Next Article in Journal
Association of the STAT4 Gene rs7574865 Polymorphism with IFN-γ Levels in Patients with Systemic Lupus Erythematosus
Next Article in Special Issue
Missense Variants of von Willebrand Factor in the Background of COVID-19 Associated Coagulopathy
Previous Article in Journal
Genomic Insights into the Origin of a Thermotolerant Tomato Line and Identification of Candidate Genes for Heat Stress
Previous Article in Special Issue
Association of Single-Nucleotide Polymorphisms Rs2779249 (chr17:26128581 C>A) and Rs rs2297518 (chr17: chr17:27769571 G>A) of the NOS2 Gene with Tension-Type Headache and Arterial Hypertension Overlap Syndrome in Eastern Siberia
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 14
Issue 3
10.3390/genes14030536
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genetic Variants of HNF4A, WFS1, DUSP9, FTO, and ZFAND6 Genes Are Associated with Prediabetes Susceptibility and Inflammatory Markers in the Saudi Arabian Population

by
Dalal N. Binjawhar
1,†,
Mohammed G. A. Ansari
2,†,
Shaun Sabico
2,
Syed Danish Hussain
2,
Amal M. Alenad
2,
Majed S. Alokail
2,
Abeer A. Al-Masri
3 and
Nasser M. Al-Daghri
2,*
1
Department of Chemistry, College of Science, Princess Nourah bint Abdulrahman University, Riyadh 11564, Saudi Arabia
2
Department of Biochemistry College of Science, King Saud University, Riyadh 11451, Saudi Arabia
3
Department of Physiology, College Medicine, King Saud University, Riyadh 11451, Saudi Arabia
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work and shared the first authorship.
Genes 2023, 14(3), 536; https://doi.org/10.3390/genes14030536
Submission received: 16 January 2023 / Revised: 19 February 2023 / Accepted: 20 February 2023 / Published: 21 February 2023
(This article belongs to the Special Issue Single-Nucleotide Polymorphisms: Association, Molecular Function, Application, and Progress)
Review Reports Versions Notes

Abstract

:
Prediabetes is a reversible, intermediate stage of type 2 diabetes mellitus (T2DM). Lifestyle changes that include healthy diet and exercise can substantially reduce progression to T2DM. The present study explored the association of 37 T2DM- and obesity-linked single nucleotide polymorphisms (SNPs) with prediabetes risk in a homogenous Saudi Arabian population. A total of 1129 Saudi adults [332 with prediabetes (29%) and 797 normoglycemic controls] were randomly selected and genotyped using the KASPar SNP genotyping method. Anthropometric and various serological parameters were measured following standard procedures. Heterozygous GA of HNF4A-rs4812829 (0.64; 95% CI 0.47–0.86; p < 0.01), heterozygous TC of WFS1-rs1801214 (0.60; 95% confidence interval (CI) 0.44–0.80; p < 0.01), heterozygous GA of DUSP9-rs5945326 (0.60; 95% CI 0.39–0.92; p = 0.01), heterozygous GA of ZFAND6-rs11634397 (0.75; 95% CI 0.56–1.01; p = 0.05), and homozygous AA of FTO-rs11642841 (1.50; 95% CI 0.8–1.45; p = 0.03) were significantly associated with prediabetes, independent of age and body mass index (BMI). Additionally, C-reactive protein (CRP) levels in rs11634397 (AA) with a median of 5389.0 (2767.4–7412.8) were significantly higher than in the heterozygous GA genotype with a median of 1736.3 (1024.4–4452.0) (p < 0.01). In conclusion, only five of the 37 genetic variants previously linked to T2DM and obesity in the Saudi Arabian population [HNF4A-rs4812829, WFS1-rs1801214, DUSP9-rs5945326, ZFAND6-rs11634397, FTO-rs11642841] were associated with prediabetes susceptibility. Prospective studies are needed to confirm the potential clinical value of the studied genetic variants of interest.

1. Introduction

Type 2 diabetes mellitus (T2DM) is one of the most prevalent multifactorial chronic disorders, characterized by impaired glucose tolerance and insulin sensitivity, leading to hyperglycemia in both fasting and postprandial states [1,2,3]. It is a rising epidemic of the last century, globally affecting 536.6 million adults aged 20–79 and projected to escalate to 783.2 million by 2045 [4]. The Kingdom of Saudi Arabia (KSA) is not immune to this global epidemic, with an estimated 7 million people in KSA affected by type 2 diabetes mellitus (T2DM) and 3 million with prediabetes [5]. The International Diabetes Federation (IDF) forecasted that the Middle East region would experience the most significant relative growth in the T2DM population, whereas, according to the World Health Organization (WHO), KSA has the 2nd highest T2DM prevalence in the Middle East and 7th highest worldwide [4,5]. This rise in T2DM prevalence is alarming, as it is associated with or triggers various chronic, acute, macro-, and microvascular complications [6,7,8], which significantly impact the quality of life and exert a socioeconomic burden. The estimated global economic burden was 966 billion USD in 2021, which is estimated to possibly escalate to 1054 billion USD by 2045 [4].
Prediabetes is an intermediate stage of T2DM characterized by above normal blood glucose or HbA1c levels, but not high enough to meet the diabetes threshold [9]. It has been linked to a higher risk of chronic diabetes-related complications [10,11]. The rapid industrialization and urbanization in KSA resulted in a notable rise in living standards, leading to a Westernized lifestyle where unhealthy food patterns and limited physical activity dominate. Moreover, age, obesity, and a sedentary lifestyle are the conventional risk factors for T2DM [8,12,13]. Additionally, the Saudi population appears to have a genetic predisposition to T2DM, which might be due to the high prevalence of consanguineous marriages and gestational diabetes mellitus (GDM) [14,15]. Screening plays a significant role, as prediabetes is amenable to interventions that prevent/delay the transition to overt T2DM and reduce the risk of T2DM-related complications [16,17,18]. Aside from screening, a multidimensional approach is desperately needed to holistically understand this disease. Studying gene variants that affect DM phenotypes might be an effective tool in predicting and preventing prediabetes and related complications.
In a milestone meta-analysis review involving 21 genome-wide association studies covering almost 123,000 individuals, with a replication set involving another 76,000 individuals, several genetic loci were identified with direct associations to glycemic traits, glucose homeostasis, and insulin resistance [19]. These novel loci were successfully replicated in a homogenous Chinese population, thus reinforcing their impact on diabetes risk [20]. 4Consequently, we previously assessed the association of these genetic loci with T2DM risk based on identified genetic variants that increase T2DM susceptibility in other populations and found that nine variants [WFS1, JAZF1, SLC30A8, CDKN2A/B, TCF7L2, KCNQ1, HMG20A, HNF4A, and DUSP9] were associated with T2DM in the Saudi population [21]. In addition, the same previously genotyped cohort was also investigated for its possible link to obesity, revealing only five allelic variants [FTO and TCF7L2 genes] out of 37 were associated with obesity in the Saudi Arabian population [22]. Given this information, it makes sense to further examine the individual and cumulative influences of these genetic variants on the development and susceptibility to prediabetes, as this intermediate stage to T2DM has yet to be investigated in the Arabian population. Given that genetic testing and interventional gene therapy is rapidly taking shape as an emerging field in medicine, genetic variant studies are needed to fully understand the molecular basis of common human disorders, including insulin-resistant diseases and, in this, case, intermediate stages (prediabetes) of harder outcomes (T2DM). Therefore, the rationale of the current study was to explore the association of previously established 37 SNPs with prediabetes susceptibility in the Saudi Arabian population.

2. Materials and Methods

2.1. Study Design and Population

In this cross-sectional study, 1129 Saudi adults (332 with prediabetes and 797 with normoglycemia) aged between 30 and 60 years were randomly selected from the Biomarker screening project in Riyadh (RIYADH COHORT) [21]. This is a capital-wide epidemiological study comprising over 17,000 consenting Saudis recruited from various Primary Health Care Centers (PHCCs) in Riyadh, KSA. Demographic data and medical history were obtained through a self-administered general questionnaire. Moreover, written informed consent was obtained from all participants before inclusion in the study. Subjects taking anti-diabetic drugs or any medication known to affect glucose homeostasis were excluded from this study. Additionally, subjects who were morbidly obese, had thyroid disorders, including history of hyperparathyroidism, hypercalcemia, chronic kidney disease, or significantly affected with comorbidities that would interfere with study participation were excluded from the study. The study was conducted in accordance with the Declaration of Helsinki. Permission to collect samples from the different PHCCs were provided by the Ministry of Health, General Directorate of Affairs in Riyadh, KSA (No. 74191, dated Hijri 25/05/1434, corresponding to 13 April 2013).

2.2. Biochemical Analysis

Participants were requested to report to their allocated PHCCs after an overnight fast (>10 h) for anthropometric analysis and blood sampling. Peripheral blood was obtained in EDTA tubes for DNA extraction, while plain tubes were used to collect blood for serum analysis. Extracted serum and EDTA tubes were transferred to the Chair for Biomarkers of Chronic Diseases (CBCD) laboratory and stored at −20 °C until further analysis. Various anthropometric parameters were recorded, as mentioned in our previous study [23]. Fasting glucose and lipid profile (high- and low-density lipoprotein cholesterol, total cholesterol, and triglycerides) were measured routinely using an autoanalyzer (Konelab, Vantaa, Finland) [24]. Pro-inflammatory cytokines, including tumor necrosis factor α (TNF-α) and interleukins IL6 and IL1β, were measured using commercially available multiplex immunoassay kits that utilize the Luminex xMAP technology platform (Luminex Corporation, Austin, TX, USA), which enables simultaneous analysis of multiple biomarkers in human serum. For TNFα: intra-assay <10% coefficient of variation (CV), inter-assay <20% CV. For IL-6: intra-assay <10% CV, inter-assay <15% CV. For IL-1β: intra-assay <10% CV, inter-assay <15% CV. C-Reactive protein levels were quantified using a commercial enzyme-linked immunosorbent assay (ELISA) kit (Human C-Reactive Protein/CRP Quantikine ELISA Kit, R&D systems, Minneapolis, MN, USA) following the manufacturer’s instructions.

2.3. Prediabetes Screening

The operational definition of prediabetes used in the present study was based on the cut-off provided by the American Diabetes Association (ADA), which is a fasting blood glucose level of 5.6–6.9 mmol/L (100–125 mg/dL) [25]. The fasting blood glucose level was measured using an automatic biochemical analyzer. For the purpose of this study, the fasting blood glucose was preferred over a 2 h oral glucose tolerance test (for the diagnosis of impaired glucose tolerance) since it is more practical for large-scale screening.

2.4. Genotyping

Genomic DNA was extracted from the blood using the Blood Genomic Prep Mini Spin Kit (GE Healthcare, Chicago, IL, USA). A Nanodrop spectrophotometer (ND-1000, NanoDrop Technologies by Thermo Fisher Scientific, Wilmington, DE, USA) was used to quantify the concentrations of purified DNA (260/280). The 37 SNPs (rs7903146, rs5015480, rs12779790, rs10923931, rs10440833, rs11899863, rs13081389, rs3802177, rs849134, rs5215, rs1470579, rs6795735, rs1387153, rs243021, rs7578326, rs4457053, rs972283, rs896854, rs13292136, rs2311362, rs1552224, rs7957197, rs11634397, rs8042680, rs5945326, rs163184, rs4430796, rs4812829, rs1802295, rs7178572, rs2028299, rs3923113, rs16861329, rs1531343, rs1801214, rs10965250, and rs11642841) were evaluated in prediabetes subjects and their normoglycemic counterparts using the KASPar method (KbioScience, Hoddesdon, UK), with a genotype success rate of 99.1% according to our earlier described work [21].

2.5. Statistical Analysis

Data was analyzed using SPSS version 21.0 software. Categorical variables were presented as N (%). Hardy–Weinberg (HW) distribution was assessed for the genotypes in the prediabetes group and their healthy counterparts. Normal quantitative variables were presented as mean (SD) and non-normal quantitative variables were presented as median (quartile 1–quartile 3). The independent samples t-test and Mann-Whitney U-test were used to determine statistical differences between normal and prediabetes subjects for normal and non-normal quantitative variables, respectively. The Kruskal–Wallis test was used to determine statistical differences between SNPs for respective quantitative variables. Bonferroni corrections were used to adjust for multiple comparison. Logistics regression was used to determine the association between prediabetes and SNPs. Furthermore, the effects of covariates including age, gender, and BMI, were removed to obtain the adjusted odds ratios (OR) with 95% confidence interval (CI). A p value < 0.05 was considered statistically significant.

3. Results

3.1. General Characteristics

The anthropometric, clinical, and biochemical characteristics of the studied population according to prediabetes status are shown in Table 1. The prevalence of prediabetes in the studied population was 29.4%. The prediabetes group was significantly older than the control group (p < 0.01) and while the percentage of males was higher in the prediabetes group (45%) than in the control group (38%) (p = 0.03), prediabetes was more common in women than men. Measurements of weight, BMI, waist, hips, waist-hip ratio (WHR), fasting glucose, and triglycerides were significantly higher in prediabetes subjects than in controls (p < 0.01). No significant differences were seen between the prediabetes and control groups in terms of pro-inflammatory cytokines measured.

3.2. Association of T2DM-Related Genetic Variants with the Occurrence of Prediabetes

A logistic regression analysis of the genotypes with the five SNPs is presented in Table 2. Prediabetes risk increased by 57% among participants with the homozygous AA genotype of rs11642841 (FTO) compared to the CC genotype (p = 0.02). After adjusting for age, gender, and BMI, the risk was reduced to 50%. Furthermore, heterozygous GA of rs4812829 (HNF4A), rs5945326 (DUSP9), and rs11634397 (ZFAND6), along with heterozygous TC of rs1801214 (WFS1), were associated with a decreased risk for prediabetes.
The relationship between the 37 T2DM-related SNP loci and predisposition to prediabetes was assessed by applying a logistic regression model using age, gender, and BMI as covariates. Among the 37 SNPs, five SNPs, including FTO (rs11642841), HNF4A (rs4812829), WFS1 (rs1801214), DUSP9 (rs5945326), and ZFAND6 (rs11634397), showed significant associations with prediabetes (p-values = 0.03, <0.01, <0.01, 0.01, 0.05, respectively) (Supplementary Table S1).
Genotype frequencies of all the significant SNPs (rs11642841, rs4812829, rs1801214, and rs11634397) did not deviate from Hardy-Weinberg equilibrium in our population except for rs5945326 (Supplementary Table S2).

3.3. Association of Five Selected Genetic Variants with Anthropometric Measures

Table 3 shows the median and quartiles of anthropometric data according to the studied polymorphisms. The median and quartiles of weight (p = 0.04) and BMI (p = 0.02) were significantly higher in the AA genotype than in the CC genotype of rs11642841 (p < 0.05). In addition, the median and quartiles of WHR in the GG genotype were higher than in the GA and AA genotypes of rs5945326 (p < 0.01).

3.4. Association of Five Selected Genetic Variants with Inflammatory Markers

We assessed the association of the five selected SNPs with various inflammatory markers (Table 4). In rs11634397, CRP levels of the homozygous genotype (AA) with a median of 5389.0 (2767.4–7412.8) were significantly higher than those of the heterozygous GA genotype with a median of 1736.3 (1024.4–4452.0) (p < 0.01). Additionally, TNF-α, CRP, and IL-1β levels were associated with rs11634397, rs4812829, and rs1801214, respectively. This significance was lost in post-hoc analysis.

4. Discussion

Epidemiological data reveal that approximately 5–10% of prediabetes subjects will develop diabetes each year and an equal percentage will return to normal [9]. In the past three decades, reports from KSA suggest a ten-fold rise in diabetes prevalence, which is anticipated to rise globally [5]. Identifying genetic markers potentially enables early detection and reduces the risk of T2DM prognosis and related complications. This study was primarily aimed at exploring the association of 37 T2DM-related genetic variants with prediabetes. These variants of interest conferred susceptibility to T2DM in European and South Asian diabetes populations [26,27] and were subsequently replicated in Saudi Arabian ethnic groups [21,22]. The current study revealed that five out of 37 genetic variants were associated with prediabetes and inflammation among Saudi Arabian adults. Interesting to note was the high prevalence of prediabetes (29%) in the group and the anticipated worse cardiometabolic profile of individuals with prediabetes compared to controls, including being significantly older and the substantially higher prevalence in women.
Hepatocyte nuclear factor 4-α (HNF4A) regulates hepatic gluconeogenesis and insulin secretion. It belongs to the nuclear receptor superfamily and plays a crucial role in glucose homeostasis in pancreatic β cells and the liver [28,29]. The corresponding gene is located on chromosome 20q13 and is directly implicated in insulin gene expression [28]. The present study is the first to report an association between the HNF4A gene variant (rs4812829) with prediabetes, suggesting that the heterozygous genotype (GA) is protective of prediabetes risk in Saudi Arabian adults. However, Wang et al. tested and linked P2 promoter polymorphism rs1884613 of HNF4A with prediabetes susceptibility in the Chinese Han population [30]. In other populations, a genome-wide association (GWA) study reported that the risk allele of rs4812829 was significantly associated with T2DM and (GDM) risk in a South Asian cohort [31,32]. Conversely, rs4812829 has also been associated with obesity [33,34]. Moreover, multiple studies in different ethnicities have linked T2DM susceptibility with HNF4A variants [29,31,35,36,37]. Interestingly, it was shown that HNF4A variants play a role in type I maturity-onset diabetes of the young (MODY) by impairing insulin sensitivity and β-cell function [38].
WFS1 encodes several proteins, including Wolframin, which is embedded in the endoplasmic reticulum membrane. It is widely expressed across various organs, particularly in the brain and pancreas [39]. Several studies have linked variations in the WFS1 gene to Wolfram Syndrome, an autosomal recessive disorder, and T2DM susceptibility [40,41,42,43]. In mice, WFS1 disruption resulted in increased glucose intolerance and insulin deficiency [44]. However, the underlying effect of these variants on the prediabetes phenotype has not been explored much. In the current study, heterozygous TC of the WFS1-rs1801214 variant located in the coding sequence showed a statistically significant association with prediabetes (0.60; 95% CI 0.44–0.80; p < 0.01). To the best of our knowledge, no studies have shown a relationship between the WFS1-rs1801214 variant and prediabetes risk; however, Sparsø et al. revealed the interplay between other variants in WFS1 (rs734312, rs10010131) and the prediabetes phenotype [45].
DUSP9 and ZFAND6 are expressed in various tissues with a significant role in glucose homeostasis. The current study revealed that their variants, including the GA heterozygous of DUSP9 (rs5945326) and GG heterozygous of ZFAND6 (rs11634397) genotypes, contributed to decreased risk of developing prediabetes. The GA heterozygous of rs5945326 genotype was also associated with higher waist measurement and WHR. Moreover, the AA homozygous genotype had significantly higher CRP levels than the GA heterozygous genotype. These gene loci were associated with T2DM and β-cell dysfunction and are believed to play a prominent role in protecting against versus developing insulin resistance. There was no prior knowledge available revealing the impact of the various genotypes studied in the current study, including heterozygous GA of HNF4A-rs4812829, (0.64; 95% CI 0.47–0.86; p < 0.01), heterozygous TC of WFS1-rs1801214 (0.60; 95% CI 0.44–0.80; p < 0.01), heterozygous GA of DUSP9-rs5945326 (0.60; 95% CI 0.39–0.92; p = 0.01), heterozygous GA of ZFAND6-rs11634397 (0.75; 95% CI 0.56–1.01; p = 0.05), and homozygous AA of FTO-rs11642841, on prediabetes susceptibility.
FTO located on 16q12.2 is substantially associated with elevated basal metabolic rate and T2DM [46]. Numerous FTO gene variants have been reported with an amplified contributory effect on T2DM and obesity [47,48]. However, our study showed that SNP rs1164284 had the most substantial susceptibility to prediabetes in the Arab population. Furthermore, among obesity-related traits, weight, BMI, and waist were recognized to be the most significantly associated with the homozygous genotype AA compared to the CC genotype. Importantly, findings from our previous study and other research are concurrent and support the current study outcomes, which suggests that BMI and waist circumference could be potential T2DM predictors. Among the various anthropometric parameters, WC was found to be a significant early marker of T2DM [22,46,49].
Several variants in the same or different genes act synergistically and affect diabetes phenotypes. Initially, the association of 37 T2DM-related SNPs with T2DM was reported in the European population. Subsequently, we replicated and identified around nine loci, with a significant effect on the development of T2DM in the Saudi Arabian population [21]. However, no study has shown the role/relationship of these SNPs in the development of prediabetes. For the first time, we found five loci with an independent effect on prediabetes susceptibility. It is not surprising that the risk alleles of these SNPs are all associated with pancreatic β-cell dysfunction. Even though the significance of prediabetes has been highly underscored, very few studies have assessed the diabetogenic impact of genetic variants.

5. Limitation

The authors acknowledge certain limitations. Fasting glucose instead of a 2 h glucose tolerance test was used for prediabetes screening; hence, there is a risk of categorizing individuals with impaired glucose tolerance under the normoglycemic group. Nevertheless, the use of fasting glucose was justified as it is more practical for screening large numbers of participants. Additionally, the subjects of the current study are of Saudi Arabian ethnicity; hence, the outcomes of this research might not apply to other populations. However, the study’s strength includes the homogeneity of the population, providing first-hand evidence of the association of the studied SNPs with prediabetes in individuals of a homogenous Arab ethnic group.

6. Conclusions

In summary, we found significant associations between prediabetes risk and five variants closely related to the FTO, HNF4A, WFS1, DUSP9, and ZFAND6 genes (rs4812829, rs1801214, rs5945326, rs11642841, and rs11634397) among Saudi Arabian adults. Prospective studies involving metabolically healthy individuals should be conducted to assess the true value of the investigated polymorphisms as risk factors for prediabetes and T2DM.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/2073-4425/14/3/536/s1, Table S1: Association between SNPs and prediabetes status; Table S2: Hardy-Weinberg equilibrium.

Author Contributions

Conceptualization, N.M.A.-D.; methodology, S.S.; formal analysis, S.D.H.; Investigation, M.G.A.A., writing—original draft preparation, M.G.A.A. and D.N.B.; writing—review and editing, D.N.B., S.S., A.M.A., M.S.A. and A.A.A.-M.; validation, D.N.B.; supervision, A.M.A., M.S.A., A.A.A.-M. and N.M.A.-D.; project administration, S.S. and A.A.A.-M.; funding acquisition, A.A.A.-M. All authors have read and agreed to the published version of the manuscript.

Funding

This study is supported by the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia, which funded this research work through project number IFKSU-2-1603.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was from the Institutional Review Board (IRB) of King Fahad Medical City, Riyadh, Saudi Arabia (IRB Log No: 13-094, 14 January 2019).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data is available upon request to the corresponding author.

Acknowledgments

The authors thank all nurses and study coordinators who helped screen participants, blood, and data collection.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Al-Daghri, N.M.; Abdi, S.; Sabico, S.; Alnaami, A.M.; Wani, K.A.; Ansari, M.G.A.; Khattak, M.N.K.; Khan, N.; Tripathi, G.; Chrousos, G.P.; et al. Gut-Derived Endotoxin and Telomere Length Attrition in Adults with and without Type 2 Diabetes. Biomolecules 2021, 11, 1693. [Google Scholar] [CrossRef] [PubMed]
  2. Al-Disi, D.; Ansari, M.G.A.; Sabico, S.; Wani, K.; Hussain, S.D.; Elshafie, M.M.; McTernan, P.; Al-Daghri, N.M. High glucose load and endotoxemia among overweight and obese Arab women with and without diabetes: An observational study. Medicine 2020, 99, e23211. [Google Scholar] [CrossRef] [PubMed]
  3. Al-Daghri, N.M.; Al-Attas, O.S.; Alkharfy, K.M.; Khan, N.; Mohammed, A.K.; Vinodson, B.; Ansari, M.G.; Alenad, A.; Alokail, M.S. Association of VDR-gene variants with factors related to the metabolic syndrome, type 2 diabetes and vitamin D deficiency. Gene 2014, 542, 129–133. [Google Scholar] [CrossRef]
  4. Sun, H.; Saeedi, P.; Karuranga, S.; Pinkepank, M.; Ogurtsova, K.; Duncan, B.B.; Stein, C.; Basit, A.; Chan, J.C.N.; Mbanya, J.C.; et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res. Clin. Pract. 2022, 183, 109119. [Google Scholar] [CrossRef] [PubMed]
  5. Robert, A.A.; Al Dawish, M.A.; Braham, R.; Musallam, M.A.; Al Hayek, A.A.; Al Kahtany, N.H. Type 2 Diabetes Mellitus in Saudi Arabia: Major Challenges and Possible Solutions. Curr. Diabetes Rev. 2017, 13, 59–64. [Google Scholar] [CrossRef]
  6. Veronese, N.; Cooper, C.; Reginster, J.Y.; Hochberg, M.; Branco, J.; Bruyère, O.; Chapurlat, R.; Al-Daghri, N.; Dennison, E.; Herrero-Beaumont, G.; et al. Type 2 diabetes mellitus and osteoarthritis. Semin. Arthritis. Rheum. 2019, 49, 9–19. [Google Scholar] [CrossRef]
  7. Ansari, P.; Hannan, J.M.A.; Azam, S.; Jakaria, M. Challenges in Diabetic Micro-Complication Management: Focus on Diabetic Neuropathy. Int. J. Transl. Med. 2021, 1, 175–186. [Google Scholar] [CrossRef]
  8. Wu, Y.; Ding, Y.; Tanaka, Y.; Zhang, W. Risk factors contributing to type 2 diabetes and recent advances in the treatment and prevention. Int. J. Med. Sci. 2014, 11, 1185–1200. [Google Scholar] [CrossRef]
  9. Tabák, A.G.; Herder, C.; Rathmann, W.; Brunner, E.J.; Kivimäki, M. Prediabetes: A high-risk state for diabetes development. Lancet 2012, 379, 2279–2290. [Google Scholar] [CrossRef]
  10. Schlesinger, S.; Neuenschwander, M.; Barbaresko, J.; Lang, A.; Maalmi, H.; Rathmann, W.; Roden, M.; Herder, C. Prediabetes and risk of mortality, diabetes-related complications and co-morbidities: Umbrella review of meta-analyses of prospective studies. Diabetologia 2022, 65, 275–285. [Google Scholar] [CrossRef]
  11. Mutie, P.M.; Pomares-Millan, H.; Atabaki-Pasdar, N.; Jordan, N.; Adams, R.; Daly, N.L.; Tajes, J.F.; Giordano, G.N.; Franks, P.W. An investigation of causal relationships between Prediabetes and vascular complications. Nat. Commun. 2020, 11, 4592. [Google Scholar] [CrossRef] [PubMed]
  12. Ismail, L.; Materwala, H.; Al Kaabi, J. Association of risk factors with type 2 diabetes: A systematic review. Comput. Struct. Biotechnol. J. 2021, 19, 1759–1785. [Google Scholar] [CrossRef]
  13. Al-Hazzaa, H.M. Physical inactivity in Saudi Arabia revisited: A systematic review of inactivity prevalence and perceived barriers to active living. Int. J. Health Sci. 2018, 12, 50–64. [Google Scholar]
  14. Alzahrani, S.H.; Alzahrani, N.M.; Al Jabir, F.M.; Alsharef, M.K.; Zaheer, S.; Hussein, S.H.; Alguwaihes, A.M.; Jammah, A.A. Consanguinity and Diabetes in Saudi Population: A Case-Control Study. Cureus 2021, 13, e20836. [Google Scholar] [CrossRef] [PubMed]
  15. Aljulifi, M.Z. Prevalence and reasons of increased type 2 diabetes in Gulf Cooperation Council Countries. Saudi Med. J. 2021, 42, 481–490. [Google Scholar] [CrossRef]
  16. Braga, T.; Kraemer-Aguiar, L.G.; Docherty, N.G.; Le Roux, C.W. Treating Prediabetes: Why and how should we do it? Minerva Med. 2019, 110, 52–61. [Google Scholar] [CrossRef]
  17. Amer, O.E.; Sabico, S.; Alfawaz, H.A.; Aljohani, N.; Hussain, S.D.; Alnaami, A.M.; Wani, K.; Al-Daghri, N.M. Reversal of Prediabetes in Saudi Adults: Results from an 18 Month Lifestyle Intervention. Nutrients 2020, 12, 804. [Google Scholar] [CrossRef]
  18. Ansari, M.G.A.; Sabico, S.; Clerici, M.; Khattak, M.N.K.; Wani, K.; Al-Musharaf, S.; Amer, O.E.; Alokail, M.S.; Al-Daghri, N.M. Vitamin D Supplementation Is Associated with Increased Glutathione Peroxidase-1 Levels in Arab Adults with Prediabetes. Antioxidants 2020, 9, 118. [Google Scholar] [CrossRef]
  19. Dupuis, J.; Langenberg, C.; Prokopenko, I.; Saxena, R.; Soranzo, N.; Jackson, A.; Wheeler, E.; Glazer, N.; Bouatia-Naji, N.; Gloyn, A.; et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 2010, 42, 105–116. [Google Scholar] [CrossRef]
  20. Hu, C.; Zhang, R.; Wang, C.; Wang, J.; Ma, X.; Hou, X.; Lu, J.; Yu, W.; Jiang, F.; Bao, Y.; et al. Variants from GIPR, TCF7L2, DGKB, MADD, CRY2, GLIS3, PROX1, SLC30A8 and IGF1 are associated with glucose metabolism in the Chinese. PLoS ONE 2010, 5, e15542. [Google Scholar] [CrossRef]
  21. Al-Daghri, N.M.; Alkharfy, K.M.; Alokail, M.S.; Alenad, A.M.; Al-Attas, O.S.; Mohammed, A.K.; Sabico, S.; Albagha, O.M. Assessing the contribution of 38 genetic loci to the risk of type 2 diabetes in the Saudi Arabian Population. Clin. Endocrinol. 2014, 80, 532–537. [Google Scholar] [CrossRef] [PubMed]
  22. Al-Daghri, N.M.; Alkharfy, K.M.; Al-Attas, O.S.; Krishnaswamy, S.; Mohammed, A.K.; Albagha, O.M.; Alenad, A.M.; Chrousos, G.P.; Alokail, M.S. Association between type 2 diabetes mellitus-related SNP variants and obesity traits in a Saudi population. Mol. Biol. Rep. 2014, 41, 1731–1740. [Google Scholar] [CrossRef] [PubMed]
  23. Abdi, S.; Binbaz, R.A.; Mohammed, A.K.; Ansari, M.G.A.; Wani, K.; Amer, O.E.; Alnaami, A.M.; Aljohani, N.; Al-Daghri, N.M. Association of RANKL and OPG Gene Polymorphism in Arab Women with and without Osteoporosis. Genes 2021, 12, 200. [Google Scholar] [CrossRef]
  24. Al-Othman, A.; Al-Musharaf, S.; Al-Daghri, N.M.; Yakout, S.; Alkharfy, K.M.; Al-Saleh, Y.; Al-Attas, O.S.; Alokail, M.S.; Moharram, O.; Sabico, S.; et al. Tea and coffee consumption in relation to vitamin D and calcium levels in Saudi adolescents. Nutr. J. 2012, 11, 56. [Google Scholar] [CrossRef] [PubMed]
  25. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2014, 37 (Suppl. S1), S81–S90. [Google Scholar] [CrossRef] [PubMed]
  26. Scott, L.J.; Mohlke, K.L.; Bonnycastle, L.L.; Willer, C.J.; Li, Y.; Duren, W.L.; Erdos, M.R.; Stringham, H.M.; Chines, P.S.; Jackson, A.U.; et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 2007, 316, 1341–1345. [Google Scholar] [CrossRef]
  27. Rung, J.; Cauchi, S.; Albrechtsen, A.; Shen, L.; Rocheleau, G.; Cavalcanti-Proença, C.; Bacot, F.; Balkau, B.; Belisle, A.; Borch-Johnsen, K.; et al. Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nat. Genet. 2009, 41, 1110–1115. [Google Scholar] [CrossRef]
  28. Bartoov-Shifman, R.; Hertz, R.; Wang, H.; Wollheim, C.B.; Bar-Tana, J.; Walker, M.D. Activation of the insulin gene promoter through a direct effect of hepatocyte nuclear factor 4 α. J. Biol. Chem. 2002, 277, 25914–25919. [Google Scholar] [CrossRef]
  29. Silander, K.; Mohlke, K.L.; Scott, L.J.; Peck, E.C.; Hollstein, P.; Skol, A.D.; Jackson, A.U.; Deloukas, P.; Hunt, S.; Stavrides, G.; et al. Genetic variation near the hepatocyte nuclear factor-4 α gene predicts susceptibility to type 2 diabetes. Diabetes 2004, 53, 1141–1149. [Google Scholar] [CrossRef]
  30. Wang, C.; Chen, S.; Zhang, T.; Chen, Z.; Liu, S.; Peng, X.; Ma, J.; Zhong, X.; Yan, Y.; Tang, L.; et al. Prediabetes Is Associated with HNF-4α P2 Promoter Polymorphism rs1884613: A Case-Control Study in Han Chinese Population and an Updated Meta-Analysis. Dis. Markers 2014, 2014, 231736. [Google Scholar] [CrossRef]
  31. Kooner, J.S.; Saleheen, D.; Sim, X.; Sehmi, J.; Zhang, W.; Frossard, P.; Been, L.F.; Chia, K.S.; Dimas, A.S.; Hassanali, N.; et al. Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci. Nat. Genet. 2011, 43, 984–989. [Google Scholar] [CrossRef] [PubMed]
  32. Kanthimathi, S.; Chidambaram, M.; Bodhini, D.; Liju, S.; Bhavatharini, A.; Uma, R.; Anjana, R.M.; Mohan, V.; Radha, V. Association of recently identified type 2 diabetes gene variants with Gestational Diabetes in Asian Indian population. Mol. Genet. Genom. 2017, 292, 585–591. [Google Scholar] [CrossRef] [PubMed]
  33. Shabana; Ullah Shahid, S.; Wah Li, K.; Acharya, J.; Cooper, J.A.; Hasnain, S.; Humphries, S.E. Effect of six type II diabetes susceptibility loci and an FTO variant on obesity in Pakistani subjects. Eur. J. Hum. Genet. 2016, 24, 903–910. [Google Scholar] [CrossRef] [PubMed]
  34. Ashour, E.; Gouda, W.; Mageed, L.; Afify, M.; Hamimy, W.; Shaker, Y.M. Evaluation of genetic susceptibility of six type II diabetes Genome-Wide association tudies loci for obesity. Meta Gene 2020, 26, 100758. [Google Scholar] [CrossRef]
  35. Mohlke, K.L.; Boehnke, M. The role of HNF4A variants in the risk of type 2 diabetes. Curr. Diab. Rep. 2005, 5, 149–156. [Google Scholar] [CrossRef]
  36. Cho, Y.S.; Chen, C.H.; Hu, C.; Long, J.; Ong, R.T.; Sim, X.; Takeuchi, F.; Wu, Y.; Go, M.J.; Yamauchi, T.; et al. Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat. Genet. 2011, 44, 67–72. [Google Scholar] [CrossRef]
  37. Lehman, D.M.; Richardson, D.K.; Jenkinson, C.P.; Hunt, K.J.; Dyer, T.D.; Leach, R.J.; Arya, R.; Abboud, H.E.; Blangero, J.; Duggirala, R.; et al. P2 promoter variants of the hepatocyte nuclear factor 4alpha gene are associated with type 2 diabetes in Mexican Americans. Diabetes 2007, 56, 513–517. [Google Scholar] [CrossRef]
  38. Yamagata, K.; Oda, N.; Kaisaki, P.J.; Menzel, S.; Furuta, H.; Vaxillaire, M.; Southam, L.; Cox, R.D.; Lathrop, G.M.; Boriraj, V.V.; et al. Mutations in the hepatocyte nuclear factor-1α gene in maturity-onset diabetes of the young (MODY3). Nature 1996, 384, 455–458. [Google Scholar] [CrossRef]
  39. Inoue, H.; Tanizawa, Y.; Wasson, J.; Behn, P.; Kalidas, K.; Bernal-Mizrachi, E.; Mueckler, M.; Marshall, H.; Donis-Keller, H.; Crock, P.; et al. A gene encoding a transmembrane protein is mutated in patients with diabetes mellitus and optic atrophy (Wolfram syndrome). Nat. Genet. 1998, 20, 143–148. [Google Scholar] [CrossRef]
  40. Alfaifi, M. Interaction between rs6446482 polymorphisms in the WFS1 gene in type 2 diabetes patients. J. King Saud Univ. Sci. 2022, 34, 101721. [Google Scholar] [CrossRef]
  41. Mair, H.; Fowler, N.; Papatzanaki, M.E.; Sudhakar, P.; Maldonado, R.S. Novel missense WFS1 variant causing autosomal dominant atypical Wolfram syndrome. Ophthalmic Genet. 2022, 43, 567–572. [Google Scholar] [CrossRef] [PubMed]
  42. Deng, H.; Zhang, J.; Zhu, F.; Deng, X.; Yuan, L. Identification of the rare variant p.Val803Met of WFS1 gene as a cause of Wolfram-like syndrome in a Chinese family. Acta Diabetol. 2020, 57, 1399–1404. [Google Scholar] [CrossRef]
  43. Sandhu, M.S.; Weedon, M.N.; Fawcett, K.A.; Wasson, J.; Debenham, S.L.; Daly, A.; Lango, H.; Frayling, T.M.; Neumann, R.J.; Sherva, R.; et al. Common variants in WFS1 confer risk of type 2 diabetes. Nat. Genet. 2007, 39, 951–953. [Google Scholar] [CrossRef] [PubMed]
  44. Ishihara, H.; Takeda, S.; Tamura, A.; Takahashi, R.; Yamaguchi, S.; Takei, D.; Yamada, T.; Inoue, H.; Soga, H.; Katagiri, H.; et al. Disruption of the WFS1 gene in mice causes progressive β-cell loss and impaired stimulus-secretion coupling in insulin secretion. Hum. Mol. Genet. 2004, 13, 1159–1170. [Google Scholar] [CrossRef] [PubMed]
  45. Sparsø, T.; Andersen, G.; Albrechtsen, A.; Jørgensen, T.; Borch-Johnsen, K.; Sandbæk, A.; Lauritzen, T.; Wasson, J.; Permutt, M.A.; Glaser, B.; et al. Impact of polymorphisms in WFS1 on prediabetic phenotypes in a population-based sample of middle-aged people with normal and abnormal glucose regulation. Diabetologia 2008, 51, 1646–1652. [Google Scholar] [CrossRef] [PubMed]
  46. Shaikh, F.; Shah, T.; Madkhali, N.A.B.; Gaber, A.; Alsanie, W.F.; Ali, S.; Ansari, S.; Rafiq, M.; Sayyed, R.Z.; Rind, N.A.; et al. Frequency distribution and association of Fat-mass and obesity (FTO) gene SNP rs-9939609 variant with Diabetes Mellitus Type-II population of Hyderabad, Sindh, Pakistan. Saudi J. Biol. Sci. 2021, 28, 4183–4190. [Google Scholar] [CrossRef]
  47. Grzeszczak, W.; Molsa, M.; Tłuczykont, M.; Markowicz, A.; Swoboda, R.; Biedak, M.; Kałuża, A.; Sirek, S.; Strojek, K. The age of developing diabetes and FTO polymorphisms (rs9939609, rs1421085, and rs9930506). Endokrynol. Pol. 2017, 68, 402–406. [Google Scholar] [CrossRef]
  48. Chauhan, G.; Tabassum, R.; Mahajan, A.; Dwivedi, O.P.; Mahendran, Y.; Kaur, I.; Nigam, S.; Dubey, H.; Varma, B.; Madhu, S.V.; et al. Common variants of FTO and the risk of obesity and type 2 diabetes in Indians. J. Hum. Genet. 2011, 56, 720–726. [Google Scholar] [CrossRef]
  49. Wang, S.; Ma, W.; Yuan, Z.; Wang, S.M.; Yi, X.; Jia, H.; Xue, F. Association between obesity indices and type 2 diabetes mellitus among middle-aged and elderly people in Jinan, China: A cross-sectional study. BMJ Open 2016, 6, e012742. [Google Scholar] [CrossRef]
Table
Table 1. Anthropometry and Clinical Characteristics of Subjects.
Table 1. Anthropometry and Clinical Characteristics of Subjects.
ParametersControlPrediabetesp-Values
N797 (70.6)332 (29.4)
Age (years)41.5 ± 12.645.7 ± 13.9<0.01
Male/Female301/496149/1830.03
Weight (kg)72.0 (62.0–83.0)77.0 (67.0–88.0)<0.01
BMI (kg/m2)27.5 (24.3–31.6)29.0 (25.4–33.8)<0.01
WHR0.88 (0.81–0.94)0.89 (0.84–0.97)<0.01
Waist (cm)90.0 (79.0–101.0)95.0 (83.0–104.0)<0.01
Hip (cm)103.0 (94.0–112.0)106.0 (95.0–115.0)0.011
HDL-cholesterol (mmol/L)0.9 ± 0.40.9 ± 0.30.11
LDL-cholesterol (mmol/L)3.4 ± 1.03.4 ± 1.00.65
Total cholesterol (mmol/L)5.0 ± 1.05.1 ± 1.00.47
Triglycerides (mmol/L)1.3 (1.0–1.9)1.5 (1.1–2.0)<0.01
Fasting Glucose (mmol/L)4.9 (4.5–5.3)5.9 (5.7–6.0)<0.01
TNF-α (pg/mL)5.1 (1.6–10.2)5.7 (2.6–8.1)0.89
CRP (ng/mL)3147.2 (1250.8–5851.2)2767.4 (1111.7–5780.0)0.54
IL-6 (pg/mL)3.7 (1.9–11.2)2.5 (1.7–6.7)0.48
IL-1β (pg/mL)1.0 (0.8–1.2)1.2 (0.6–2.8)0.61
Note: Data presented as mean ± SD for normal variables and median (Q1–Q3) for non-normal variables. BMI, body mass index; WHR, waist-hip ratio; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TNF-α, tumor necrosis factor α; CRP, C-reactive protein; IL-6, interleukin 6; IL-1β, interleukin 1β; p < 0.05 was considered significant.
Table
Table 2. Association between SNPs and prediabetes status.
Table 2. Association between SNPs and prediabetes status.
SNPsControlPrediabetesUnadjustedAdjusted
OR (95%CI)p-ValueOR (95%CI)p-Value
rs11642841CC332 (42.2)124 (37.6)1 1
CA349 (44.3)144 (43.6)1.10 (0.83–1.47)0.501.08 (0.81–1.45)0.58
AA106 (13.5)62 (18.8)1.57 (1.08–2.28)0.021.50 (1.02–2.21)0.03
rs4812829GG485 (61.6)231 (70.0)1 1
GA273 (34.7)83 (25.2)0.64 (0.48–0.85)<0.010.64 (0.47–0.86)<0.01
AA29 (3.7)16 (4.8)1.16 (0.62–2.18)0.651.17 (0.62–2.22)0.63
rs1801214TT222 (28.4)129 (39.0)1 1
TC405 (51.7)142 (42.9)0.60 (0.45–0.81)<0.010.60 (0.44–0.80)<0.01
CC156 (19.9)60 (18.1)0.66 (0.46–0.96)0.030.67 (0.46–0.98)0.04
rs5945326AA628 (79.5)282 (85.2)1 1
GA122 (15.4)30 (9.1)0.55 (0.36–0.84)<0.010.60 (0.39–0.92)0.01
GG40 (5.1)19 (5.7)1.06 (0.60–1.86)0.841.03 (0.58–1.83)0.92
rs11634397GG244 (30.8)114 (34.4)1 1
GA402 (50.8)142 (42.9)0.76 (0.56–1.01)0.060.75 (0.56–1.01)0.05
AA146 (18.4)75 (22.7)1.10 (0.77–1.57)0.601.06 (0.74–1.53)0.75
Note: Data presented as N (%) and odds ratio (95% CI) obtained from logistic regression; adjusted indicates results adjusted for covariates, i.e., age, gender, and BMI. p-value < 0.05 was considered significant.
Table
Table 3. Anthropometric data according to selected SNPs.
Table 3. Anthropometric data according to selected SNPs.
SNPsWeight (Kg)p-ValueBMI (kg/m2)p-ValueWaist (cm)p-ValueHips (cm)p-ValueWHRp-Value
rs11634397GG73.0 (63.0–85.0)0.1027.8 (24.2–32.0)0.6590.0 (79.0–102.0)0.46102.0 (91.0–112.0)0.160.9 (0.8–1.0)0.11
GA73.5 (63.0–84.0)27.8 (24.9–32.6)90.5 (81.0–102.0)104.0 (95.0–113.0)0.9 (0.8–0.9)
AA75.0 (62.5–84.0)27.8 (24.3–33.2)92.0 (82.0–102.0)104.0 (94.0–114.0)0.9 (0.8–0.9)
rs5945326AA74.0 (64.0–85.0)0.0627.8 (24.6–32.5)0.1092.0 (81.0–102.0)<0.01104.0 (94.0–113.0)0.570.9 (0.8–0.9)<0.01 AB
GA70.0 (60.0–82.0)27.6 (24.2–32.9)86.0 (74.0–98.3)103.0 (94.0–113.0)0.8 (0.8–0.9)
GG76.5 (64.0–86.0)28.0 (25.2–32.4)96.0 (80.0–105.0)B102.0 (90.0–113.0)0.9 (0.9–1.0)
rs4812829GG73.5 (63.0–84.5)0.8027.8 (24.8–32.7)0.5191.0 (80.0–102.0)0.35104.0 (95.0–113.0)0.700.9 (0.8–0.9)0.35
GA73.0 (62.0–84.0)27.6 (24.2–32.2)92.0 (81.0–103.0)103.0 (94.0–113.0)0.9 (0.8–0.9)
AA76.5 (65.5–84.5)28.2 (25.3–32.4)88.0 (79.0–100.0)103.0 (88.0–112.0)0.9 (0.8–0.9)
rs1801214TT71.3 (62.0–84.0)0.3827.4 (24.3–32.3)0.4091.0 (80.0–102.0)0.53102.0 (93.5–112.0)0.480.9 (0.8–0.9)0.60
TC74.0 (64.5–84.5)28.0 (24.9–32.7)92.0 (81.0–103.0)104.0 (94.0–114.0)0.9 (0.8–0.9)
CC76.0 (64.0–85.0)28.1 (24.6–32.4)90.0 (80.0–100.5)104.5 (95.0–113.0)0.9 (0.8–0.9)
rs11642841CC72.0 (63.0–84.0)0.0427.5 (24.2–31.8)0.02 A89.5 (79.0–102.0)0.06102.0 (93.0–112.0)0.080.9 (0.8–0.9)0.29
CA73.0 (62.2–83.0)27.7 (24.7–32.0)91.0 (81.0–101.0)104.0 (95.0–113.0)0.9 (0.8–0.9)
AA77.5 (65.0–88.0)A29.8 (25.3–34.4)95.0 (82.0–104.0)106.5 (95.0–114.0)0.9 (0.8–1.0)
Note: Data presented as median (1st quartile–3rd quartile); p-values were obtained from Kruskal–Wallis test; p < 0.05 considered significant. Superscripts A and B indicate significance from 1st and 2nd genotypes, respectively.
Table
Table 4. Inflammatory markers according to selected SNPs.
Table 4. Inflammatory markers according to selected SNPs.
SNPsTNF-α (pg/mL)p-ValueCRP (ng/mL)p-ValueIL-6 (Pg/mL)p-ValueIL-1β (Pg/mL)p-Value
rs11634397GG6.4 (3.6–10.2)0.053043.6 (832.8–7571.3)<0.011.3 (0.7–3.7)0.150.8 (0.7–2.0)0.67
GA4.4 (1.3–8.1)1736.3 (1024.4–4452.0)4.8 (2.1–9.5)1.2 (0.8–1.7)
AA6.2 (2.2–12.0)5389.0 (2767.4–7412.8) B5.6 (3.5–105.6)0.8 (0.7–12.9)
rs5945326AA6.1 (2.2–8.7)0.102841.4 (1178.9–5780.0)0.843.6 (1.7–7.0)0.421.0 (0.7–1.7)0.51
GA3.5 (1.1–4.7)3019.2 (957.7–5780.0)3.2 (0.9–22.4)1.2 (1.0–1.5)
GG6.5 (0.9–12.3)4822.6 (2505.9–6129.5)14.3 (14.3–14.3)1.5 (0.8–2.2)
rs4812829GG5.9 (2.1–9.9)0.522841.4 (1286.8–5780.0)0.055.2 (1.7–10.5)0.881.2 (0.8–1.7)0.84
GA5.4 (2.0–7.9)3888.5 (1111.7–7884.9)3.6 (1.7–7.7)0.9 (0.6–3.0)
AA2.7 (1.7–6.4)140.9 (121.9–159.9)2.7 (2.6–2.9)1.2 (1.2–1.2)
rs1801214TT6.1 (1.8–8.6)0.153427.2 (1418.9–7412.8)0.484.8 (2.2–6.3)0.080.9 (0.8–1.2)0.05
TC4.1 (1.4–8.4)2615.3 (1024.4–5547.7)2.9 (1.3–7.7)1.0 (0.6–1.7)
CC6.6 (4.2–8.7)2869.1 (1250.8–5279.5)105.6 (5.6–1493.2)12.9 (1.2–98.9)
rs11642841CC6.0 (1.3–9.1)0.713748.1 (1500.0–5780.0)0.543.7 (1.3–7.7)0.581.0 (0.8–2.3)0.93
CA5.3 (2.6–8.7)2581.7 (991.1–5897.2)2.9 (1.3–11.9)1.2 (0.8–1.7)
AA3.0 (1.8–7.9)2767.4 (839.3–7412.8)6.3 (2.1–12.9)0.9 (0.7–1.8)
Note: Data presented as median (1st quartile–3rd quartile); p-values were obtained from Kruskal–Wallis test; p < 0.05 was considered significant. Superscript B indicates significance from 2nd genotype.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Binjawhar, D.N.; Ansari, M.G.A.; Sabico, S.; Hussain, S.D.; Alenad, A.M.; Alokail, M.S.; Al-Masri, A.A.; Al-Daghri, N.M. Genetic Variants of HNF4A, WFS1, DUSP9, FTO, and ZFAND6 Genes Are Associated with Prediabetes Susceptibility and Inflammatory Markers in the Saudi Arabian Population. Genes 2023, 14, 536. https://doi.org/10.3390/genes14030536

AMA Style

Binjawhar DN, Ansari MGA, Sabico S, Hussain SD, Alenad AM, Alokail MS, Al-Masri AA, Al-Daghri NM. Genetic Variants of HNF4A, WFS1, DUSP9, FTO, and ZFAND6 Genes Are Associated with Prediabetes Susceptibility and Inflammatory Markers in the Saudi Arabian Population. Genes. 2023; 14(3):536. https://doi.org/10.3390/genes14030536

Chicago/Turabian Style

Binjawhar, Dalal N., Mohammed G. A. Ansari, Shaun Sabico, Syed Danish Hussain, Amal M. Alenad, Majed S. Alokail, Abeer A. Al-Masri, and Nasser M. Al-Daghri. 2023. "Genetic Variants of HNF4A, WFS1, DUSP9, FTO, and ZFAND6 Genes Are Associated with Prediabetes Susceptibility and Inflammatory Markers in the Saudi Arabian Population" Genes 14, no. 3: 536. https://doi.org/10.3390/genes14030536

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop