Association of genetic variants in RETN, NAMPT and ADIPOQ gene with glycemic, metabolic traits and diabetes risk in a Chinese population

Accepted Manuscript Association of genetic variants in RETN, NAMPT and ADIPOQ gene with glycemic, metabolic traits and diabetes risk in a Chinese population

Qiang Zhou, Bo Chen, Tianxing Ji, Miaoshan Luo, Jiandong Luo PII: DOI: Reference:

S0378-1119(17)30929-0 doi:10.1016/j.gene.2017.10.084 GENE 42305

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Gene

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26 July 2017 26 October 2017 30 October 2017

Please cite this article as: Qiang Zhou, Bo Chen, Tianxing Ji, Miaoshan Luo, Jiandong Luo , Association of genetic variants in RETN, NAMPT and ADIPOQ gene with glycemic, metabolic traits and diabetes risk in a Chinese population. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Gene(2017), doi:10.1016/j.gene.2017.10.084

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ACCEPTED MANUSCRIPT Association of genetic variants in RETN, NAMPT and ADIPOQ gene with glycemic, metabolic traits and diabetes risk in a Chinese population Qiang Zhou1, Bo chen1, Tianxing Ji1, Miaoshan Luo2, Jiandong Luo2* 1

Clinical laboratory, The second Affiliated Hospital of Guangzhou Medical University, No.

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250 Changgang East Road, Haizhu District, Guangzhou 510260,China. 2

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Department of Pharmacology, Guangzhou Medical University, Xinzao, Panyu District,

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Guangzhou 511436, China. *

Corresponding author:

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Jiandong Luo, MD., PhD., Department of Pharmacology, Guangzhou Medical University,

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Xinzao , Panyu District, Guangzhou, 511436, P. R. China. Tel: 86-20-34152437; Fax:

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86-20-34152437; E-mail: [email protected]

ACCEPTED MANUSCRIPT Abstract Abnormal serum levels of adipokine have been established to be a strong predictor of developing several human diseases including type 2 diabetes mellitus (T2DM). Association studies have reported several genetic variants in genes coding adipokines with contributions

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to T2DM susceptibility as well as some glycemic and metabolic traits, of which the single

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nucleotide polymorphisms (SNPs) of RETN, NAMPT, and ADIPOQ gene were well

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documented. However, little is known about contributions of these SNPs to above phenotypes in Chinese. In the current study, with availably quantitative glycemic and metabolic data from

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a total of 185 T2DM patients and 191 healthy controls, we tested associations between four

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SNPs of RETN, NAMPT, ADIPOQ gene and 13 glycemic and metabolic traits. The results showed that the rs1862513 and rs34861192 of RETN gene were functional and negatively

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correlated with the levels of serum creatinine and cholesterol, respectively. The rs16861194 of

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ADIPOQ gene was positively correlated with the aspartate aminotransferase (AST) and AST/alanine aminotransferase level. Moreover, the rs34861192 and rs13237989 of NAMPT

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gene synergistically affected the levels of insulin and glycemic index. However, due to the

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limited sample size, only the rs16861194 exerted a significant increased risk on T2DM. These results underscore the contributions of SNPs in RETN, NAMPT, ADIPOQ gene to glycemic and metabolic traits as well as T2DM susceptibility in Chinese.

ACCEPTED MANUSCRIPT Highlights： 

The RETN SNPs rs1862513 and rs34861192 were functional and associated with the

levels of serum creatinine and cholesterol, respectively. 

The ADIPOQ SNP rs16861194 was positively correlated with the levels of serum AST

The RETN rs34861192 and NAMPT rs13237989 synergistically affected the levels of

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

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as well as AST/ ALT.

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serum insulin and glycemic index.

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Keywords: adipokine, glycemic and metabolic traits, susceptibility

ACCEPTED MANUSCRIPT 1. Introduction Diabetes has been a common public health problem, which is characterized by impaired insulin secretion and/or the insulin resistance. According to the report from the World Health Organization (WHO), there were estimated 422 million adults with diabetes in the world in

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2014 and by 2030, diabetes would be the seventh cause of death (Organization, 2016). In

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China, the prevalence rate of diabetes was estimated to be 11.6% in Chinese adults in 2010,

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which signified about 114 million peoples living with diabetes (Xu et al., 2013). Prevalence of diabetes has been exponentially increasing in China during the past 30 years, and being

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bring invaluable burden to Chinese society.

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accompanied by rapid development of economy, lifestyle changes and aging, the disease will

More than 90% of diabetes patients are type 2 diabetes mellitus (T2DM) (Weng et al.,

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2016). T2DM is a disease affected by polygenic inheritance and multiple environmental

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factors, whose pathomechanism involves gene-environment interaction. Different susceptibility to T2DM development has been suggested by the fact that genetic heritability

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ranges from 23% to 68% for T2DM and the disease-related metabolic syndrome (Jowett et al.,

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2009). Moreover, a family based analysis demonstrated that heritability of insulin sensitivity index and acute insulin response to glucose were estimated as about 67% and 38%, respectively (Elbein et al., 1999). As far as can be determined, genetic variants such as single nucleotide polymorphism (SNP) account for the genetic heritability. Multiple studies have yet identified a lot of SNPs harboring genetic contributions to T2DM susceptibility, especially the genome-wide association studies (GWASs) that have maximally identified the genetic determiners of T2DM (Go et al., 2014; Below and Parra, 2016; Imamura et al., 2016; Teumer

ACCEPTED MANUSCRIPT et al., 2016). The majority of these T2DM-associated loci appear to act through the β-cell function-related pathways, and a handful of ones seem to operate via insulin resistance such as diabetes-associated adipocytokines (Dastani et al., 2012; Chung et al., 2014). Diabetes-associated adipocytokines are a class of endogenous signal molecules that are

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involved in glucose and lipid metabolism. These adipocytokines can interfere the insulin

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signaling pathway and play pivotal roles in T2DM development. Resistin, visfatin and

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adiponectin are well-known adipocytokines with contributions to development of insulin resistance (Koleva et al., 2013; Bilir et al., 2016). Serum levels of the three adipocytokines

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have been documented to be associated with various human diseases such as T2DM and

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obesity (Noureldeen et al., 2014; Dikbas et al., 2016; Grygiel-Gorniak et al., 2016; Karpavicius et al., 2016). Furthermore, The genetic variants in the genes coding resistin

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(RETN, OMIM: 605565) and adiponectin (ADIPOQ, OMIM: 605441) were also reported to

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be associated with the risk of T2DM in different ethnics (Engert et al., 2002; Ma et al., 2002; Hivert et al., 2009; Peters et al., 2013; Ramya et al., 2013; Arikoglu et al., 2014; Chung et al.,

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2014; Tsai et al., 2014; Narayana Swamy et al., 2015; Erfanian et al., 2016; Phani et al., 2016;

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Yao et al., 2016). However, there is a lack of study on variants in visfatin genes (NAMPT, OMIM: 608764) and their associations with T2DM risk, except for three studies conducting in different ethnics demonstrating no significant association between the NAMPT SNPs and T2DM risk (Blakemore et al., 2009; Paschou et al., 2010; Sheng et al., 2011). Glycemic and metabolic traits are critical clinical features of T2DM, which are correlated with the risk, progression and complications of disease. However, few studies have assessed effect of genetic variants on these traits, and little is known about their genetic

ACCEPTED MANUSCRIPT determinants in Chinese. Considering the relationship between the adipocytokines and glycemic/metabolic traits (Ciresi et al., 2016),we hypothesized that the genetic variants of RETN, NAMPT, and ADIPOQ gene were associated with glycemic and metabolic traits. On account of that the SNPs rs1862513 and rs34861192 in the promoter of RTEN gene and

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rs16861194 in the promoter of ADIPOQ gene have been reported to be functional and

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associated with T2DM risk (Hivert et al., 2009; Mtiraoui et al., 2012; Chu et al., 2013;

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Hishida et al., 2013), as well as the SNP rs13237989 in intron of NAMPT gene was supported to be putatively functional, we selected the four SNPs to test their associations with glycemic

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and metabolic traits as well as the risk of T2DM in the current study.

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2. Materials and methods 2.1 Study subjects

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Having approved by the institutional review boards of Guangzhou medical university, a

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total of 346 subjects were recruited from the second affiliated hospital of Guangzhou medical university from March 2014 to April 2016. Among them, 185 were newly diagnosed T2DM in

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accordance with the WHO diagnostic criteria for diabetes at 1999; 161 were healthy controls

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with fasting blood glucose (FBG) <6.1mmol/L and had no history of kidney disease, hypertension as well as family history of diabetes, who participated in a healthy checkup program on the community health at the physical examination department of above hospital. All subjects were genetically-unrelated Han Chinese. Having signed an inform consent, each participant who did not take in any food for at least 8 hours was asked to denote double 3 ml venous blood samples in the morning and then provide data on age and sex. One sample was stored in the vacuum tube without anticoagulation, which was further used to measure

ACCEPTED MANUSCRIPT glycemic and metabolic traits, and the other was stored in the vacuum tube with anticoagulation, which was further applied for genotyping. 2.2 Measurement of glycemic and metabolic traits Serum levels of glycemic and metabolic traits including blood sugar, insulin, glycemic

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index (GI), triglyceride (TG), cholesterol (CHOL), high density lipoprotein cholesterol (HDL),

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low density lipoprotein cholesterol (LDL), aspartate aminotransferase (AST), alanine

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aminotransferase (ALT), creatinine (CREA) and uric acid (UA) were measured by standard laboratory methods. The HOMAB-cell function index (HBCI) was calculated as 20 ×fasting

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insulin/(FBG-3.5) as suggested (Albareda et al., 2000).

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2.3 SNP selection and genotyping

Putatively functional common SNPs (minor allele frequency >5%) that have been

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previously reported or suggested by bioinformatics analysis were selected. In brief, the

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rs1862513 and rs34861192 of RTEN gene and the rs16861194A>G of ADIPOQ were selected, because they have been demonstrated to be functional and promising risk indicators of T2DM

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susceptibility (Hivert et al., 2009; Mtiraoui et al., 2012; Chu et al., 2013; Hishida et al., 2013).

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Since no published study reported functional SNPs of NAMPT gene with association of T2DM risk, we performed bioinformatics analysis (https://snpinfo.niehs.nih.gov/) to select possible functional SNPs with an expectation of previously reported SNPs that are not associated with T2DM risk. The rs13237989C>T were selected, because it was shown that the SNP is located at an intron splicing enhancer and ranks the top with regard to function scores. Genomic DNA was extracted from the blood sample using the DNA Blood Mini Kit (Qiagen, Valencia, A). The Sequenom MassARRAY® SNP detection was used to genotyping each SNP

ACCEPTED MANUSCRIPT under the MassARRAY Nanodispenser and Compact System by the Beijing Genomics Institute (Shenzhen, China). 2.4 Detection of resistin and visfatin The standard enzyme-linked immunosorbent assay (ELISA) method was conducted to

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determine the levels of plasma resistin and visfatin using the human Resistin ELISA Kit and

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visfatin ELISA Kit (mskbio, Wuhan, China).

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2.5 Statistical analysis

The data of categorical variables are presented as number (%), while those of continuous

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variables are presented as mean ± standard deviation (SD). The Hardy-Weinberg equilibrium

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was tested by a goodness-of-fit χ2 test in healthy controls. The χ2 test was used to assess differences in frequency of categorical data between cases and controls. The Satterthwaite

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approximate t-test was applied for evaluating differences in continuous variables between

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cases and controls. The linear regression analysis with adjustment to age and sex was performed to test association between each SNP and the glycemic and metabolic traits.

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Results for the linear regression analysis are presented as regression coefficient ± standard

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error (SE). The one-way ANOVA test was used to analyze genotype-phenotype correlation. The generalized multifactor dimensionality reduction (GMDR) software was applied for analyzing possible “gene-gene” or “gene-environment” interaction (Lou et al., 2007). The logistical model without or with age and sex as co-variables was used to calculate crude odds ratio (OR) or adjusted OR as well as 95% confidence interval (95%CI) to determine association between each SNP and the risk of T2DM. Both the additive genetic model and dominant model were estimated in above analyses. The SAS software (version 9.4; SAS

ACCEPTED MANUSCRIPT Institute, Cary, NC) was applied to perform above analyses and P< 0.05 was considered to be statistically significant. 3. Results 3.1 Demographics and glycemic, metabolic traits of study subjects

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Distributions in demographic variables and glycemic, metabolic traits of the studied 376

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subjects are presented in Table 1. The serum levels of blood sugar, insulin and GI were

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significantly higher in the T2DM patients than the healthy controls (P< 0.05 for all). The patients also exerted statistically significant decrease in HDL (P< 0.001) and increase in

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CREA (P< 0.001) in comparison with the controls. Furthermore, both plasma resistin (P<

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0.001) and visfatin (P= 0.010) showed significantly higher levels in the cases than controls. However, no statistically significant difference was observed for other traits between the cases

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and controls. In addition, there were significant differences in age and sex between the cases

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and controls, thus the two variables were further adjusted in the logistic and linear regression model to control possible confounding effects among main effects of each SNP on the traits or

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disease.

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3.2 Associations between the four SNPs and glycemic and metabolic traits Results for association analyses of the four SNPs with glycemic and metabolic traits are shown in Table 2-3. For the rs1862513 of RETN gene, there was a significantly negative correlation between it and the level of serum CREA under both the additive model (regression coefficient ± standard error: -19.8±8.05; P = 0.015) and dominant model (-30.3±11.3; P = 0.008). The CREA level gradually decreased from CC genotype carriers, GC genotype ones to GG genotype ones. For the rs34861192 of RETN gene, significant correlations were observed

ACCEPTED MANUSCRIPT between it and insulin level (7.51±3.06; P = 0.015), GI (3.28±1.50; P = 0.029) under the additive model as that individuals with the AA genotype had visibly higher concentrations of insulin and GI than those with AG or GG genotype. Meanwhile, the SNP was also significantly correlated with CHOL level under both the additive model (-0.24±0.10; P =

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0.022) and dominant model (-0.25±0.13; P = 0.022) as that individuals harboring the AA or

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AG genotype exerted lower concentration of CHOL than those harboring the GG genotype.

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The rs13237989 of NAMPT gene was significantly associated with insulin level under the dominant model (-7.93±3.65; P = 0.031) and GI level under both the additive model

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(-3.07±1.27; P = 0.016) and dominant model (-4.95±1.78; P = 0.006). The rs16861194 of

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ADIPOQ gene were statistically significant correlated with AST level (2.82±1.14; P = 0.014), AST/ALT level (0.14±0.05; P = 0.008) as that the concentrations of AST and AST/ALT were

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gradually increased in a G-allele dose-dependent manner. Furthermore, no other significant

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correlation was observed between each SNP and glycemic and metabolic traits (data not shown).

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3.3 Combined effect of promising SNPs on glycemic and metabolic traits

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Because above findings showed that the levels of insulin and GI were correlated with both the rs34861192 and rs13237989, we combined genotype of the two SNPs based on the number of risk allele according to the following rule. The A allele of rs34861192 and the T allele of rs13237989 were defined as risk alleles, because of higher levels of insulin and GI of them in comparison with their corresponding alleles. As long as the SNP exerted additive effect, the number was defined as 0 for non-risk homozygote genotype, 1 for heterozygote genotype and 2 for risk homozygote genotype. If the SNP only exerted dominant effect, the

ACCEPTED MANUSCRIPT number was defined as 0 for non-risk homozygote genotype, 1 for both heterozygote genotype and risk homozygote genotype. As shown in Figure 1, the number of risk allele was positively correlated with both concentrations of insulin (6.75±2.60; P = 0.010) and GI (3.16±0.95; P = 0.001). Individuals carrying 3 risk alleles existed the highest level of insulin

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(0: 8.90±8.22 vs. 1:9.76±18.7 vs. 2: 11.3±17.5 vs. 3: 49.1±149.8; P< 0.001), while subjects

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4.49±13.3 vs. 3: 7.34±17.7 vs. 4: 53.4±100.3; P< 0.001).

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carrying 4 risk alleles exerted the highest level of GI (0: 3.78±5.44 vs. 1:3.52±7.32 vs. 2:

3.4 Interaction analysis

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There was a significant interaction between the rs34861192 and rs13237989 on affecting

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the levels of serum insulin and GI (P< 0.05 for both). Meanwhile, the rs1862513 significantly interacted with age and sex on influencing TG level (P = 0.011). No significant interaction

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was observed for other traits (data not shown).

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3.5 Correlations between resistin and CREA as well as CHOL Because the RETN SNPs showed significant effect on CREA level and CHOL level, we

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assessed correlations between resistin concentration and serum levels of the two traits. As

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shown in Figure 1c and 1d, there was a moderately positive correlation between resistin and CREA (r = 0.347, P < 0.001), while a mildly negative correlation between resistin and CHOL (r = -0.167, P = 0.001). 3.6 Genotype-phenotype correlations Serum levels of resistin were much higher in subjects with the rs1862513GG and GC genotypes than those with the GG genotype (P < 0.01; Figure 2a). They were also significantly higher in individuals with the rs34861192AA and AG genotypes than those with

ACCEPTED MANUSCRIPT the GG genotype (P < 0.001; Figure 2b).When combined the two SNPs, the rs1862513GG/rs34861192AA carriers exerted the highest plasma resistin than other combinations (P < 0.001; Figure 2c). However, the rs13237989 had no significant effect on the level of serum visfatin (Figure 2d).

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3.7 Associations between the selected SNPs and the risk of T2DM

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Results of association between each SNP and T2DM risk are shown in Table 4. Due to

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the limited sample size, we only observed a significant difference in genotype frequency of rs16861194 between the cases and controls (P = 0.002).Compared to the AA genotype, GA

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heterozygote exerted an increased risk of T2DM (adjusted OR = 1.72, 95%CI = 1.06-2.08, P

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=0.030), and GG homozygote had a further increased risk (adjusted OR = 4.92, 95%CI = 1.33-18.1, P =0.017). Since that only 12 subjects has GG genotype, we added them to

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heterozygotes for all the statistical analyses. After combination, the risk of T2DM increased

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by 95% in the G genotypes (GG+GA) carriers compared to the AA genotype carriers (adjusted OR = 1.95, 95%CI = 1.22-3.10, P =0.005).

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4. Discussion

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This study represents the first evaluation of associations between genetic variants in genes coding three well-known adipocytokines and glycemic and metabolic traits, which are closely related to the risk, progression and complications of T2DM. Significant associations were observed between (1) the rs1862513 of RETN gene and the level of serum CREA; (2) the rs34861192 of RETN gene and the levels of insulin, GI and CHOL; (3) the rs13237989 of NAMPT gene and the levels of insulin and GI; and (4) the rs16861194 of ADIPOQ gene and the levels of AST and AST/ALT. Also, the rs34861192 and the rs13237989 had a significant

ACCEPTED MANUSCRIPT interaction on modulating the levels of insulin and GI. Both RETN SNPs were functional. In addition, only the rs16861194 exerted a significantly increased risk of T2DM. The most puzzling aspect of findings in this study is that the rs1862513 showed a negative correlation with CREA. As shown in the results, the level of serum resistin was

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positively correlated with that of CREA, which is in accordance with previous reports (Zhang

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et al., 2015). However, the rs1862513GG or GC genotype exerted a higher level of resistin

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than the CC genotype. Thus, as expected, the rs1862513 should have been positively correlated with CREA, but instead they were negatively correlated. This controversy may be

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due to that the plasma level of CREA depends on multiple factors. Since the resistin is not the

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only promising factor for determining plasma CREA level, it is possible that other factors disturbed the correlation between the rs1862513 and CREA level, just like that the

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rs34861192 was also significantly correlated with resistin, but showed no significant

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association with CREA level. Furthermore, being consistent with the current study, multiple studies have confirmed that the G genotypes of the rs1862513 were associated with higher

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resistin concentrations than the CC genotype (Azuma et al., 2004; Cho et al., 2004; Asano et

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al., 2010). Functional analysis also supported this (Osawa et al., 2004). Moreover, the rs34861192 has been reported to be associated with the level of resistin (Asano et al., 2010). These evidences further supported the rs1862513 and rs34861192 to be promising variants for determining plasma resistin. In addition, the rs34861192 was also associated with the level of serum insulin, GI and CHOL. Resistin can induce insulin resistance (Cantley, 2014). Also, the level of resistin was negatively correlated with that of CHOL. Thus, it is conceivable to see

ACCEPTED MANUSCRIPT that subjects carrying the rs34861192AG and AA genotypes had higher levels of insulin and GI, and lower level of CHOL than those carrying the GG genotype. In addition, the two RTEN SNPs are expected to be associated with the risk of T2DM, but things go athwart, no significant association was observed between them and T2DM risk

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in the current study. The two RTEN SNPs have been reported to be susceptible loci for T2DM

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in several ethnics including Han Chinese (Khodaeian et al., 2015). The RETN gene codes

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resistin, which is a newly found adipocytokines and closely related to diabetes (Steppan et al., 2001). Since the two SNPs were associated with the serum levels of resistin, insulin as well as

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GI, we speculated that our non-significant finding was majorly due to the limited sample size.

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The significant associations between the rs13237989 of NAMPT gene and insulin as well as GI are first reported. The NAMPT gene codes the protein visfatin, which is critical for beta

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cell function via mediation of nicotinamide adenine dinucleotide biosynthesis and thus

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correlated with the blood sugar level (Revollo et al., 2007). The serum level of visfatin has been demonstrated to be positively correlated with the insulin resistance (Jin et al., 2016).

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Therefore, it is theoretically reliable that the rs13237989 has contribution to insulin and GI.

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There are three published studies investigating the associations of genetic variants of NAMPT gene with the risk of T2DM, all of which reported a non-significant association. Yet, they revealed a promoter SNP and an intron rare variant to be associated with post-prandial serum insulin and obesity, respectively (Blakemore et al., 2009; Paschou et al., 2010; Sheng et al., 2011). Analogously, the rs13237989 was associated with insulin but not T2DM risk in the current study. However, no statistically significant difference of the serum level of visfatin was observed between subjects carrying different genotype of the rs13237989, which is also

ACCEPTED MANUSCRIPT in compliance with the intron location of this SNP in NAMPT gene. Since the SNP is located at an intron splicing enhancer, it is possible that the SNP affects NAMPT function via alternative splicing process. This mechanism is leaved for future research. We also found a significant association between the rs16861194 of ADIPOQ gene and

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ALT as well as AST/ALT. The rs16861194 was functional as that the G genotypes has been

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demonstrated to be associated with a lower expression of adiponectin than the AA genotype in

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human (Laumen et al., 2009). Several studies have demonstrated that the plasma adiponectin levels were inversely correlated to AST (Lu et al., 2007; Caner et al., 2014). These evidences

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confirm the positive correlation between the rs16861194 and the levels of ALT as well as

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AST/ALT. Furthermore, adiponectin is an adipocyte-derived protein with downregulated expression in T2DM (Savvidou et al., 2016). Thus, the subjects with the rs16861194G

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genotypes exerting low adiponectin expression should have an increased risk of T2DM. This

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study showed the findings so they could be, suggesting the rs16861194G genotypes to be a possible biomarker of increased T2DM susceptibility.

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There are some limitations in the current study. Based on a retrospective design, bias

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such as information bias and selection bias could not be rule out. The studied sample size was relatively small. There was also a lack of validation samples to verify our findings. However, most of our findings are consistent with functional evidence and theoretical basis, suggesting that our findings are unlikely to be achieved by chance. 5. Conclusions In conclusion, results of the present underscore the contributions of genetic variants in adipokines genes to glycemic and metabolic traits. The RETN variants rs1862513, rs34861192

ACCEPTED MANUSCRIPT and ADIPOQ variant rs16861194 have contributions to serum levels of creatinine, cholesterol, aspartate aminotransferase and aspartate aminotransferase/alanine aminotransferase, respectively. Moreover, the rs34861192 and rs13237989 synergistically determine the concentrations of insulin and glycemic index. On the account of that the sample size is

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relatively small, further studies with large sample size in Chinese are needed.

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Acknowledgements:

This study was supported by theGuangdong Science and Technology Program Grant

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2012B061700044 (Q, Zhou) and the GuangdongMedical Scientific Research Project

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GrantA2012256 (Q, Zhou).

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Conflict of Interest Statement: None declare.

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10, 1925-1930.

ACCEPTED MANUSCRIPT Table 1. Difference in baseline characteristics, biochemical indices and diabetes-related Adipokines between the diabetes patients and healthy controls. Diabetes patients

Healthy controls

P value a

185

191

Age (years)

65.3±12.8

62.2±11.7

0.016

0.002

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Total no. of subjects

RI

Variables

Sex 92(49.7)

Female

93(50.3) 10.5±6.42

Insulin (mIU/L)

16.9±47.0

GI HBCI

126(66.0) 4.95±0.52

<0.001

6.39±6.00

0.003

8.85±22.8

1.43±1.49

<0.001

104.5±320.4

95.5±73.8

0.712

1.98±1.62

3.42±228.8

0.490

5.17±1.39

5.29±0.89

0.337

1.06±0.30

1.36±0.26

<0.001

3.19±1.15

3.32±0.82

0.182

26.7±14.9

26.1±8.80

0.644

22.7±16.3

20.8±7.55

0.154

AST/ALT

1.36±0.68

1.34±0.38

0.715

CREA (umol/L)

143.6±148.2

86.8±21.2

<0.001

UA (umol/L)

364.7±122.9

357.9±87.5

0.541

Resistin (ng/ml)

4.70±4.19

2.33±2.11

<0.001

MA

D PT E

TG (mmol/L)

HDL (mmol/L)

ALT (U/L)

AC

LDL (mmol/L)

CE

CHOL (mmol/L)

AST (U/L)

NU

Blood sugar (mmol/L)

65(34.0)

SC

Male

ACCEPTED MANUSCRIPT Visfatin (pg/ml)

24.0±30.7

17.8±9.91

0.010

*

Abbreviations: GI: glycemic index; HBCI: HOMAB-cell function index; TG: triglyceride; CHOL:

cholesterol; HDL: high density lipoprotein cholesterol; LDL: low density lipoprotein cholesterol; AST:

AC

CE

PT E

D

MA

NU

SC

RI

PT

aspartate aminotransferase; ALT: alanine aminotransferase; CREA: creatinine; UA: Uric Acid.

ACCEPTED MANUSCRIPT Table 2. Associations between the SNPs of RTEN gene and 15 glycemic and metabolic traits. rs1862513 CC

GC

GG

(n=150)

(n=173)

(n=53)

Additive

Dominant

effect

effect

(GG vs. GC

(GC + GG vs.

vs. CC)

CC)

rs34861192 a GG

I R

Traits

Glycemic

Blood

7.14±4.08

8.26±5.61

7.40±7.01

0.39±0.40

SC

(n=111)

AA (n=17)

Dominant

effect

effect

(AA vs. AG

(AA + AG vs.

vs. GG)

GG)

7.77±5.29

7.62±5.59

7.04±3.66

-0.19±0.48

-0.19±0.59

2.09±3.57

9.81±18.3

10.8±16.5

41.7±136.0

7.51±3.06*

4.84±3.72

1.22±1.74

4.23±11.3

5.13±12.6

17.0±55.7

3.28±1.50*

2.35±1.82

118.3±274.9

sugar

D E

10.3±21.2

10.8±16.0

17.7±77.4

GI

4.31±12.6

5.14±11.9

β-cell

95.0±140.0

98.6±275.6

T P E

1.21±1.23 8.59±17.2

7.69±24.3

87.7±119.7

113.4±337.4

189.2±472.8

32.9±21.0

31.8±25.4

3.95±30.3

1.85±2.24

0.63±1.56

1.83±2.19

3.30±25.4

1.63±1.08

1.43±0.75

-1.60±1.90

-1.99±2.29

C C

A

7.06±31.8

2.85±2.53

A M

Insulin

function Metabolic

(n=247)

U N

0.89±0.56

T P

AG

Additive

TG

1.59±1.07

CHOL

5.26±1.32

5.23±1.04

5.16±1.11

-0.04±0.08

-0.02±0.13

5.30±1.21

5.07±1.04

4.81±1.13

-0.24±0.10*

-0.25±0.13*

HDL

1.21±0.32

1.20±0.30

1.25±0.35

0.01±0.02

0.01±0.03

1.22±0.31

1.20±0.31

1.14±0.38

-0.03±0.03

-0.02±0.03

ACCEPTED MANUSCRIPT LDL

3.30±1.12

3.28±0.89

3.05±0.97

-0.09±0.07

-0.05±0.10

3.34±1.03

3.12±0.91

3.02±0.93

-0.17±0.09

-0.20±0.11

AST

26.6±14.1

26.1±10.1

26.7±12.5

-0.07±0.91

-0.36±1.29

26.3±12.7

26.1±8.86

29.5±20.4

0.59±1.12

0.18±1.35

ALT

21.2±11.4

22.1±13.9

22.3±11.5

0.75±0.95

1.18±1.33

21.4±11.4

22.7±15.3

20.5±9.18

0.80±1.16

1.40±1.40

AST/ALT

1.40±0.70

1.32±0.42

1.30±0.41

-0.06±0.04

-0.09±0.06

1.36±0.60

1.29±0.42

1.44±0.45

-0.02±0.05

-0.05±0.06

CREA

132.3±147.9

104.7±76.6

97.7±39.9

-19.8±8.05*

-30.3±11.3**

118.8±121.0

101.9±63.2

139.8±148.6

-4.16±9.89

-12.2±11.9

UA

365.2±109.8

354.0±101.8

373.0±109.6

0.05±7.89

-8.78±11.1

363.0±96.8

378.2±121.9

5.41±9.62

3.49±11.7

Bold indicated significant correlation.*P< 0.05; **P< 0.01.

D E

T P E

A

C C

A M

I R

SC

U N

T P

359.2±109.2

ACCEPTED MANUSCRIPT Table 3. Associations between the SNPs of NAMPT and ADIPOQ gene and 15 glycemic and metabolic traits. rs13237989 CC

TC

TT

(n=127)

(n=195)

(n=54)

Additive

Dominant

effect

effect

(TT vs. TC

(TT+TC vs.

vs. CC)

CC)

rs16861194 AA

I R

Traits

Glycemic

Blood

8.09±6.38

7.39±4.65

7.85±4.77

-0.26±0.41

sugar

SC

A M

(n=93)

GG (n=15)

Dominant

effect

effect

(GG vs. GA

(GG+GA vs.

vs. AA)

AA)

7.49±5.37

8.35±5.29

7.20±4.09

0.34±0.50

0.63±0.60

-5.09±2.60

-7.93±3.65*

12.6±38.4

9.15±17.6

7.81±4.39

-2.82±3.18

-3.51±3.85

-3.07±1.27*

-4.95±1.78**

5.69±18.8

3.73±8.12

2.48±1.97

-1.78±1.55

-2.14±1.88

73.4±60.5

-33.0±17.7

-47.2±24.9

105.9±257.6

70.5±79.2

176.3±323.1

-1.27±21.7

-18.3±26.2

1.59±0.99

9.32±54.1

2.98±1.59

1.70±2.26

1.58±1.04

6.20±41.4

1.57±0.58

2.64±1.95

4.05±2.36

Insulin

16.8±55.8

9.01±11.1

8.31±6.97

GI

8.35±26.5

3.41±6.86

β-cell

131.8±374.1

86.5±98.9

T P E

C C

function Metabolic

D E

(n=268)

U N

-0.63±0.58

T P

GA

Additive

A

3.39±4.71

TG

1.61±1.06

CHOL

5.27±1.09

5.18±1.21

5.30±1.19

-0.001±0.09

-0.05±0.12

5.23±1.15

5.20±1.19

5.40±1.30

0.04±0.11

0.03±0.13

HDL

1.24±0.28

1.20±0.32

1.18±0.36

-0.03±0.02

-0.03±0.03

1.22±0.32

1.19±0.30

1.20±0.39

-0.01±0.03

-0.01±0.04

ACCEPTED MANUSCRIPT LDL

3.30±0.91

3.23±1.05

3.25±1.01

-0.03±0.08

-0.05±0.11

3.29±0.97

3.14±1.05

3.49±1.08

-0.03±0.09

-0.09±0.11

AST

26.2±12.0

26.9±12.2

25.1±12.5

-0.25±0.94

0.31±1.33

25.7±9.85

27.0±12.5

35.3±31.3

2.82±1.14*

2.41±1.39

ALT

22.0±15.1

21.8±10.8

21.1±12.4

-0.35±0.98

-0.34±1.38

21.8±13.2

21.8±11.5

20.3±7.06

-0.42±1.19

-0.25±1.45

AST/ALT

1.33±0.41

1.38±0.67

1.27±0.30

-0.01±0.04

0.03±0.06

1.31±0.43

1.36±0.47

1.81±1.66

0.14±0.05**

0.11±0.06

CREA

100.8±60.3

117.0±127.6

139.4±119.7

18.48±10.3

20.3±11.8

119.5±123.1

105.7±62.0

86.8±23.6

-16.4±10.2

-18.1±12.3

UA

354.3±104.5

363.8±109.1

368.1±99.5

6.91±8.16

9.45±11.5

356.3±107.0

332.1±101.9

-13.3±9.93

-13.9±12.1

Bold indicated significant correlation.*P< 0.05; **P< 0.01.

D E

T P E

A

C C

A M

I R

SC

U N

364.5±106.1

T P

ACCEPTED MANUSCRIPT Table 4. Associations between the SNPs of RTEN, NAMPT and ADIPOQ gene and the risk of T2DM.

Genotypes

Patients

185

191

CC

72(38.9)

78(40.8)

GC

92(49.7)

81(42.4)

GG

21(11.4)

32(16.8)

Crude

Adjusted

OR (95% CI)

OR (95% CI)b

P

PT

Total no. of subjects

Controls

a

1.00 (ref.)

NU

1.00 (ref.)

1.23(0.79-1.91)

1.16(0.74-1.82)

0.71(0.38-1.34)

0.73(0.38-1.39)

0.623

0.599

1.00 (ref.)

1.00 (ref.)

MA D

rs34861192 117(63.2)

130(68.1)

57(30.8)

55(28.8)

1.15(0.74-1.80)

1.09(0.68-1.72)

11(6.0)

6(3.1)

2.04(0.73-5.68)

2.27(0.80-6.45)

0.198

0.227

1.00 (ref.)

1.00 (ref.)

PT E

GG

AA

0.351

AC

Trend test P value

CE

AG

rs13237989

SC

0.209

Trend test P value

RI

rs1862513

CC

58(31.4)

69(36.1)

TC

97(52.4)

98(51.3)

1.19(0.75-1.84)

1.15(0.73-1.82)

TT

30(16.2)

24(12.6)

1.49(0.78-2.82)

1.48(0.77-2.85)

0.221

0.247

Trend test P value

0.466

ACCEPTED MANUSCRIPT

1.00 (ref.)

1.00 (ref.)

38(19.9)

1.84(1.14-2.97) *

1.72(1.06-2.80) *

12(6.5)

3(1.6)

5.09(1.40-18.4) *

4.92(1.33-18.1) *

67(36.2)

41(21.5)

PT

rs16861194 118(63.8)

150(78.5)

GA

55(29.7)

GG GA+GG

2.08(1.32-3.28)**

1.95(1.22-3.10)**

0.002

RI

AA

Trend test P value

SC

0.002

The observed genotype frequencies of SNPs among the controls were all in agreement with the

Hardy–Weinberg equilibrium (P > 0.05 for all).

NU

a

0.001

AC

CE

PT E

D

MA

Bold indicated significant correlation.*P< 0.05; **P< 0.01.

ACCEPTED MANUSCRIPT Figure legends Figure 1: Differences in levels of serum insulin and GI between 376 subjects carrying different number of risk genotypes and correlations between the levels of serum resistin and CREA as well as CHOL. a. The serum level of insulin was grouped by the number of risk

PT

genotypes. b. The serum level of GI was grouped by the number of risk genotypes. Bar height

RI

corresponds to the mean, and error bars represent SD. ***P < 0.001, calculated by the

SC

one-way ANOVA test. c. Correlation between resistin and CREA. d. Correlation between resistin and CHOL. P and r were calculated by the linear regression model.

NU

Figure 2: Differences in the levels of serum diabetes-related Adipokine between 376 subjects

MA

carrying different genotypes. a. The serum level of resistin by the rs1862513. b. The serum level of resistin by the rs34861192. c. The serum level of resistin by the combination of

D

rs1862513 and rs34861192. d. The serum level of visfatin by the rs13237989. Bar height

PT E

corresponds to the mean, and error bars represent SD. ** P < 0.01, ***P < 0.001, calculated by

AC

CE

the one-way ANOVA test.

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

AC

CE

PT E

Fig. 1

PT E

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

AC

CE

Fig. 2

ACCEPTED MANUSCRIPT Abbreviations list T2DM: Type 2 diabetes mellitus; RETN: resistin; NAMPT: visfatin; ADIPOQ: adiponectin; SNP: single nucleotide polymorphism; GWAS: genome-wide association study; WHO: World Health Organization; FBG: fasting blood glucose; GI: glycemic index; HBCI: HOMAB-cell

PT

function index; TG: triglyceride; CHOL: cholesterol; HDL_CH: high density lipoprotein

RI

cholesterol; LDL_CH: low density lipoprotein cholesterol; AST: aspartate aminotransferase;

SC

ALT: alanine aminotransferase; CREA: creatinine; UA: Uric Acid; GMDR: generalized

AC

CE

PT E

D

MA

NU

multifactor dimensionality reduction; OR: odds ratios; 95%CI: 95% confidence interval.

AC

CE

PT E

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

Graphical abstract