulin (2hI) of the pathophysiology of prediabetes in the Chinese population.
Material and methods: The levels of fasting plasma glucose (FPG), FPI, FI, 2hPI, and 2hI were examined by an automatic biochemical analyser. The Applied BiosystemsTM AxiomTM Precision Medicine Diversity Array, the Gene Titan Multi-Channel instrument, and Axiom Analysis Suite 6.0 Software were used for genotyping. Imputation was performed with IMPUTE 2.0 software from HapMap, 1000 Genomes Phase 3 as a reference panel.
Results: Six single nucleotide polymorphisms (SNPs) in DLG1-AS1, SORCS1, and CTAGE11P for FPI, and 27 SNPs in ZNF718, MARCHF2, and HNRNPM for 2hPI reached genome-wide significance. Genome-wide significance was reached for associations of 6 SNPs in KRT71 to FI. Also, 14 SNPs in UBE2U, ABO, and GRID1-AS1 were genome-wide significant in their relationship with 2hI. Among these, the genetic loci of CTAGE11P, MARCHF2, KRT71, and ABO have the strongest association with FPI, 2hPI, FI, and 2hI.
Conclusions: The genetic variants of CTAGE11P, MARCHF2, KRT71, and ABO are significantly correlated with FPI, 2hPI, FI, and 2hI, respectively, in Chinese Han people. These genetic variants may serve as new biomarkers for the prevention of prediabetes. (Endokrynol Pol 2022; 73 (5): 856–862)
Key words: GWAS; fasting proinsulin (FPI); fasting insulin (FI); 2hPI; 2hI

Introduction

Diabetes mellitus (DM) is a chronic metabolic disease caused by abnormal glucose metabolism, which is mainly characterized by hyperglycaemia. There are approximately 382 million people affected with DM worldwide, and type 2 diabetes mellitus (T2DM) accounts for 90% of DM patients [1, 2]. Prediabetes refers to blood glucose levels above normal but below diabetes thresholds. The prevalence of prediabetes is increasing worldwide, and it is estimated that 470 million people will suffer from prediabetes in 2030 [3]. In China, it is reported that the overall prevalence of diabetes is 10.9% and that for prediabetes it is 35.7% [4]. It has been demonstrated that prediabetes is a high-risk state of diabetes, and it also increases the risk of myocardial infarction, stroke, and cardiovascular death [5]. There is accumulating evidence to demonstrate that prediabetes could cause damage to the kidneys and the nervous system [6, 7]. Additionally, prediabetes imposes a huge economic burden on individuals and society [8]. Therefore, effective prevention strategies for prediabetes are increasingly important.

Proinsulin (PI), the precursor form of insulin (I), is synthesized and secreted in pancreatic b-cells. PI only accounts for 10–20% of fasting insulin (FI) under physiological conditions. However, some research indicates that the level of PI was highly expressed in glucose-intolerant and insulin-resistant individuals [9, 10]. Also, fasting proinsulin (FPI) has been demonstrated to be associated with impaired b-cell function, T2DM and insulin resistance, and it could be used as a specific predictor of T2DM [10, 11]. Insulin is a well-known hormone to reduce the level of blood glucose via the stimulation of glucose uptake into muscle cells and adipocytes, etc. by binding to its receptor in the target cells. It has been shown that elevated fasting insulin (FI) is a hallmark of T2DM [12]. Our previous study demonstrated that FPI, 2-hour postprandial proinsulin (2hPI), FI, and 2-hour postprandial insulin (2hI) were associated with an increased risk of prediabetes [13]. Despite these findings, it is still unclear how these common phenotypes affect T2DM.

In this study, we performed a genome-wide association study (GWAS) of FPI, 2hPI, FI, and 2hI in 451 prediabetes subjects from the Chinese Han population. The BiosystemsTM AxiomTM Precision Medicine Diversity Array (PMDA) was used to identify single nucleotide polymorphisms (SNPs) associated with FPI, 2hPI, FI, and 2hI. Our study will provide an effective diagnostic method for early screening of people who are susceptible to T2DM, and for controlling and preventing the development of prediabetes to T2DM.

Material and methods

Participants

In this study, we recruited 451 prediabetes subjects aged ≥ 18 years from the Hainan Affiliated Hospital of Hainan Medical University. Participants with 100 mg/dL (5.6 mmol/L) ≤ fasting plasma glucose < 125 mg/dL (6.9 mmol/L) or 5.7% ≤ glycated haemoglobin (HbA1c) < 6.4% were defined as prediabetes [14]. Individuals without a history of diabetes and malignant tumours, or severe liver and kidney diseases were included in this research. This study was conducted with ethical approval from the Hainan Affiliated Hospital of Hainan Medical University Ethics Committees, and was performed in line with the Declaration of Helsinki. We also obtained consent forms signed by each participant.

Metabolic variables

Fasting blood samples were collected from all subjects after an overnight fast. The levels of fasting plasma glucose (FPG), FPI, FI, 2hPI, and 2hI were examined by an automatic biochemical analyser.

Genotyping and imputation

Genomic DNA was isolated from a whole blood sample using a DNA Extraction Kit (GoldMag Co. Ltd., Xi’an, China), as described previously [15]. The Applied BiosystemsTM Axiom TM Precision Medicine Diversity Array (PMDA, Thermo Scientific, USA), the Gene Titan Multi-Channel instrument, and Axiom Analysis Suite 6.0 Software were used for genotyping.

Genotype data in subjects was cleaned using standard thresholds (HWE p > 5 × 10-6, call rate > 95%). Imputation for chromosomes 1 to 22 was performed with IMPUTE 2.0 software from HapMap 1000 Genomes Phase 3 as a reference panel.

Statistical analyses

The association analysis was conducted using Gold Helix SNP and Variation Suite 8.7 software. The association between SNPs and FPI, 2hPI, FI, and 2hI was evaluated using linear regression assuming an additive genetic model. The 4 traits were analysed with adjustments for age and sex. A p < 5.0 × 10-6 was used as the threshold of genome-wide significance.

Results

A total of 451 prediabetes individuals aged18 years (216 men and 235 women) were included and genotyped in the present study. The average age of the subjects was 51.78 ± 14.49 years. The clinical parameters of participants are summarized in Table 1.

Table 1. Participants characteristics

Variable

Subjects

Number of individuals

451

Age (years, mean ± SD)

51.78 ± 14.49

Gender

Male

216 (47.9%)

Female

235 (52.1%)

FPG [mmol/L]

5.88 ± 1.42

FPI [mU/L]

15.74 ± 12.18

2hPI [mU/L]

63.42 ± 44.10

FI [mU/L]

72.10 ± 43.08

2hI [mU/L]

578.22 ± 435.40

As presented in Table 2, we found that 6 loci in 3 genes (DLG1-AS1, SORCS1, CTAGE11P) reached genome-wide significance associated with FPI, and 27 SNPs in 3 genes (ZNF718, MARCHF2, and HNRNPM) were associated with 2hPI. In addition, the correlation of 6 SNPs in the KRT71 gene with FI reached genome-wide significance. Also, 14 SNPs in 3 genes (UBE2U, ABO, and GRID1-AS1) were genome-wide significant in their relationship with 2hI. The distributions of association p-values for FPI, 2hPI, FI, and 2hI are presented in Figure 1 (the quantile-quantile plots and Locus zoom are shown in Fig. S1 and Fig. S2). Among these, the genetic loci of CTAGE11P, MARCHF2, KRT71, and ABO have the strongest association with FPI, 2hPI, FI, and 2hI, respectively.

Table 2. Significant loci associated with fasting proinsulin (FPI), 2h proinsulin (2hPI), fasting insulin (FI), and 2h insulin (2hI) in study populations

Gene

Traits

Description

SNP

Chr

Position

Allele

Minor allele

MAF

p

DLG1-AS1;LINC02012

FPI

DLG1 antisense RNA1

DLG1 antisense RNA1

rs78022276

3

197482798

T/C

T

0.029

2.88E-06

DLG1-AS1;LINC02012

FPI

rs78750477

3

197483030

C/G

C

0.029

2.88E-06

SORCS1

FPI

Sortilin related VPS10 domain containing receptor

rs58879794

10

106889263

C/A

C

0.150

3.94E-06

CTAGE11P

FPI

CTAGE family member 11, preudogene

rs9600432

13

75107716

A/C

A

0.453

1.42E-06

CTAGE11P

FPI

rs9565135

13

75122830

A/G

A

0.454

3.75E-06

CTAGE11P

FPI

rs9543852

13

75124005

T/G

T

0.454

2.51E-06

ZNF718

2hPI

Zinc finger protein 718

rs56128594

4

188333

T/C

T

0.446

5.88E-08

ZNF718

2hPI

rs4690234

4

192200

T/C

T

0.446

2.41E-08

MARCHF2

2hPI

Membrane associated ring-CH-type finger 2

rs12979798

19

8419748

G/A

G

0.395

4.05E-07

MARCHF2

2hPI

rs12978137

19

8420144

C/T

C

0.405

2.02E-08

MARCHF2

2hPI

rs62117527

19

8421448

C/T

C

0.405

2.02E-08

MARCHF2

2hPI

rs11259979

19

8435167

C/T

C

0.444

8.99E-07

MARCHF2

2hPI

rs12975669

19

8435935

T/G

T

0.445

1.20E-06

MARCHF2

2hPI

rs35562870

19

8436208

C/T

C

0.445

8.84E-07

HNRNPM

2hPI

Heterogeneous nuclear ribonuclaoprotein M

rs17160491

19

8448056

T/G

T

0.469

9.47E-07

HNRNPM

2hPI

rs2081197

19

8448452

A/C

A

0.439

7.83E-07

HNRNPM

2hPI

rs11666117

19

8449010

A/C

A

0.441

6.15E-07

HNRNPM

2hPI

rs11259983

19

8450491

A/C

A

0.447

4.20E-07

HNRNPM

2hPI

rs868781681

19

8450732

T/A

T

0.460

2.17E-06

HNRNPM

2hPI

rs200358539

19

8450735

T/A

T

0.459

1.97E-06

HNRNPM

2hPI

rs17160495

19

8451394

A/T

A

0.447

4.20E-07

HNRNPM

2hPI

rs11259985

19

8451793

A/T

A

0.447

4.20E-07

HNRNPM

2hPI

rs34337793

19

8454339

A/G

A

0.447

3.61E-07

HNRNPM

2hPI

rs34244685

19

8458122

T/C

T

0.441

6.40E-07

HNRNPM

2hPI

rs3764570

19

8463393

A/G

A

0.446

3.38E-07

HNRNPM

2hPI

rs3794997

19

8465003

A/T

A

0.446

3.38E-07

HNRNPM

2hPI

rs34445564

19

8468214

A/T

A

0.439

6.01E-07

HNRNPM

2hPI

Heterogeneous nuclear ribonuclaoprotein M

rs17159302

19

8469632

A/C

A

0.439

6.01E-07

HNRNPM

2hPI

rs17159303

19

8469677

G/A

G

0.446

3.38E-07

HNRNPM

2hPI

rs74180130

19

8479935

C/T

C

0.452

1.19E-06

HNRNPM

2hPI

rs17160520

19

8483705

G/A

G

0.453

1.01E-06

HNRNPM

2hPI

rs2277987

19

8487389

A/G

A

0.440

5.76E-08

HNRNPM

2hPI

rs1599870

19

8488516

G/A

G

0.440

6.62E-08

KRT71

FI

Keratin 71

rs12308719

12

52548451

G/T

G

0.482

2.31E-06

KRT71

FI

rs10876309

12

52548517

C/T

C

0.482

2.31E-06

KRT71

FI

rs3803084

12

52548843

A/G

A

0.495

1.02E-06

KRT71

FI

rs3803085

12

52548910

C/T

C

0.483

1.08E-06

KRT71

FI

rs4761930

12

52549360

G/A

G

0.487

1.52E-06

KRT71

FI

rs4761933

12

52555091

C/T

C

0.491

2.12E-06

UBE2U

2hI

Ubiquitin conjugating enzyme E2 U

rs11585260

1

64315831

G/C

G

0.024

4.94E-06

UBE2U

2hI

rs11577590

1

64315842

C/G

C

0.024

4.94E-06

ABO

2hI

Alpha 1-3-N-acetylgalactosaminyltransferase and alpha 1-3-galactosaminyltransferase

rs9411372

9

133258677

A/G

A

0.138

9.59E-07

ABO

2hI

rs977371848

9

133266456

T/C

T

0.163

1.56E-07

ABO

2hI

rs879055593

9

133271182

T/C

T

0.163

1.56E-07

ABO

2hI

rs992108547

9

133273983

A/G

A

0.163

1.56E-07

ABO

2hI

rs947073006

9

133274414

A/G

A

0.163

1.56E-07

ABO

2hI

rs600038

9

133276354

C/T

C

0.159

7.95E-07

ABO

2hI

rs651007

9

133278431

T/C

T

0.159

3.84E-07

ABO

2hI

rs579459

9

133278724

C/T

C

0.159

3.84E-07

ABO

2hI

rs495828

9

133279294

T/G

T

0.159

3.84E-07

ABO

2hI

rs635634

9

133279427

T/C

T

0.159

3.84E-07

LINC01520;GRID1-AS1

2hI

GRID1 antisense RNA1

rs375709957

10

85558056

T/A

T

0.176

1.87E-06

LINC01520;GRID1-AS1

2hI

rs77136415

10

85558059

T/C

T

0.176

1.87E-06

212388.png
Figure. 1 Manhattan plot for loci associated with fasting proinsulin (FPI) (A), 2-hour postprandial proinsulin (2hPI) (B), fasting insulin (FI) (C), and 2-hour postprandial insulin (2hI) (D)

Discussion

The current study illustrated that the genetic variants of CTAGE11P, MARCHF2, KRT71, and ABO were significantly correlated with FPI, 2hPI, FI, and 2hI in Chinese Han people, respectively. Our research will provide scientific methods and ideas for the prevention and diagnosis of prediabetes, and it will contribute to controlling and reducing the progression of prediabetes to T2DM.

Recently, GWAS was performed by Strawbridge et al., which found that 9 SNPs in 8 genes were associated with FPI levels in the European population [16]. Subsequently, Huyghe et al. also identified low-frequency coding variants associated with FPI at SGM2 and MADD gene in Finnish males [17]. Moreover, it is suggested that IGF-1 genetic variants were associated with FI in European ancestry [18]. Manning et al. also observed that 6 SNPs in COBLL1-GRB14, IRS1, PPP1R3B, PDGFC, UHRF1BP1, and LYPLAL1 are correlated with the FI level [19]. However, those SNPs explained only a small percentage of the total variation in FPI and FI. In the present study, we found 6 SNPs in DLG1-AS1, SORCS1, and CTAGE11P for FPI, 27 SNPs in ZNF718, MARCHF2, and HNRNPM for 2hPI, 6 SNPs in KRT71 for FI, and 14 SNPs in UBE2U, ABO, and GRID1-AS1 for 2hI. Among these, the genetic variants of CTAGE11P, MARCHF2, KRT71, and ABO have the strongest association with FPI, 2hPI, FI, and 2hI.

The E3 ubiquitin ligase membrane-associated ring-CH-type finger 2 (MARCHF2) is a member of the membrane-associated RING-CH E3 ubiquitin ligase family (MARCH) and localizes to the endoplasmic reticulum and Golgi [20]. The known substrate of MARCHF2 includes cystic fibrosis transmembrane conductance regulator (CFTR) [21]. Some studies have indicated that patients with CFTR gene variants show an insufficiency of insulin secretion, leading to the development of DM [22, 23]. Moreover, Khan et al. found that inhibition of CFTR decreased the concentrations of plasma insulin and pancreatic insulin in CFTR-inhibited animals [24]. Another study demonstrated that the mutation of CFTR is associated with insulin resistance and decreased b-cell mass in mice [25]. This evidence led us to believe that MARCHF2 is involved in the development of pancreas and DM by interacting with CFTR.

Keratin 71 (KRT71) is a member of the keratin family and is located on chromosome 12q13.13. Keratin constitutes the intermediate filament proteins of epithelial cells. It is documented that the loss of keratin 8 decreased fasting blood glucose levels, and increased glucose uptake and glycogen synthesis [26, 27]. The abnormal expression of keratin 1 and 10 reduced insulin secretion, thus leading to the development of DM [28].

The ABO gene encodes glycosyltransferases that catalyse the transfer of nucleotide donor sugars to the H antigen to form the A and B antigens. Variation in the ABO gene is the basis of the ABO blood group. Meo et al. found that blood group “B” is associated with a higher risk of T2DM, while blood group “O” has a weak correlation with T2DM [29]. Also, a GWAS reported that ABO variants are associated with increased levels of plasma lipid and soluble intercellular adhesion molecule 1 and tumour necrosis factor 2 (TNF-2). These molecules could affect insulin and its receptors and contribute to the development of DM [30].

CTAGE family member 11 pseudogene (CTAGE11P) belongs to the cutaneous T-cell lymphoma-associated antigen (CTAGE) family and is located on 13q22.2. It is reported that the mutation of family members reduces cholesterol and triglyceride levels in mice [31]. Another family member can regulate the plasma low-density lipoprotein-cholesterol concentration and is associated with coronary artery disease [32]. Our study found for the first time that CTAGE11P genetic variants are associated with FPI in the Chinese people.

Conclusions

We found that the genetic variants of CTAGE11P, MARCHF2, KRT71, and ABO are significantly correlated with FPI, 2hPI, FI, and 2hI in Chinese Han people, respectively. These genetic variants may serve as new biomarkers for the prevention of prediabetes.

Conflict of interest

All authors declare that they have no competing interests.

Funding

This study received the support of the major research and development program of Hainan Province (no. ZDYF2021SHFZ078), and received the support of project supported by Hainan Province Clinical Medical Center.

Acknowledgments

The authors thank all participants and volunteers in this study. We also thank the Hainan Affiliated Hospital of Hainan Medical University for their help with sample collections.

Data availability statement

The data that support the findings of this study are available from the supporting information files of this manuscript.

Ethical approval

This study was conducted with ethical approval from the Hainan Affiliated Hospital of Hainan Medical University Ethics Committees, and it was performed in line with the Declaration of Helsinki. We also obtained consent forms signed by each participant.

Consent to participate

Not applicable.

Code availability

Not applicable.

Authors’ contributions

Le.L. and H.Q. designed this study protocol and drafted the manuscript; T.F. and Lu.L. performed the DNA extraction and genotyping; Q.O. performed the data analysis; H.Z. performed the sample collection and information recording; K.C. and Z.Z. revised the manuscript; H.Q. conceived and supervised the study. All authors read and approved the final manuscript.

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