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KCNJ11, ABCC8 and TCF7L2 polymorphisms and the response to sulfonylurea treatment in patients with type 2 diabetes: a bioinformatics assessment

BMC Medical GeneticsBMC series – open, inclusive and trusted201718:64

https://doi.org/10.1186/s12881-017-0422-7

Received: 16 June 2016

Accepted: 11 May 2017

Published: 6 June 2017

Abstract

Background

Type 2 diabetes (T2D) is a worldwide epidemic with considerable health and economic consequences. Sulfonylureas are widely used drugs for the treatment of patients with T2D. KCNJ11 and ABCC8 encode the Kir6.2 (pore-forming subunit) and SUR1 (regulatory subunit that binds to sulfonylurea) of pancreatic β cell KATP channel respectively with a critical role in insulin secretion and glucose homeostasis. TCF7L2 encodes a transcription factor expressed in pancreatic β cells that regulates insulin production and processing. Because mutations of these genes could affect insulin secretion stimulated by sulfonylureas, the aim of this study is to assess associations between molecular variants of KCNJ11, ABCC8 and TCF7L2 genes and response to sulfonylurea treatment and to predict their potential functional effects.

Methods

Based on a comprehensive literature search, we found 13 pharmacogenetic studies showing that single nucleotide polymorphisms (SNPs) located in KCNJ11: rs5219 (E23K), ABCC8: rs757110 (A1369S), rs1799854 (intron 15, exon 16 -3C/T), rs1799859 (R1273R), and TCF7L2: rs7903146 (intron 4) were significantly associated with responses to sulfonylureas. For in silico bioinformatics analysis, SIFT, PolyPhen-2, PANTHER, MutPred, and SNPs3D were applied for functional predictions of 36 coding (KCNJ11: 10, ABCC8: 24, and TCF7L2: 2; all are missense), and HaploReg v4.1, RegulomeDB, and Ensembl’s VEP were used to predict functions of 7 non-coding (KCNJ11: 1, ABCC8: 1, and TCF7L2: 5) SNPs, respectively.

Results

Based on various in silico tools, 8 KCNJ11 missense SNPs, 23 ABCC8 missense SNPs, and 2 TCF7L2 missense SNPs could affect protein functions. Of them, previous studies showed that mutant alleles of 4 KCNJ11 missense SNPs and 5 ABCC8 missense SNPs can be successfully rescued by sulfonylurea treatments. Further, 3 TCF7L2 non-coding SNPs (rs7903146, rs11196205 and rs12255372), can change motif(s) based on HaploReg v4.1 and are predicted as risk factors by Ensembl’s VEP.

Conclusions

Our study indicates that a personalized medicine approach by tailoring sulfonylurea therapy of T2D patients according to their genotypes of KCNJ11, ABCC8, and TCF7L2 could attain an optimal treatment efficacy.

Keywords

Sulfonylurea Type 2 diabetes Pharmacogenetics ABCC8 KCNJ11 TCF7L2 Single nucleotide polymorphism Bioinformatics In silico

Background

The prevalence of diabetes is increasing at a fast rate, which was 6.4% (285 million) among adults aged 20–79 years in 2010, and will increase to 7.7% (438 million) by 2030 [1]. Among all diabetic cases, approximately 90% are patients with type 2 diabetes (T2D), which is associated with a number of microvascular complications including retinopathy, nephropathy, neuropathy, as well as macrovascular complications [2]. T2D is caused by a plethora of lifestyle and genetic factors [3, 4]. Current therapies for T2D include life-style modifications and use of oral antidiabetic drugs, with sulfonylurea being one of the most frequently used one [5]. There are a number of different sulfonylurea treatments for T2D patients, among which the commonly used ones are gliclazide, glibenclamide, glimepiride and glipizide [6].

Sulfonylurea promotes insulin secretion from the pancreatic β cells of the pancreas in a glucose-independent manner by binding to ATP-sensitive K+ (KATP) channel on the cell membrane of pancreatic β cells. KATP channel is a hetero-octamer comprising the inward-rectifier potassium ion channels Kir6.x (i.e., Kir6.1 and Kir6.2) that form the pore, and sulfonylurea receptors (SUR; i.e., SUR1, SUR2A, and SUR2B) that regulate the opening and closing of its associated Kir6.x potassium channel, as SUR is sensitive to ATP and ADP levels. The binding of sulfonylureas to the corresponding receptors could lead to an efflux of intracellular potassium, hyperpolarization of the β cell membrane, and the opening of voltage-gated calcium channels, which result in an increased secretion of insulin to circulation (Fig. 1).
Fig. 1

A schematic representation of the pancreatic β cell illustrating the molecular model for insulin secretion mediated by KATP channel comprising KCNJ11 and ABCC8 subunits in sulfonylurea treatment

The pancreatic β cell KATP channel consists of four pore-forming subunits of the inwardly rectifying potassium channel Kir6.2 and four regulatory subunits of the SUR1 [79]. When blood glucose concentrations rise, an increase in glucose metabolism results in a change of ADP/ATP ratio, which leads to a closing of KATP channel. The respective genes encoding Kir6.2 and SUR1, i.e., KCNJ11 and ABCC8, are located next to each other on human chromosome 11p15.15. Mutations in KCNJ11 or ABCC8 genes could decrease or abolish the metabolic sensitivity of β cell KATP channel function, leading to a constant depolarization of the cell membrane and a persistent insulin secretion even at very low plasma glucose concentrations [10]. E.g., single nucleotide polymorphism (SNP) E23K (i.e., rs5219) of KCNJ11 gene is associated with T2D risk (reviewed in [11]), is shown to result in a decrease or loss of sensitivity of KATP channel to the inhibitory effect of ATP [12] and/or an enhancement of activation by free fatty acids [13]. Further, mutations in ABCC8 gene could cause hyperinsulinemic hypoglycemia [10]. The β cell KATP channel can be pharmacologically regulated by sulfonylureas, which function by binding to and closing the KATP channel [14] that leads to membrane depolarization, which subsequently results in an activation of voltage-dependent calcium channels causing an influx of calcium, which then triggers insulin granule exocytosis.

TCF7L2 encodes a member of the T-cell factor (TCF) transcription factor that plays a critical role in Wnt signaling pathway [15], which is shown to be involved in β cell dysfunction in T2D [16]. TCF7L2 is a member of the TCF-lymphocyte enhancer factor (LEF) protein family [17], and the bipartite transcription factor β-catenin/TCF-LEF serves as an effector of cAMP-dependent protein kinase A (PKA) signaling to mediate the physiological effects of peptide hormones including glucagon-like peptide-1 (GLP-1), which utilizes cAMP as a second messenger [18, 19]. TCF7L2 gene SNPs are strongly associated with a higher risk of T2D development [15], which could be mediated by their influences on blood glucose homeostasis [20].

Sulfonylureas show considerable inter-individual variations in the hypoglycemic response, with approximately 10–20% of patients having a less than 20 mg/dl reduction in fasting plasma glucose (FPG) following the initiation of sulfonylurea therapy (called primary sulfonylurea failure) [21]. Further, about 50–60% of patients will initially have a greater than 30 mg/dl reduction in FPG, but will fail to reach the desired glycemic treatment goals [21]. In contrast, some T2D patients could have higher risks of mild or severe hypoglycemia in response to sulfonylurea treatment [2224]. Molecular variants of sulfonylurea drug target genes KCNJ11, ABCC8, and TCF7L2 could lead to different responses to sulfonylurea therapy in T2D patients. Therefore, their impacts need to be carefully evaluated. The primary objective of this study is to predict functional effects of 36 coding (KCNJ11: 10, ABCC8: 24, and TCF7L2: 2, and all missense) and 7 non-coding (KCNJ11: 1, ABCC8: 1, and TCF7L2: 5) SNPs that were identified from published literatures and MutDB database (http://www.mutdb.org/) by applying a spectrum of in silico bioinformatics tools. Each Kir6.2 subunit has two transmembrane domains called M1 and M2, and the pore-forming domain is located between them [25]. The locations of 10 missense SNPs (including the well-studied E23K) in the KCNJ11 protein that comprises 390 amino acids [26] are shown in Fig. 2, respectively. Each SUR1 subunit has three transmembrane domains, i.e., TMD0, TMD1, and TMD3, and two nucleotide binding domains, i.e., NBD1 and NBD2. Between TMD0 and TMD1, there is a cytosolic loop called CL3 [27]. The locations of 24 missense SNPs (including the well-studied A1369S) in the ABCC8 protein that comprises 1581 amino acids [28] are shown in Fig. 3. The human TCF7L2 gene consists of 17 exons, five of which are alternatively spliced (i.e., exons 4, 13, 14, 15, and 16) and exhibits tissue-specific expression [29]. The differential splicing of TCF7L2 potentially gives rise to three groups of protein isoforms (i.e., short-, medium-, and large-length isoforms) with highly differential functional properties. These three groups depend on the predicted stop codon usages, which are located in exons 15, 16, 17 [30]. To date, TCF7L2 intronic SNP, rs7903146, represents the most significant risk variant for T2D [31]. However, four other non-coding SNPs, i.e., rs7901695, rs7895340, rs11196205 and rs12255372, have also been significantly associated with an increased risk of T2D [32] and have been widely studied. The locations of these 5 non-coding SNPs in the gene structure of TCF7L2 (including the well-studied intronic SNP rs7903146) are illustrated in Fig. 4.
Fig. 2

A schematic representation of 10 KCNJ11 missense SNPs in the protein product. Each Kir6.2 subunit (i.e., KCNJ11 protein product) contains two transmembrane domains, M1 and M2. Between M1 and M2, there is a pore-forming loop that creates the core of the K+ channel

Fig. 3

A schematic representation of 24 ABCC8 missense SNPs in the protein product. Each SUR1 subunit (i.e., ABCC8 protein product) contains 17 transmembrane helices, which are arranged in three transmembrane domains, i.e., TMD0, TMD1, and TMD2, respectively. The large cytosolic loop between TMD0 and TMD1 is called cytosolic loops 3 (CL3). The large cytosolic domains following TMD1 and TMD2 contain nucleotide-binding domain 1 (NBD1) and NBD2, respectively

Fig. 4

A schematic representation of 5 TCF7L2 non-coding SNPs in the gene structure. The start and stop codons are indicated by “ATG” (in exon 1) and “STOP” (in exons 15, 16, and 17), respectively. Because of alternative splicing, 3 groups of protein isoforms (i.e., short-, medium-, and large-length isoforms) can be generated by using different stop codons, which are indicated by “Short”, “Medium”, and “Long”, respectively

Methods

Literature search strategy

Comprehensive electronic literature searches of databases including PubMed, Google Scholar, Cochrane Library, Excerpta Medica Database (EMBASE) were performed up to June 1, 2016 using the following keywords: sulfonylurea, type 2 diabetes, KCNJ11, ABCC8, and TCF7L2. A manual search of the references cited in initially identified articles was also performed. Furthermore, we searched all relevant references of three comprehensive review articles [5, 33, 34]. The search was restricted to English language articles.

Inclusion and exclusion criteria

Randomized controlled trials and observational studies were eligible for inclusion in the current study. In vitro studies, animal studies, letters, reviews, and unrelated articles and duplicates were excluded from this study.

Data extraction

From each included study, the following data were extracted: first author, publication year, SNP name, gene name, National Center for Biotechnology Information (NCBI) dbSNP (http://www.ncbi.nlm.nih.gov/snp/) ID, study design, study subjects, control source, length of follow-up, and results.

In silico bioinformatics analysis

Computational predictions of functional impacts of non-synonymous SNPs (nsSNPs)

Five in silico tools were applied: (i) SIFT [35] (http://sift.jcvi.org/), (ii) PolyPhen-2 [36] (http://genetics.bwh.harvard.edu/pph2/), (iii) PANTHER [37] (http://www.pantherdb.org/tools/csnpScore.do), (iv) MutPred [38]) (http://mutpred.mutdb.org/), and (v) SNPs3D [39] (http://www.snps3d.org/).

Computational predictions of functional impacts of non-coding SNPs

Three in silico tools were applied: (i) HaploReg v4.1 [40, 41] (http://www.broadinstitute.org/mammals/haploreg/haploreg.php), (ii) RegulomeDB [42] (http://regulomedb.org/), and (iii) Ensembl’s VEP [43] (http://www.ensembl.org/Homo_sapiens/Tools/VEP?db=core).

Results

A total of 17 articles corresponding to 17 independent studies were qualified and subsequently included for evaluating the relationships between KCNJ11, ABCC8 and TCF7L2 SNPs and response to sulfonylurea in patients with T2D. The detailed characteristics of these 17 studies [4460] were presented in Table 1. Of them, 13 studies gave positive results, which showed that 1 SNP located in KCNJ11 rs5219 (E23K) [4547, 49, 50, 52], 3 SNPs located in ABCC8: rs757110 (A1369S) [46, 56], rs1799854 (intron 15, exon 16 -3C/T) [48, 53, 55], rs1799859 (R1273R) [48, 55], and 1 SNP located in TCF7L2: rs7903146 (intron 4) [5860], were significantly associated with responses to sulfonylureas. It is noteworthy that no uniform definition of response to sulfonylurea therapy was used across these 17 independent studies. Javorsky et al. (2012) [50] defined response to sulfonylurea as change in HbA1c level to sulfonylurea at 6-month therapy. Feng et al. (2008) [46] defined response to sulfonylurea as percent decrease in FPG and also FPG at day 57 as < 7.8 mmol/l as well as percent decrease in HbA1c after 8-week sulfonylurea therapy, and Holstein et al. (2009) [47] defined this drug response phenotype as sulfonylurea-induced hypoglycemia, which refers to a symptomatic event requiring treatment with intravenous glucose that was confirmed by a blood glucose measurement of <50 mg/dl. Meirhaeghe et al. (2001) [53] defined response to sulfonylurea as post-treatment fasting insulin, FPG, fasting plasma total cholesterol, and fasting plasma triglyceride concentrations, and Schroner et al. (2011) [59] defined this drug response phenotype as change of HbA1c (%) and changes of FPG for 3-month treatment and 6-month treatment, respectively. Pearson et al. (2007) [58] defined response to sulfonylurea as failure to reach a target HbA1c < 7% within 1-year treatment and minimum HbA1c achieved within 1-year treatment, and they also considered time taken on sulfonylurea treatment to achieve target HbA1c < 7% as a drug response phenotype. In addition, Sesti et al. (2006) [45] defined secondary sulfonylurea failure as FPG greater than 300 mg/dl despite sulfonylurea-metformin combined therapy and appropriate diet, in the absence of other conditions causing hyperglycemia, but Holstein et al. (2011) [60] defined secondary sulfonylurea failure as the addition of insulin after at least 6-month sulfonylurea therapy and corresponding HbA1c ≥ 7%. In the following, we first summarize major results of SNPs’ effects on sulfonylurea responses in a gene-by-gene manner, and then, we present functional prediction results for nsSNPs and non-coding SNPs by respective online bioinformatics tools.
Table 1

Characteristics of included studies (N = 17)*

Study ID

Author

Year

Gene Symbol

SNP Name

dbSNP ID

Study Design

Study Subjects

Control Source

Length of Follow-up

Results

Association

Study 1

Gloyn et al. [44]

2001

KCNJ11

E23K

rs5219

RCT

363 Caucasian T2D and 307 normoglycemic control subjects

UKPDS

1 year

Variant allele did not significantly affect the response to SU therapy significantly

No

Study 2

Sesti et al. [45]

2006

KCNJ11

E23K

rs5219

RCT

525 Caucasian T2D patients with secondary SU failure

Hospital-based

NA

Secondary SU failure, K allele vs E allele (OR = 1.45; 95% CI 1.01–2.09; P = 0.04). Adjustment for age, gender, fasting glycemia, glycosylated hemoglobin, age at diagnosis, and duration of diabetes in a logistic regression analysis did not change this association (OR = 1.69; 95% CI: 1.02–2.78; P = 0.04)

Yes

Study 3

Feng et al. [46]

2008

KCNJ11

E23K

rs5219

RCT

1268 Chinese T2D patients treated with 8-week gliclazide

Hospital-based

8 weeks

E23K variant of the KCNJ11 gene was significantly associated with decreases in FPG (P = 0.002).

Yes

Study 4

Holstein et al. [47]

2009

KCNJ11

E23K

rs5219

Case–control

43 T2D patients treated with glimepiride or glibenclamide

Hospital-based

NA

E23K variant was significantly associated with increased HbA1c levels (adjusted P = 0.04) independent of age, sex, body mass index, diabetes duration and SU dose.

Yes

Study 5

Nikolac et al. [48]

2009

KCNJ11

E23K

rs5219

Cross-sectional

228 Caucasian T2D patients with SU therapy

Hospital-based

NA

For KCNI11 E23K polymorphism, for different genotype groups, there were no significant differences of FPG, PPG, and HbA1c concentrations (P = 0.143, 0.675, and 0.824, respectively)

No

Study 6

El-sisi et al. [49]

2011

KCNJ11

E23K

rs5219

Case–control

50 Egyptian T2D patients with secondary SU failure

Hospital-based

NA

Secondary SU failure, EK + KK vs. EE (RR = 1.65; 95% CI: 1.04–2.6; P = 0.04).

Yes

Study 7

Javorsky et al. [50]

2012

KCNJ11

E23K

rs5219

RCT

55 T2D patients with 6-month treatment of gliclazide

Hospital-based

6 months

For ΔHbA1c EK + KK vs. EE (1.15 ± 0.09 vs. 0.80 ± 0.13, P = 0.036)

Yes

Study 7

Javorsky et al. [50]

2012

KCNJ11

E23K

rs5219

RCT

28 T2D patients with 6-month treatment of glimepiride

Hospital-based

6 months

For ΔHbA1c EK + KK vs. EE (1.10 ± 0.12 vs. 1.00 ± 0.19 P = 0.676)

No

Study 7

Javorsky et al. [50]

2012

KCNJ11

E23K

rs5219

RCT

14 T2D patients with 6-month treatment of glibenclamide

Hospital-based

6 months

For ΔHbA1c EK + KK vs. EE (1.05 ± 0.11 vs. 0.98 ± 0.09 P = 0.633)

No

Study 8

Ragia et al. [51]

2012

KCNJ11

E23K

rs5219

Case–control

92 T2D patients (80 glimepiride/12 gliclazide) who had experienced at least one drug-associated hypoglycemic event, while 84 T2D patients (74 glimepiride/10 gliclazide) who had never experienced a hypoglycemic event

Hospital-based

NA

KCNJ11 E23K genotype and allele frequencies were not different between hypoglycemic and non-hypoglycemic T2D patients (P = 0.35 and 0.47, respectively). In logistic regression models before and after adjustment for other risk factors (age, body mass index, sulfonylurea mean daily dose, duration of T2D, renal function and CYP2C9 genotype), KCNJ11 E23K polymorphism did not affect hypoglycemia risk

No

Study 9

Li et al. [52]

2014

KCNJ11

E23K

rs5219

RCT

108 Chinese T2D patients treated with gliclazide for 16 weeks

Hospital-based

16 weeks

Patients with the KK genotype had larger augmentations in changes (Δ) in acute insulin response (P = 0.049) and D body mass index (P = 0.003); Patients with the EK genotype had a lower variance in changes in fasting insulin levels (P = 0.049) and homeostasis model assessment of β cell function (P = 0.021) than those with the KK genotype

Yes

Study 1

Gloyn et al. [44]

2001

KCNJ11

L270V

rs1800467

RCT

363 Caucasian T2D patients

UKPDS

1 year

Variant allele did not significantly affect the response to SU therapy significantly

No

Study 10

Meirhaeghe et al. [53]

2001

ABCC8

Intron 15, exon 16 -3C/T

rs1799854

Cross-sectional

70 T2D patients with SU therapy

3 large representative French samples (in Lille, Strasbourg, and Toulouse) participating in the risk factor surveys of the WHO-MONICA

NA

For T2D patients treated with SU agents, those subjects bearing at least one -3C allele and had fasting plasma TG concentrations 35% lower than TT homozygotes [2.20 mmol/L (1.14–4.14) for TT vs. 1.43 mmol/L (0.81–2.52) for TC + CC; P = 0.026]

Yes

Study 11

Zychma et al. [54]

2002

ABCC8

Intron 15, exon 16 -3C/T

rs1799854

Case–control

68 Caucasian T2D patients who required insulin treatment and had known diabetes duration ≤ 5 years, compared to 99 Caucasian T2D patients receiving SU alone or in combination with metformin or acarbose with known diabetes duration ≥ 15 years

Hospital-based

NA

There was no significant impact of ABCC8 exon 16 -3C/T polymorphism on the early ineffectiveness of SU treatment (P = 0.4126 based on a Chi-square test)

No

Study 5

Nikolac et al. [48]

2009

ABCC8

Intron 15, exon 16 -3C/T

rs1799854

Cross-sectional

228 Caucasian T2D patients with SU therapy

Hospital-based

NA

CC genotype of the ABCC8 exon 16 polymorphism had significantly lower HbA1c concentration compared to the patients with T genotype [6.9 (6.2–7.7) mmol/L vs. 8.1 (6.7–8.8) mmol/L; P = 0.009]

Yes

Study 12

Nikolac et al. [55]

2012

ABCC8

Intron 15, exon 16 -3C/T

rs1799854

Cross-sectional

251 Caucasian T2D patients with SU therapy

Hospital-based

NA

Polymorphic allele carriers of the ABCC8 intron 15 -3C/T (which is 3 bp ahead of exon 16) polymorphism were more frequent in the subgroup of patients with the TG concentration increase after 6 months (P for genotype and allelic differences: 0.024 and 0.015, respectively)

Yes

Study 13

Zhang et al. [56]

2007

ABCC8

A1369S

rs757110

RCT

115 T2D patients with gliclazide treatment for 8 weeks

Hospital-based

8 weeks

For ΔHbA1c TG + GG vs. TT (1.60 ± 1.39 vs. 0.76 ± 1.70, P = 0.044)

Yes

Study 3

Feng et al. [46]

2008

ABCC8

A1369S

rs757110

RCT

1268 Chinese T2D patients treated with 8-week gliclazide

Hospital-based

8 weeks

Compared with TT genotype, subjects with the GG genotype had a 7.7% greater decrease in FPG (P < 0.001), an 11.9% greater decrease in 2-h plasma glucose (P = 0.003), and a 3.5% greater decrease in HbA1c (P = 0.06)

Yes

Study 14

Sato et al. [57]

2010

ABCC8

A1369S

rs757110

Case–control

32 patients with T2D admitted to hospital with severe hypoglycemia and 125 consecutive T2D outpatients without severe hypoglycemia, and all of the patients were taking glimepiride or glibenclamide

Hospital-based

NA

There were no significant differences in ABCC8 A1369S genotype distribution between patients with or without severe hypoglycemia (P = 0.26). Moreover, the A1369 allele tended to be less frequent in the hypoglycemic group (31 vs. 43%; OR = 1.65; 95% CI: 0.92–2.96; P = 0.09)

No

Study 5

Nikolac et al. [48]

2009

ABCC8

R1273R **

rs1799859

Cross-sectional

228 Caucasian T2D patients with SU therapy

Hospital-based

NA

GG genotype of the ABCC8 exon 31 polymorphism had significantly higher HbA1c concentration compared to the AA genotype [7.8 (6.9–8.8) mmol/L vs. 6.3 (5.7–6.8) mmol/L; P < 0.001]

Yes

Study 12

Nikolac et al. [55]

2012

ABCC8

R1273R **

rs1799859

Cross-sectional

251 Caucasian T2D patients with SU therapy

Hospital-based

NA

Wile-type G allele carriers had a significantly higher TG concentration when compared with the carriers of two variant A alleles (P = 0.023)

Yes

Study 15

Pearson et al. [58]

2007

TCF7L2

NA

rs7903146

RCT

901 T2D patients with SU treatment

GoDARTS

12 months

Carriers of the risk allele were less likely to respond to SUs with an OR for failure of 1.95 (95% CI: 1.23–3.06; P = 0.005), comparing rs12255372 TT vs. GG. Including the baseline HbA1c strengthened this association (OR = 2.16, 95% CI: 1.21–3.86, P = 0.009)

Yes

Study 16

Schroner et al. [59]

2011

TCF7L2

NA

rs7903146

RCT

87 T2D patients with 6-month SU treatment in addition to metformin

Hospital-based

6 months

Reduction in HbA1c: CC vs. CT + TT is 1.16 ± 0.07 vs. 0.86 ± 0.07%, P = 0.003 Reduction in FPG: 1.57 ± 0.12 vs. 1.14 ± 0.14 mmol/L, P = 0.031)

Yes

Study 17

Holstein et al. [60]

2011

TCF7L2

NA

rs7903146

RCT

189 T2D patients with 6-month SU treatment

Hospital-based

6 months

T allele was significantly more frequent in the group of patients who failed to respond to SU (i.e., those with HbA1c ≥ 7%) (36%) than in the control (i.e., those with HbA1c < 7%) group (26%) (OR = 1.57, 95% CI: 1.01-2.45, P = 0.046)

Yes

*Studies are grouped by different genes. For each gene, studies are first sorted by SNP Name, then by Year, and then by Author, in ascending orders. Abbreviations: CI confidence interval, FPG fasting plasma glucose, Go-DARTS Genetics of Diabetes Audit and Research Study in Tayside Scotland, HbA1c glycated hemoglobin A1c, OR odds ratio, RCT randomized clinical trial, SNP single nucleotide polymorphism, SU sulfonylurea, SUR sulfonylurea receptor, T2D type 2 diabetes, TG triglyceride, UKPDS United Kingdom Prospective Diabetes Study, WHO-MONICA World Health Organization-Multinational MONItoring of trends and determinants of CArdiovascular diseases, NA not available

**Because R1273R is a synonymous SNP, it is not included in functional prediction

KCNJ11

The most widely studied genetic polymorphism of KCNJ11 for sulfonylurea response is E23K (i.e., rs5219) located in exon 1 [33]. However, functional effects of KCNJ11 E23K polymorphism on the secretion and sensitivity of insulin in humans remain contentious [5]. Recent larger studies demonstrated that a significant reduction of insulin secretion, lower levels of insulin, and an improvement of insulin sensitivity were related to E23K variant in KCNJ11 gene [61]. Moreover, E23K variant was associated with T2D development, which means that the K allele carriers had an increased risk of T2D [44, 62, 63]. Furthermore, some studies also found that the K allele carriers had better therapeutic response to gliclazide in comparison with the EE homozygous wild-type group [50], as well as an increased risk of sulfonylurea treatment failure [45, 49]. In addition, E23K variant was significantly associated with an increase of glycated hemoglobin A1c (HbA1c) level [47] and fasting glucose level that patients with the KK homozygous variant genotype had lower fasting glucose levels than those with the EE/EK heterozygous genotype [52]. Importantly, recent evidence demonstrated that patients with KCNJ11 variants responded more efficiently to sulfonylurea than insulin [6466]. Another KCNJ11 polymorphism that was associated with sulfonylurea treatment responses is rs5210 which is located in 3’- untranslated region (UTR). A study conducted in two independent cohorts of Chinese T2D patients (cohort 1: n = 661, cohort 2: n = 607) treated with gliclazide demonstrated that KCNJ11 rs5210 was positively associated with gliclazide response in cohort 1 study [46].

ABCC8

The most widely studied genetic polymorphism of ABCC8 for sulfonylurea response is S1369A (i.e., rs757110) located in exon 33 [67]. This genetic variant was demonstrated to influence antidiabetic efficacy of sulfonylurea treatment in Chinese [46, 56], as well as an increased sensitivity to gliclazide [56]. More importantly, KCNJ11 E23K and ABCC8 S1369A, two common KATP channel mutations that were in strong linkage disequilibrium, form a haplotype that appears to be associated with an increased T2D risk [68]. Additional ABCC8 gene polymorphisms including rs1799854 (intron 15, exon 16 -3C/T) and rs1799859 (exon 31) had been shown to be associated with sulfonylurea treatment efficacy in Caucasians [48, 55].

TCF7L2

Previous studies have shown that several non-coding genetic variants of TCF7L2 are associated with T2D risk in populations of diverse ancestries from countries encompassing United Kingdom [69], the Netherlands [70], Finland [32], Sweden [71], France [72], United States [73], India [74], and Japan [75] populations. Among these T2D-associated TCF7L2 variants, rs7903146 (intron 4) showed the strongest association with T2D [76]. Significant reductions in HbA1c and fasting plasma glucose levels following a combined sulfonylurea and metformin treatment between T2D patients with CC genotype and those with CT/TT genotype were associated with TCF7L2 rs7903146 variant allele [59]. Moreover, the rs12255372 variant, together with the rs7903146 variant, was shown to be associated with a significantly more frequent treatment failure [5860]. It shall be noted that although in previous literatures, e.g., as in [32, 77], TCF7L2 rs7901695 and rs7903146 are indicated to be in intron 3, and rs7895340, rs11196205 and rs12255372 are indicated to be in intron 4, this is because exon 4, which is a variable exon, is often named as “3a” [78]. Because of a high incorporation in pancreatic β cells [79], exon 4 shall be included in the gene structure, such that rs7901695 and rs7903146 shall be indicated as located in intron 4, and rs7895340, rs11196205, and rs12255372 in intron 5, respectively, e.g., as in [80]. For the linear ordering of these 5 non-coding SNPs, according to the most updated (i.e., as of April 18, 2017) NCBI dbSNP, the chromosomal coordinates for rs7901695, rs7903146, rs7895340, rs11196205 and rs12255372 are 112994329, 112998590, 113041766, 113047288, and 113049143, respectively, on human chromosome 10 based on GRCh38.p7 assembly. Therefore, the linear ordering shall be rs7901695-rs7903146-rs7895340-rs11196205-rs12255372, as shown in Fig. 4 (all drawings in Figs. 1, 2, 3, and 4 are not to their exact scales and are for illustration purposes), which is agreement with that of [77].

In silico bioinformatics analysis results

For KCNJ11, ABCC8 and TCF7L2 genes, functional prediction results for 36 nsSNPs by SIFT, PolyPhen-2, PANTHER, MutPred, and SNPs3D were presented in Table 2, and those prediction results for 7 non-coding SNPs by HaploReg v4.1, RegulomeDB and Ensembl’s VEP were presented in Table 3.
Table 2

In silico predicted functional effects of 36 non-synonymous SNPs in the pharmacogenetics of sulfonylureas treatment by SIFT, PolyPhen-2, PANTHER, MutPred, and SNPs3D*

SNP ID

Gene Symbol

SNP Name

dbSNP ID

SNP Location

Chromosome Location (GRCh38.p7)

SIFT Score/Prediction

PolyPhen-2 Score/Prediction

PANTHER subSPEC

PANTHER Pdeleterious

MutPred Pdeleterious

SNPs3D Score

SNP1

KCNJ11

E23K

rs5219

Exon 1

11:17388025

1.00/Tolerated

0.001/Benign

−0.69172

0.09044

0.35

2

SNP2

KCNJ11

R27H

NA

Exon 1

NA

0.18/Tolerated

0.006/Benign

−3.75303

0.67984

0.248

NA

SNP3

KCNJ11

V59M

NA

Exon 1

NA

0.12/Tolerated

0.999/Probably damaging

−2.72126

0.43076

0.855

NA

SNP4

KCNJ11

I182V

NA

Exon 1

NA

0.98/Tolerated

0.998/Probably damaging

−1.62168

0.20128

0.684

NA

SNP5

KCNJ11

R192H

NA

Exon 1

NA

0.01/Affect Protein Function

1.000/Probably damaging

−6.9765

0.98159

0.816

NA

SNP6

KCNJ11

R201H

rs80356624

Exon 1

11:17387490

0.00/Affect Protein Function

1.000/Probably damaging

NA

NA

0.981

NA

SNP7

KCNJ11

E227K

NA

Exon 1

NA

0.00/Affect Protein Function

1.000/Probably damaging

−7.17583

0.98487

0.94

NA

SNP8

KCNJ11

L270V

rs1800467

Exon 1

11:17387284

0.13/Tolerated

0.003/Benign

−1.54301

0.18893

0.566

0.68

SNP9

KCNJ11

I337V

rs5215

Exon 1

11:17387083

0.73/Tolerated

0.000/Benign

−0.89045

0.10817

0.462

0.94

SNP10

KCNJ11

S385C

rs41282930

NA

11:17386938

0.02/Affect Protein Function

0.380/Possibly damaging

NA

NA

0.229

NA

SNP11

ABCC8

G7R

NA

Exon 1

NA

0.00/Affect Protein Function

1.000/Probably damaging

NA

NA

0.863

NA

SNP12

ABCC8

N24K

NA

Exon 1

NA

0.03/Affect Protein Function

1.000/Probably damaging

NA

NA

0.877

NA

SNP13

ABCC8

F27S

NA

Exon 1

NA

0.00/Affect Protein Function

0.884/Probably damaging

NA

NA

0.858

NA

SNP14

ABCC8

N72S

rs80356634

Exon 2

11:17474961

0.12/Tolerated

0.402/Possibly damaging

NA

NA

0.802

NA

SNP15

ABCC8

R74W

NA

Exon 2

NA

0.00/Affect Protein Function

1.000/Probably damaging

NA

NA

0.904

NA

SNP16

ABCC8

A116P

NA

NA

NA

0.12/Tolerated

1.000/Probably damaging

NA

NA

0.825

NA

SNP17

ABCC8

E128K

NA

Exon 3

NA

0.02/Affect Protein Function

1.000/Probably damaging

NA

NA

0.829

NA

SNP18

ABCC8

F132L

rs80356637

Exon 3

11:17470119

0.16/Tolerated

0.877/Possibly damaging

NA

NA

0.847

NA

SNP19

ABCC8

V187D

NA

Exon 4

NA

0.01/Affect Protein Function

0.042/Benign

NA

NA

0.857

NA

SNP20

ABCC8

L213R

rs80356642

Exon 5

11:17461767

0.41/Tolerated

0.212/Possibly damaging

−3.12006

0.52998

0.786

NA

SNP21

ABCC8

E382K

rs80356651

NA

11:17453151

0.27/Tolerated

0.392/Possibly damaging

−1.96296

0.26172

0.872

NA

SNP22

ABCC8

R495Q

NA

Exon 10

NA

0.00/Affect Protein Function

1.000/Probably damaging

−8.28432

0.99496

0.906

NA

SNP23

ABCC8

E501K

NA

Exon 10

NA

0.00/Affect Protein Function

1.000/Probably damaging

−2.39817

0.35392

0.948

NA

SNP24

ABCC8

L503P

NA

Exon 10

NA

0.00/Affect Protein Function

1.000/Probably damaging

−4.69708

0.84515

0.964

NA

SNP25

ABCC8

F686S

NA

Exon 15

NA

0.01/Affect Protein Function

0.998/Probably damaging

−4.1174

0.75351

0.909

NA

SNP26

ABCC8

G716V

rs72559723

Exon 16

11:17427124

0.18/Tolerated

1.000/Probably damaging

−8.97797

0.99747

0.974

NA

SNP27

ABCC8

K1336N

NA

NA

NA

0.25/Tolerated

0.016/Benign

−1.84974

0.24044

0.693

NA

SNP28

ABCC8

L1349Q

NA

Exon 33

NA

0.01/Affect Protein Function

0.997/Probably damaging

−4.16681

0.76257

0.912

NA

SNP29

ABCC8

A1369S

rs757110

Exon 33

11:17396930

0.51/Tolerated

0.000/Benign

−0.85285

0.1046

0.323

0.87

SNP30

ABCC8

S1386F

NA

Exon 34

NA

0.00/Affect Protein Function

1.000/Probably damaging

−4.60703

0.833

0.95

NA

SNP31

ABCC8

L1389P

NA

Exon 34

NA

0.00/Affect Protein Function

0.993/Probably damaging

−4.93634

0.87395

0.886

NA

SNP32

ABCC8

R1420C

rs28938469

Exon 35

11:17395659

0.00/Affect Protein Function

1.000/Probably damaging

NA

NA

0.863

−1.19

SNP33

ABCC8

I1424V

rs80356653

Exon 35

11:17395647

0.00/Affect Protein Function

0.988/Probably damaging

−2.02622

0.27413

0.882

NA

SNP34

ABCC8

D1471H

NA

Exon 36

NA

0.00/Affect Protein Function

0.994/Probably damaging

−4.60764

0.83308

0.913

NA

SNP35

TCF7L2

P179H

rs3197486

NA

10:113141236

0.00/Affect Protein Function

1.000/Probably damaging

−5.62868

0.93268

0.879

0.01

SNP36

TCF7L2

K323N

rs2757884

NA

10:113151761

0.00/Affect Protein Function

1.000/Probably damaging

−4.23841

0.77529

0.309

−0.15

* Abbreviations: MutPred Mutation Prediction, PANTHER, Protein ANalysis THrough Evolutionary Relationships, PolyPhen-2 Polymorphism Phenotyping v2, SIFT Sorting Intolerant from Tolerant, SNP Single Nucleotide Polymorphism, subSPEC subStitution Position-specific Evolutionary Conservation, NA Not Available

Table 3

In silico predicted functional effects of 7 non-coding SNPs in the pharmacogenetics of sulfonylureas treatment by Haploreg v4.1, RegulomeDB, and Ensembl’s VEP*

SNP ID

Gene Symbol

dbSNP ID

SNP Location

Chromosome Location (GRCh38.p7)

HaploReg v4.1

Motifs changed by SNP

RegulomeDB Score/Prediction

Ensembl’s VEP

SNP37

KCNJ11

rs5210

3’-UTR

11:17386704

None

4/Minimal binding evidence

NA

SNP38

ABCC8

rs1799854

Intron 15

11:17427157

4 altered motifs

5/Minimal binding evidence

NA

SNP39

TCF7L2

rs7895340

Intron 5

10:113041766

Irf, PRDM1

NA

NA

SNP40

TCF7L2

rs7901695

Intron 4

10:112994329

None

5/Minimal binding evidence

NA

SNP41

TCF7L2

rs7903146

Intron 4

10:112998590

7 altered motifs

5/Minimal binding evidence

Risk factor

SNP42

TCF7L2

rs11196205

Intron 5

10:113047288

SMC3

5/Minimal binding evidence

Risk factor

SNP43

TCF7L2

rs12255372

Intron 5

10:113049143

5 altered motifs

NA

Risk factor

*Abbreviations: RegulomeDB Regulome Database, SNP Single Nucleotide Polymorphism, UTR Untranslated Region, VEP Variant Effect Predictor, NA Not Available

Analysis of functional effects of SNPs by SIFT

SIFT was used to predict the functional impact of an nsSNP on a protein molecule. An nsSNP with a SIFT score ≤ 0.05 is considered as having a deleterious effect on protein function [81]. A total of 22 nsSNPs were predicted to affect protein function (SIFT score range: 0.00-0.03) including 4 KCNJ11 missense SNPs (R192H, R201H, E227K, S385C), 16 ABCC8 missense SNPs (G7R, N24K, F27S, R74W, E128K, V187D, R495Q, E501K, L503P, F686S, L1349Q, S1386F, L1389P, R1420C, I1424V, D1471H), and 2 TCF7L2 missense SNPs (P179H, K323N), whereas the remaining 14 missense SNPs were predicted to be tolerated (SIFT score range: 0.12–1.00) (Table 2).

Analysis of functional effects of nsSNPs by PolyPhen-2

PolyPhen-2 calculates a naïve Bayes posterior probability for a given mutation that it will be benign (PolyPhen-2 score < 0.15), possibly damaging (PolyPhen-2 score is greater than or equal to 0.15 but is less than 0.85), or probably damaging (PolyPhen-2 score ≥ 0.85), respectively [82]. A total of 25 nsSNPs were predicted to be probably damaging to protein function (PolyPhen-2 score range: 0.877–1.000), which includes 5 KCNJ11 missense SNPs (V59M, I182V, R192H, R201H, E227K), 18 ABCC8 missense SNPs (G7R, N24K, F27S, R74W, A116P, E128K, F132L, R495Q, E501K, L503P, F686S, G716V, L1349Q, S1386F, L1389P, R1420C, I1424V, D1471H) and 2 TCF7L2 missense SNPs (P179H, K323N), and the remaining 11 SNPs were classified as benign (PolyPhen-2 score range: 0.000–0.402) (Table 2).

Analysis of functional effects of nsSNPs by PANTHER

PANTHER characterizes likely functional effect of amino acid variation by means of a hidden Markov model-based statistical modeling and evolutionary relationship. The SNP with subSPEC score ≤ −3 is considered as intolerant or deleterious, whereas SNP with subSPEC score > −3 is classified to be less deleterious [83]. A total of 14 amino acid substitutions were classified as intolerant (subSPEC score range: from−8.97797 to−3.12006) including 3 KCNJ11 missense SNPs (R27H, R192H, E227K), 9 ABCC8 missense SNPs (L213R, R495Q, L503P, F686S, G716V, L1349Q, S1386F, L1389P, D1471H) and 2 TCF7L2 missense SNPs (P179H, K323N), another 10 amino acid substitutions were classified as tolerated (subSPEC score range: from−2.72126 to−0.69172), and the remaining 12 amino acid substitutions did not have subSPEC scores (Table 2).

Analysis of functional effects of nsSNPs by MutPred

MutPred predicts molecular causes of disease or deleterious amino acid substitution. A total of 30 nsSNPs had p-values > 0.5, which were considered to be functional [84] (MutPred Pdeleterious range: 0.566-0.981), which included 6 KCNJ11 missense SNPs (V59M, I182V, R192H, R201H, E227K, L270V), 23 ABCC8 missense SNPs (G7R, N24K, F27S, N72S, R74W, A116P, E128K, F132L, V187D, L213R, E382K, R495Q, E501K, L503P, F686S, G716V, K1336N, L1349Q, S1386F, L1389P, R1420C, I1424V, D1471H) and 2 TCF7L2 missense SNPs (P179H, K323N) (Table 2).

Analysis of functional consequences of SNPs by SNPs3D

SNPs3D assigns molecular functional effects of nsSNPs based on structure and sequence analysis. Of the 36 nsSNPs, SNPs3D SVM score was available for only 7 nsSNPs (KCNJ11: 2, ABCC8: 3, and TCF7L2: 2). Of them, two nsSNPs, i.e., R1420C amino acid substitution of ABCC8 gene and K323N amino acid substitution of TCF7L2 gene, had SVM scores < 0, which were classified as deleterious substitutions [85] (Table 2).

Analysis of functional consequences of SNPs by HaploReg v4.1

HaploReg v4.1 is an online software for exploring annotations of the non-coding genome among those results of published genome-wide association studies or new sets of genetic variants, which help researchers to integrate DNA regulatory elements data with genetic variants to quickly formulate novel biological hypotheses [40, 41]. As predicted by HaploReg v4.1, rs1799854, rs7895340, rs7903146, rs11196205 and rs12255372 could change 4, 2 (i.e., Irf and PRDM1), 7, 1 (i.e., SMC3), and 5 DNA motifs for DNA-binding proteins, and could have regulatory effects on gene transcription. Neither rs5210 nor rs7901695 appear to change known motifs (Table 3).

Analysis of functional consequences of SNPs by RegulomeDB

RegulomeDB is a database that annotates SNPs with known and predicted regulatory elements in the intergenic regions of the human genome. Of the 7 non-coding SNPs, rs5210, rs1799854, rs7901695, rs7903146, and rs11196205 had RegulomeDB scores of 4, 5, 5, 5, and 5, respectively, which were all classified as having minimal binding evidence. Predictions were not available for either rs7895340 or rs12255372 (Table 3).

Analysis of functional consequences of SNPs by Ensembl’s VEP

The Ensembl’s VEP determines the effects of genetic variants on genes, transcripts, and protein sequences, as well as regulatory regions. Three non-coding SNPs of TCF7L2 gene, i.e., rs7903146, rs11196205 and rs12255372, were predicted as risk factors (Table 3).

Discussion

Sulfonylureas are a class of drugs that stimulates insulin secretion by closing KATP channels in pancreatic β cells. It has been estimated that 10–20% of individuals treated do not attain adequate glycemic control, and 5–10% initially responding to sulfonylurea subsequently lose the ability to maintain near-normal glycemic level [86]. This implies that genetic factors are linked with treatment efficacy of sulfonylureas. In our study, that includes 17 studies, two KCNJ11 SNPs — rs5219 (E23K) (exon 1) and rs5210 (3’-UTR), three ABCC8 SNPs — rs757110 (A1369S) (exon 33), rs1799854 (intron 15, exon 16 -3C/T), rs1799859 (R1273R) (exon 31), and two TCF7L2 SNPs rs7903146 (intron 4) and rs12255372 (intron 5) have been associated with response to sulfonylureas. Based on bioinformatics predictions for 36 selected coding SNPs (all are missense) for KCNJ11, ABCC8, and TCF7L2, by applying a set of computational tools — SIFT, PolyPhen-2, PANTHER, MutPred, and SNPs3D. Our bioinformatics prediction results demonstrated that 8 KCNJ11 missense SNPs (R27H, V59M, I182V, R192H, R201H, E227K, L270V, and S385C), 23 ABCC8 missense SNPs (G7R, N24K, F27S, N72S, R74W, A116P, E128K, F132L, V187D, L213R, E382K, R495Q, E501K, L503P, F686S, G716V, K1336N, L1349Q, S1386F, L1389P, R1420C, I1424V, D1471H), and 2 TCF7L2 missense SNPs (P179H, K323N) could affect protein functions with SIFT score ≤ 0.05, or PolyPhen-2 score ≥ 0.85, or PANTHER subSPEC score ≤ −3, or MutPred > 0.5, or SNPs3D score < 0. Of them, previous studies showed that mutant alleles of 4 KCNJ11 missense SNPs (R27H, V59M, R192H, and R201H) and 5 ABCC8 missense SNPs (G7R, N24K, F27S, R74W, and E128K) can be successfully rescued by sulfonylurea treatments. In addition, 3 TCF7L2 non-coding SNPs — rs7903146, rs11196205 and rs12255372 were predicted as risk factor based on Ensembl’s VEP, although their functional impacts in sulfonylurea results need to be elucidated by further experimental studies.

Conclusion

The ultimate goal of pharmacogenetics is the development of personalized medicine through individual genetic profiles which would accurately predict which individuals with a specific medical condition would respond to a specific medical therapy. Traditional medicine refers to the broad application of “standard of care” or “one-size-fits-all” treatments to all patients with a given diagnosis. In contrast, personalized medicine, often described as providing “the right drug for the right patient at the right dose and time” [87], tailors medical treatment according to each patient’s personal history, genetic profile and/or specific biomarkers [88, 89], Therefore, the full application of personalized medicine in health care will require significant changes in regulatory and reimbursement policies as well as legislative protections for privacy. The U.S. Food and Drug Administration has updated the labels of more than 120 drugs with recommendations for genetic testing prior to their use [90]. Currently, most genetic testing is based genotypic effects. Haplotypes of multiple linked genetic variants provide more precise information of their functional impacts than individual genetic markers [91, 92], which could also be potentially important for diagnosis and prognosis [93]. In future, regulatory authorities shall formulate clear guidelines for evaluating and approving personalized diagnostics and therapeutics and identify patients who can benefit from them.

Abbreviations

ABCC8: 

ATP Binding Cassette Subfamily C Member 8

ADP: 

Adenosine diphosphate

ATP: 

Adenosine triphosphate

cAMP: 

Cyclic adenosine monophosphate

CL: 

cytosolic loop

dbSNP: 

Single Nucleotide Polymorphism database

EMBASE: 

Excerpta Medica Database

GLP-1: 

Glucagon-like peptide-1

HbA1c: 

Glycated hemoglobin A1c

Go-DARTS: 

Genetics of Diabetes Audit and Research Study in Tayside Scotland

KATP

ATP-sensitive K+ channel

KCNJ11: 

Potassium channel, inwardly rectifying subfamily J, member 11

Kir: 

Inwardly rectifying K(+)

LEF: 

Lymphocyte enhancer factor

MutPred: 

Mutation prediction

NBD: 

nucleotide binding domain

NCBI: 

National Center for Biotechnology Information

nsSNP: 

Non-synonymous single nucleotide polymorphism

PANTHER: 

Protein analysis through evolutionary relationships

PKA: 

Protein kinase A

PolyPhen-2: 

Polymorphism phenotyping v2

RCT: 

Randomized clinical trial

RegulomeDB: 

Regulome database

SIFT: 

Sorting intolerant from tolerant

SNP: 

Single nucleotide polymorphism

SU: 

Sulfonylurea

subSPEC: 

subStitution Position-specific Evolutionary Conservation

SUR: 

Sulfonylurea receptor

T2D: 

Type 2 diabetes

TCF: 

T-cell factor

TCF7L2: 

T-cell factor 7-like 2

TMD: 

transmembrane domain

UKPDS: 

United Kingdom Prospective Diabetes Study

UTR: 

Untranslated region

VEP: 

Variant effect predictor

WHO-MONICA: 

World Health Organization-Multinational MONItoring of trends and determinants of CArdiovascular diseases

Wnt: 

Wingless type

Declarations

Acknowledgements

Dr. Yu-Ping Wang and Dr. Tianhua Niu are supported in part by NIH 1R01GM109068-01A1. Dr. Tianhua Niu is also supported in part by a start-up fund of the Center for Bioinformatics and Genomics, Tulane University. Dr. Franck Mauvais-Jarvis is supported by NIH R01 DK074970, the American Diabetes Association (7-13-BS-101), and the Price-Goldsmith Endowed Chair in Nutrition.

Funding

Funded in part by NIH 1R01GM109068-01A1.

Availability of data and materials

Not applicable.

Authors’ contributions

TN conceived the idea for the project. TN and JS contributed to study design and conception. JS, YY and TN participated in data analysis and interpretation. JS, YY and TN drafted the manuscript. FMJ and YW revised it critically for intellectual content. All authors read and approved the final version of the manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine
(2)
Division of Endocrinology and Metabolism, Department of Medicine, Tulane University Health Sciences Center
(3)
Department of Biomedical Engineering, Tulane University School of Science and Engineering

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