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Article

Clinical Implications of MiR128, Angiotensin I Converting Enzyme and Vascular Endothelial Growth Factor Gene Abnormalities and Their Association with T2D

by
Imadeldin Elfaki
1,*,
Rashid Mir
2,
Faisel M. Abu Duhier
2,
Maeidh A. Alotaibi
3,
Adel Ibrahim Alalawy
1,
Jameel Barnawi
2,
Abdullatif Taha Babakr
4,
Mohammad Muzaffar Mir
5,
Faris Altayeb
2,
Hyder Mirghani
6 and
Ehab A. M. Frah
7
1
Department of Biochemistry, Faculty of Science, University of Tabuk, Tabuk 71491, Saudi Arabia
2
Prince and Fahd Bin Sultan Research Chair, Department of Medical Lab Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk 71491, Saudi Arabia
3
King Faisal Medical Complex Laboratory, Ministry of Health, Taif 26521, Saudi Arabia
4
Department of Medical Biochemistry, Faculty of Medicine, Umm Al-Qura University, Makkah 57039, Saudi Arabia
5
Department of Basic Medical Sciences, College of Medicine, University of Bisha, Bisha 61992, Saudi Arabia
6
Internal Medicine and Endocrine, Medical Department, Faculty of Medicine, University of Tabuk, Tabuk 71491, Saudi Arabia
7
Department of Statistics, Faculty of Science, University of Tabuk, Tabuk 71491, Saudi Arabia
*
Author to whom correspondence should be addressed.
Curr. Issues Mol. Biol. 2021, 43(3), 1859-1875; https://doi.org/10.3390/cimb43030130
Submission received: 4 October 2021 / Revised: 25 October 2021 / Accepted: 27 October 2021 / Published: 2 November 2021
(This article belongs to the Section Molecular Medicine)
Browse Figures
Versions Notes

Abstract

:
Type 2 DM (T2D) results from the interaction of the genetic and environmental risk factors. Vascular endothelial growth factor (VEGF), angiotensin I-converting enzyme (ACE), and MicroRNAs (MiRNAs) are involved in important physiological processes. Gene variations in VEGF, ACE and MiRNA genes are associated with diseases. In this study we investigated the associations of the VEGF-2578 C/A (rs699947), VEGF-2549 insertion/deletion (I/D), and ACE I/D rs4646994 and Mir128a (rs11888095) gene variations with T2D using the amplification refractory mutation system PCR (ARMS-PCR) and mutation specific PCR (MSP). We screened 122 T2D cases and 126 healthy controls (HCs) for the rs699947, and 133 T2D cases and 133 HCs for the VEGF I/D polymorphism. For the ACE I/D we screened 152 cases and 150 HCs, and we screened 129 cases and 112 HCs for the Mir128a (rs11888095). The results showed that the CA genotype of the VEGF rs699947 and D allele of the VEGF I/D polymorphisms were associated with T2D with OR =2.01, p-value = 0.011, and OR = 2.42, p-value = 0.010, respectively. The result indicated the D allele of the ACE ID was protective against T2D with OR = 0.10, p-value = 0.0001, whereas the TC genotype and the T allele of the Mir128a (rs11888095) were associated with increased risk to T2D with OR = 3.16, p-value = 0.0001, and OR = 1.68, p-value = 0.01, respectively. We conclude that the VEGF (rs699947), VEGF I/D and Mir128a (rs11888095) are potential risk loci for T2D, and that the D allele of the ACE ID polymorphism may be protective against T2D. These results help in identification and stratification for the individuals that at risk for T2D. However, future well-designed studies in different populations and with larger sample sizes are required. Moreover, studies to examine the effects of these polymorphisms on VEGF and ACE proteins are recommended.

1. Introduction

Chronic diseases have often serious impact on the patients, their families and societies. Choosing an appropriate clinical investigation methodology is always essential to decide on proper treatment for patients [1,2]. Diabetes mellitus was one of the leading global causes of death in 2017 [3]. According to the WHO, Kingdom Saudi Arabia (KSA) ranks second in terms of DM prevalence and it has been estimated that more than 20% of Saudi population are diabetic [4]. This rate may be one of the highest in the world [4]. DM has very serious complications and consequences, such as diabetic retinopathy, diabetic nephropathy, diabetic neuropathy, cardiovascular diseases and amputation. DM is a metabolic disorder characterized by hyperglycemia. The insufficient insulin secretion by the pancreatic beta cells leads to the development of type 1 DM (T1DM) [5]. Type 2 diabetes (T2DM) results from the combination of insulin resistance initiated in major tissues, excessive hepatic production of glucose and pancreatic beta cells’ dysfunction [6]. T2DM represents more than 90% of all cases of DM [7]. T2D is induced by complex interactions of genetic and environmental risk factors [8]. Genome wide association studies (GWAS) have uncovered the association of certain loci with metabolic diseases including T2D [9,10,11,12,13,14,15]. Environmental risk factors of T2D include obesity, physical inactivity and unhealthy diet [7]. The vascular endothelial growth factor (VEGF) is a growth factor expressed in endothelial cells and is involved in angiogenesis [16]. The VEGF promoter gene variations (−2578C/A and −1154G/A) have been associated with metabolic syndrome in the Korean population [17]. Moreover, the VEGF rs10738760 (A/G) SNP was associated with metabolic syndrome in Iranian and Lebanese populations [18,19]. Angiotensin I-converting enzyme (ACE) is a nonspecific peptidase with multiple peptide substrates [20]. The ACE converts the angiotensin-I into angiotensin-II [20]. This conversion leads to the activation of the angiotensin-I peptide hormone into the vasoconstrictor angiotensin-II [21]. ACE is involved in important physiologic processes such as blood pressure, development and function of the kidney, reproduction, and the formation of blood cellular components [20]. The long-term inhibition of the ACE has been suggested as one of the preventive measures against T2D [22]. Furthermore, the ACE gene variations were associated with the progression of carotid artery disease in Slovenian T2D patients [23]. MicroRNAs are short noncoding RNA molecules that are involved in the regulation of gene expression [24,25]. It has been reported that there are elevated circulatory levels of miR-128 in patients with T2D and depression [26]. Moreover, the miR-128a rs11888095 was reported to be associated with diabetic neuropathy in the Italian population. In the present study we examined the association of VEGF rs699947 C/A (-2578), VEGF-2549 insertion/deletion (I/D), ACE I/D rs4646994 and Mir128a (rs11888095) gene variations with T2D in subjects from KSA.

2. Material Methods

2.1. Study Population, Inclusion and Exclusion Criteria

This project was approved by the Research and Studies Department, Directorate of Health Affairs, Taif, approval No. 229, and by the Research Ethics Committee of the Armed Forces hospitals, Northwestern Region approval No. R & RE C2016-115. The population comprised T2D patients vising the hospitals for routine checkup. The study included patients with clinically confirmed cases of T2D. The study included only citizens of Saudi Arabia, both males and females. All subjects gave informed consent. A standard questionnaire was used to document the socio-demographical characteristics such as age, sex and lifestyle.
The study excluded patients with T1D and patients with any previous history of any chronic diseases. The healthy controls (HCs) ranged from 20 to 80 years of age, and were visiting the hospital for a routine checkup. The controls were enrolled from the general population of the same geographical region. A routine medical check-up was conducted (CBC, KFT, LFT, etc.) and the history of illness, if detected, was recorded by a health practitioner. Those who appeared apparently healthy without any history of any significant disease, or other chronic diseases, were considered normal. Figure 1 summarizes the procedure of study from samples collection to the statistical analyses.

2.2. Sample Collection and Genomic DNA Extraction

All patient specimens were timed around the routine drawing of blood that was the part of a routine workout, and hence did not require additional phlebotomy. About 3 mL of peripheral blood was collected by venipuncture in EDTA tubes from T2D patients and from HCs. DNA was extracted using a DNeasy Blood Kit (Cat No. 69506) Qiagen (Hilden, Germany) as per the manufacturer’s instructions, then the DNA was dissolved in nuclease-free water and stored at 4 °C until use. The extracted DNA was dissolved in nuclease-free H2O and stored at 4 °C until use. The quality of the extracted DNA was checked by running the sample in 0.8% agarose gel. The quantity of the extracted DNA was determined by NanoDrop™ (Thermo Scientific, Waltham, MA, USA).

2.3. Genotyping of VEGF rs699947C/A and miR-128a rs11888095 C/T by Amplification Refractory Mutation System PCR (ARMS-PCR)

VEGF promoter region rs699947 C/A and mirR-128 rs11888095 C > T genotyping was conducted by the ARMS-PCR [27,28]. The primers were designed using Primer3 software (Table 1). The ARMS-PCR was performed in a reaction volume of 25 µL containing template DNA (50 ng), Fo-0.25 µL, Ro-0.25 µL, FI-0.25 µL and RI-0.25 µL of 25 pmol of each primer, and 10 µL from GoTaq Green PCR Master Mix (2X) (Promega, Madison, WI, USA). The final volume of 25 µL was adjusted by adding nuclease-free ddH2O. Then, 2 µL of DNA was added from each subject.

2.3.1. ARMS-PCR Programming

The PCR conditions optimized for VEGF (rs699947C/A) and miR128 rs11888095 C/T were with an initial denaturation at 95 °C for 10 min followed by 40 cycles of denaturation at 95 °C for 35 s; annealing Tm was 58 °C for VEGF rs699947 and 55 °C for miR128a rs11888095 for 40 s, extension at 72 °C for 45 s, and a final extension step at 72 °C for 10 min.

2.3.2. Gel Electrophoresis for ARMS-PCR Products

The VEGF rs699947 (−2578 C/A) amplification products were separated by electrophoresis. Primers Fo and Ro flank the exon of the VEGF-2578 C/A gene, resulting in a band of 353 bp to act as a control for DNA quality and quantity. Primers FI and Ro amplify a wild-type allele (C allele), generating a band of 229 bp, and primers Fo and RI generate a band of 149 bp from the mutant allele (A allele) Figure 2.
The mir128 rs11888095 C/T amplification products were separated by electrophoresis. Primers Fo and Ro of mir128 rs11888095 C/T resulted in a band of 458 bp to act as a control for DNA quality and quantity. Primers Fo and Ro amplify a wild-type allele (T allele), generating a band of 202 bp, and primers Fo and RI generate a band of 295 bp from the mutant allele (A allele) Figure 3.

2.4. Genotyping of VEGF I/D and ACE I/D rs4646994 by Mutation Specific PCR (MSP)

VEGF I/D and ACE I/D rs4646994 polymorphisms were genotyped using the MSP. with primers used by Amle et al. [29] for VEGF D/I. The primers were designed using the Primer3 software (Table 1). The PCR was undertaken in a reaction volume of 25 µL containing template DNA (50 ng), F-0. 25µL and R-0. 25 µL of 25 pmol of each primer, and 10 µL from GoTaq® Green Master Mix (cat no. M7122) (Promega, Madison, WI, USA). The final volume of 25 µL was adjusted by adding nuclease-free double distilled water (ddH2O).

2.4.1. MSP Programming

The PCR conditions used were initial denaturation at 95 °C for 10 min followed by 40 cycles of 95 °C for 35 s, annealing Tm for ACE I/D (58 °C) for 40 s and for VEGF I/D (58.80 °C) for 1 min, 72 °C for 45 s followed by the final extension at 72 °C for 10 min.

2.4.2. Gel Electrophoresis for MSP Products

The MSP of the product of the VEGF-2549 I/D products were separated on 1.5% agarose. There were two bands of 211 bp for the D allele and 229 bp for the I allele (Figure 4). The MSP product of ACE-I/D was separated on 1.5% agarose gel. There were 3 bands. The II genotype yielded a 490 bp fragment, the DD genotype yielded a 190 bp fragment, and ID yielded both 490 and 190 bp fragments (Figure 5).

2.5. Statistical Analysis

Group differences were compared using Student’s two-sample t-test or one-way analysis of variance (ANOVA) for continuous variables and chi-squared test for categorical variables. Deviations from the Hardy–Weinberg disequilibrium (HWD) were calculated by the chi-square (χ2) goodness-of-fit test. The differences in the VEGF rs699947 C/A, VEGF-2549 I/D, miR128 rs11888095 C/T and ACE I/D rs4646994 genotype frequencies between cases and controls were evaluated using the chi-square test. Associations between alleles and genotypes and the incidence of T2D were estimated with the odds ratios (ORs), and the risk ratios (RRs). We calculated the risk differences (RDs) with 95% confidence intervals (CIs). A p-value < 0.05 suggested a significant difference. Statistical analyses were performed using Graph Pad Prism 6.0 or SPSS 16.

3. Results

3.1. Genotypes Distribution of the Gene Polymorphisms

Our results showed that the VEGF rs699947 C/A genotype distribution was significantly different between the cases (34.4, 57.4 and 8.2%) and the healthy controls (46, 38.1 and 15.9%) with a p-value = 0.007 (Table 2). The results also indicated that the distribution of the VEGF I/D polymorphism was significantly different between the cases (15, 53.4 and 31.6), and the controls (22.6, 57.9 and 19.5) with a p-value = 0.049 (Table 3). Moreover, results indicated that there was significant difference in the genotype distribution of the ACE I/D between the cases (57.2, 36.2 and 6.6%) and controls (12, 40 and 48%) with p-value = 0.049 (Table 4). The genotype distribution of the mir128 rs11888095 C/T was also significantly different between cases (27, 53 and 20%) and the controls (55, 34 and 11%) with a p-value = 0.0001 (Table 5).

3.2. The Association of the VEGF rs699947 C/A SNP with T2D

The results indicated that VEGF rs699947 C/A SNP was associated with T2D, the CA genotype was associated with T2D with OR (95% CI) = 2.01 (1.17–3.45), RR = 1.42 (1.08–1.87), p-value = 0.011 (Table 6). Our results showed that there was a significant difference (p-value < 0.05) in the VEGF rs699947 C/A genotype distribution between male and female cases, and between cases >25 years and cases >40 years (Table 7). The results also showed that there were significant differences (p-values < 0.05) between different genotypes of the VEGF rs699947 in the patients with a normal lipid profile and cases with an abnormal lipid profile (Table 7).

3.3. The Association of VEGF-2549 I/D Polymorphism with T2D

Results indicated that the D allele of the VEGF I/D at the −2549 position was associated with T2D with OR (95% CI) = 1.43 (1.02–2.03), RR = 1.01 (0.83–1.21), p-value = 0.037 (Table 8). The results also showed that there were significant differences (p-values < 0.05) between different genotypes of the VEGF-2549 I/D polymorphism in patients with a normal lipid profile and patients with an abnormal lipid profile (Table 9).
The gender and age are based on data for 107 cases. HbA1c% is based on data for 97 cases, cholesterol mg/dl—82 cases, LDL-C mg/dl—85 cases, HDL-C mg/dl—38 cases, and vitamin D ng/mL (VIT.D)—37 cases.

3.4. Association of ACE I/D rs4646994 Polymorphism with T2D Patients

Results showed that the ACE I/D polymorphism was associated with T2D (Table 10). The ACE–ID genotype was associated with T2D with OR = 0.18 (0.101–0.354), RR = 0.32 (0.208–0.518), p-value = 0.0001 (Table 10). The ACE–DD genotype was also associated with T2D with OR = 0.128 (0.0125–0.0661), RR = 0.19(0.1272–0.2996), p-value = 0.0001 (Table 10). The D allele was associated with T2D with OR = 0.10 (0.12–0.2), RR = 0.40 (0.3–0.5), p-value = 0.0001 (Table 10). Our results indicated that there were a significant different in ACE I/D genotype distribution between cases aged >25 years and cases aged <25 years (Table 11). The results showed that there was a significant difference (p-value = 0.02) in the ACE I/D genotype distribution between cases with normal and cases with elevated HbA1c (Table 11). Moreover, results showed that there were significant differences (p-values < 0.05) in the ACE I/D genotype distribution in cases with normal and abnormal lipid profiles (Table 11).

3.5. Association of miR128 rs11888095 C/T SNP with T2D Patients

Results showed that the CT genotype of the miR128 rs11888095 was associated with T2D with OR = 3.16 (1.8–5.6), RR = 1.78 (1.3–2.4), p-value = 0.0001 (Table 12). The results also indicated that the T allele was associated with T2D with OR = 1.68 (1.13–2.5), RR = 1.36 (1.0695–1.7400), p-value = 0.0105 (Table 12). Our results showed that there was a significant difference (p-value = 0.031) in the miR128 rs11888095 genotype distribution between male and female cases (Table 13). Moreover, the results showed that there was a significant difference (p-value = 0.021) in cases aged <25 years and cases aged >25 years (Table 13). Moreover, results showed that there a significant difference (p-value = 0.0001) in cases with normal and cases with elevated HbA1c (Table 13). Furthermore, results showed that there was a significant difference (p-values < 0.05) in the genotype distribution of cases with normal and cases with elevated lipid profiles (Table 13).

4. Discussion

T2D is developed by a mixture of insulin resistance and impaired secretion of insulin [6]. The VEGF stimulates the cell proliferation, migration and vasopermeability in many tissues [30]. It has been reported that there is association between the VEGF expression and metabolic syndrome [31]. Metabolic syndrome is defined as insulin resistance associated with obesity, hypertension and dyslipidemia [31]. It has been suggested that increased levels of VEGF are associated with increased blood sugar [31]. Our results showed that there was a significant difference (p-value = 0.007) in the genotype distribution of VEGF rs699947 C/A between the cases and the controls (Table 2). The CA genotype was also associated with T2D (Table 6).
Results showed that there was a significant difference (p-values < 0.05) in the VEGF rs699947 genotype distribution between cases with normal and cases with abnormal lipid profiles (Table 7). This result is consistent with studies that reported the association of VEGF rs699947 with the risk of cardiovascular disease [32,33]. The results also showed that males with the rs699947 CA genotype and A allele are more susceptible to T2D than females (p-values < 0.05, Table 7). This result is consistent with studies reporting higher prevalence of T2D among males than females [34,35]. Our results also indicated that elder individuals (age > 40) with the CA genotype are more prone to T2D than younger individuals (<40 years old, p-values < 0.05, Table 6). This is also in agreement with a previous study that reported that T2D is more prevalent in older individuals in all populations [36]. We did not observe significant differences in the VEGF rs699947 genotype distribution in the cases with normal or elevated HBA1c (p-values > 0.05, Table 7). This is probably because of the relatively small sample size used in this study. This may be one of the limitations of this study.
The VEGF gene I/D polymorphism is an 18 bp fragment found at the −2549 position of the promoter region [29]. The results showed that there is a significant difference (p-value < 0.05) in the genotype distribution of VEGF I/D polymorphism between the cases and healthy controls (Table 3). The results indicated that the D allele of the VEGF I/D was associated with T2D (Table 8). Moreover, results indicated that there was a significant difference (p-values < 0.05) between genotypes of the VEGF I/D polymorphism in the patients with normal and patients with abnormal lipid profiles (Table 9). Gene variations in the VEGF gene promoter such as I/D polymorphism were reported to increase expression of the VEGF [30,37]. Therefore, these results may be in agreement with the result of Zafar et al., who reported that the increased expression of the VEGF gene has been associated with metabolic syndromes, such as hypertriglyceridemia [31]. Several previous studies have implicated the associations of diabetes complications with gene variations at the VEGF [38]. For instance, the VEGF gene at position −7 C/T was reported to be associated with diabetic neuropathy in a population of British Caucasians [39]. In addition, the −634 G/C SNP in the 5′UTR of the VEGF gene was associated with risk diabetic retinopathy (DR) in the Japanese population [40]. Furthermore, Buraczynska et al. showed that the VEGF I/D polymorphism was associated with risk of DR in the Polish population [37]. Churchill et al. reported the association of the VEGF promoter SNPs rs735286 and rs2146323 with the severity of DR in the British population [41], whereas Han et al. reported the association of VEGF gene SNPs rs3025039 and rs833061 with DR in the Chinese population [42]. Furthermore, the −2578 C/A, rs699947, was associated with diabetic foot ulcers in Iranian and Chinese Han populations [43,44]. All these studies may be in agreement with our results as they report the association of VEGF gene variations with diabetes complications, and our results showed that the VEGF gene variations rs699947 and VEGF I/D polymorphisms were associated with the risk of T2D (Table 6 and Table 8). Furthermore, it has been reported that the gene variations in the VEGF promoter (e.g., VEGF rs699947 and VEGF I/D) result in enhanced expression of the VEGF gene [30,37]. The increased expression of the VEGF gene was associated with insulin resistance [31], and that the neutralization of the VEGF gene resulted in improvement in insulin sensitivity in the liver and in fat tissues [45]. These studies may be in agreement with our results, which showed that VEGF rs699947 and VEGF I/D are associated with T2D risk.
Our results indicated that there was a significant difference in the ACE I/D polymorphism genotype distribution between cases and controls (p-value < 0.05, Table 4). The ACE I/D polymorphism was associated with T2D (Table 10). The results indicated that the ACE ID genotype and the ACE D allele were associated with decreased risk of T2D (Table 10). This result may be partially consistent with the results of Al-Serri et al. (2015) [46], who reported that the I allele of the ACE I/D polymorphism is associated with T2D in the Kuwaiti population. However, our result is in disagreement with the study of Al-Rubeaan et al. (2013) [47]. This disagreement may be due to different sample sizes or different populations. The results indicated that there was a significant difference in the ACE I/D polymorphism genotype distribution between males and females (Table 11). The results also showed that there were significant differences in the ACE I/D polymorphism genotype distribution between cases with elevated and cases with normal HbA1c, and between cases with normal and cases with abnormal lipid profiles (Table 11). These results are perhaps in agreement with studies reporting the association of ACE I/D polymorphism with dyslipidemia [48,49]. A previous study also proposed the ACE as a target for the protection of pancreatic beta cells from dysfunction causing T2D [50].
Results indicated the miR128 rs11888095 may be associated with development of T2D (Table 5 and Table 12). The CT genotype and T allele of the miR128 rs11888095 were associated with risk of T2D (Table 12). This result is consistent with a study reporting that miR128 regulates genes (e.g., Insr, Irs1 and Pik3r1) critical for insulin signaling [51]. Our result is in partial agreement with a study indicating that the T allele of the miR128 rs11888095 is associated with diabetic polyneuropathy [52,53]. The results showed that females carrying the CT genotype and T allele of the miR128 rs11888095 are more susceptible to T2D than males carrying the CT genotype and T allele (Table 13). Furthermore, individuals with the CT genotype and T allele aged >25 years are more susceptible to T2D (Table 13). In addition, results showed that the CT genotype and T allele were more frequently present in cases with elevated HbA1c (>6%) than in cases with normal HbA1c (<6%) (Table 13). This result is expected because miR128 rs11888095 may be associated with T2D (Table 12). Moreover, it was indicated that miR128 rs11888095 may be associated with an elevated lipid profile (Table 13); this is quite consistent with a study reporting that mir128 regulates the gene involved in lipid metabolism [54]. Limitations of this study include the small sample size, and that it is a cross-sectional study, i.e., the samples may have been collected from cases after the blood chemistry was already maintained. Future longitudinal studies with larger sample sizes and in different populations are required. Because T2D can be delayed or prevented by diet modification, weight management, regular exercise and other factors [55,56], results of the present study can be used (after confirmation in future studies) in genetic testing and personalized advice to identify and stratify the individuals that at are risk of developing T2D.

5. Conclusions

We investigated the association of the VEGF promoter gene variations (VEGF rs699947, VEGF I/D), ACE I/D (rs4646994) and miR128 (rs11888095) with T2D in the Saudi population. The results showed that the CA genotype of the VEGF rs699947, the D allele of the VEGF I/D, and the TC genotype and T allele of the Mir128a (rs11888095) were associated with an increased risk of T2D. Moreover, the results indicated the D allele of the ACE I/D was protective against T2D. Further well-designed studies with larger sample sizes in different populations are required. Moreover, proteomics investigations [57,58,59] to examine the effects of SNPs on VEGF and ACE function are recommended.

Author Contributions

Conceptualization, I.E., R.M., F.M.A.D., M.A.A., A.I.A., J.B., A.T.B., M.M.M., F.A., H.M., E.A.M.F., methodology, R.M. and I.E.; validation, R.M. and I.E.; formal analysis, R.M. and I.E.; investigation, All authors; resources, All authors; data curation, R.M. and I.E.; writing—original draft preparation, R.M. and I.E.; writing—review and editing, I.E.; visualization, I.E., R.M., F.M.A.D., M.A.A., A.I.A., J.B., A.T.B., M.M.M., F.A., H.M., E.A.M.F.; supervision I.E.; project administration, I.E.; funding acquisition, I.E., R.M., F.M.A.D., M.A.A., A.I.A., J.B., A.T.B., M.M.M., F.A., H.M., E.A.M.F. All authors have read and agreed to the published version of the manuscript.

Funding

This project is funded by the University of Tabuk, Deanship of scientific research (DSR), project No 0012-1441-S for IE and colleagues.

Institutional Review Board Statement

This study was approved by the research and studies department, directorate of health affairs, Taif, approval No. 229, and by the research ethics committee of the armed forces hospitals, northwestern region, Tabuk, approval No. R & REC2016-115.

Informed Consent Statement

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

Data Availability Statement

Not applicable.

Acknowledgments

We thank T2D patients and healthy controls for their participation in this study. This project is funded by the University of Tabuk, Deanship of scientific research (DSR), project No. 0012-1441-S for IE and colleagues.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Alexovic, M.; Urban, P.L.; Tabani, H.; Sabo, J. Recent advances in robotic protein sample preparation for clinical analysis and other biomedical applications. Clin. Chim. Acta 2020, 507, 104–116. [Google Scholar] [CrossRef] [PubMed]
  2. Mann, S.P.; Treit, P.V.; Geyer, P.E.; Omenn, G.S.; Mann, M. Ethical Principles, Constraints and Opportunities in Clinical Proteomics. Mol. Cell. Proteom. 2021, 20, 100046. [Google Scholar] [CrossRef] [PubMed]
  3. Saeedi, P.; Petersohn, I.; Salpea, P.; Malanda, B.; Karuranga, S.; Unwin, N.; Colagiuri, S.; Guariguata, L.; Motala, A.A.; Ogurtsova, K.; et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res. Clin. Pract. 2019, 157, 107843. [Google Scholar] [CrossRef] [Green Version]
  4. Alswaina, N. Awareness of diabetic retinopathy among patients with type 2 diabetes mellitus in Qassim, Saudi Arabia. J. Fam. Med. Prim. Care 2021, 10, 1183. [Google Scholar] [CrossRef] [PubMed]
  5. Roep, B.O.; Thomaidou, S.; van Tienhoven, R.; Zaldumbide, A. Type 1 diabetes mellitus as a disease of the beta-cell (do not blame the immune system?). Nat. Rev. Endocrinol. 2021, 17, 150–161. [Google Scholar] [CrossRef] [PubMed]
  6. Newsholme, P.; Keane, K.N.; Carlessi, R.; Cruzat, V. Oxidative stress pathways in pancreatic beta-cells and insulin-sensitive cells and tissues: Importance to cell metabolism, function, and dysfunction. Am. J. Physiol. Cell Physiol. 2019, 317, C420–C433. [Google Scholar] [CrossRef] [PubMed]
  7. Galicia-Garcia, U.; Benito-Vicente, A.; Jebari, S.; Larrea-Sebal, A.; Siddiqi, H.; Uribe, K.B.; Ostolaza, H.; Martin, C. Pathophysiology of Type 2 Diabetes Mellitus. Int. J. Mol. Sci. 2020, 21, 6275. [Google Scholar] [CrossRef]
  8. Davegardh, C.; Garcia-Calzon, S.; Bacos, K.; Ling, C. DNA methylation in the pathogenesis of type 2 diabetes in humans. Mol. Metab. 2018, 14, 12–25. [Google Scholar] [CrossRef]
  9. Elfaki, I.; Mir, R.; Abu-Duhier, F.; Jha, C.; Al-Alawy, A.; Babakr, A.; Habib, S. Analysis of the Potential Association of Drug-Metabolizing Enzymes CYP2C9*3 and CYP2C19*3 Gene Variations with Type 2 Diabetes: A Case-Control Study. Curr. Drug Metab. 2020, 21, 1152–1160. [Google Scholar] [CrossRef] [PubMed]
  10. Elfaki, I.; Mir, R.; Almutairi, F.M.; Duhier, F.M.A. Cytochrome P450: Polymorphisms and Roles in Cancer, Diabetes and Atherosclerosis. Asian Pac. J. Cancer Prev. 2018, 19, 2057–2070. [Google Scholar] [PubMed]
  11. Jha, C.K.; Mir, R.; Elfaki, I.; Javid, J.; Babakr, A.T.; Banu, S.; Chahal, S.M.S. Evaluation of the Association of Omentin 1 rs2274907 A>T and rs2274908 G>A Gene Polymorphisms with Coronary Artery Disease in Indian Population: A Case Control Study. J. Pers. Med. 2019, 9, 30. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Elfaki, I.; Mir, R.; Abu-Duhier, F.M.; Khan, R.; Sakran, M. Phosphatidylinositol 3-kinase Glu545Lys and His1047Tyr Mutations are not Associated with T2D. Curr. Diabetes Rev. 2020, 16, 881–888. [Google Scholar] [CrossRef] [PubMed]
  13. Elfaki, I.; Mir, R.; Mir, M.M.; AbuDuhier, F.M.; Babakr, A.T.; Barnawi, J. Potential Impact of MicroRNA Gene Polymorphisms in the Pathogenesis of Diabetes and Atherosclerotic Cardiovascular Disease. J. Pers. Med. 2019, 9, 51. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Mir, R.; Elfaki, I.; Abu-Duhier, F.; Alotaibi, M.; Alalawy, A.; Barnawi, J.; Babakr, A.; Mir, M.; Mirghani, H.; Hamadi, A.; et al. Molecular Determination of mirRNA-126 rs4636297, Phosphoinositide-3-Kinase Regulatory Subunit 1-Gene Variability rs7713645, rs706713 (Tyr73Tyr), rs3730089 (Met326Ile) and Their Association with Susceptibility to T2D. J. Pers. Med. 2021, 11, 861. [Google Scholar] [CrossRef]
  15. Elfaki, I.; Almutairi, F.; Mir, R.; Khan, R.; Abu-Duhier, F. Cytochrome P450 CYP1B1*2 gene and its association with T2D in Tabuk population, Northwestern region of saudi arabia. Asian J. Pharm. Clin. Res. 2018, 11, 55. [Google Scholar] [CrossRef] [Green Version]
  16. Johnson, K.E.; Wilgus, T.A. Vascular Endothelial Growth Factor and Angiogenesis in the Regulation of Cutaneous Wound Repair. Adv. Wound Care 2014, 3, 647–661. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Kim, Y.R.; Hong, S.H. Promoter polymorphisms of the vascular endothelial growth factor gene are associated with metabolic syndrome susceptibility in Koreans. Biomed. Rep. 2017, 6, 555–560. [Google Scholar] [CrossRef] [Green Version]
  18. Ghazizadeh, H.; Fazilati, M.; Pasdar, A.; Avan, A.; Tayefi, M.; Ghasemi, F.; Mehramiz, M.; Mirhafez, S.R.; Ferns, G.A.; Azimi-Nezhad, M.; et al. Association of a Vascular Endothelial Growth Factor genetic variant with Serum VEGF level in subjects with Metabolic Syndrome. Gene 2017, 598, 27–31. [Google Scholar] [CrossRef]
  19. Salami, A.; El Shamieh, S. Association between SNPs of Circulating Vascular Endothelial Growth Factor Levels, Hypercholesterolemia and Metabolic Syndrome. Medicina 2019, 55, 464. [Google Scholar] [CrossRef] [Green Version]
  20. Bernstein, K.E.; Ong, F.S.; Blackwell, W.L.; Shah, K.H.; Giani, J.F.; Gonzalez-Villalobos, R.A.; Shen, X.Z.; Fuchs, S.; Touyz, R.M. A modern understanding of the traditional and nontraditional biological functions of angiotensin-converting enzyme. Pharmacol. Rev. 2013, 65, 1–46. [Google Scholar] [CrossRef] [Green Version]
  21. Pan, Y.H.; Wang, M.; Huang, Y.M.; Wang, Y.H.; Chen, Y.L.; Geng, L.J.; Zhang, X.X.; Zhao, H.L. ACE Gene I/D Polymorphism and Obesity in 1,574 Patients with Type 2 Diabetes Mellitus. Dis. Markers 2016, 2016, 7420540. [Google Scholar] [CrossRef] [PubMed]
  22. Pigeyre, M.; Sjaarda, J.; Chong, M.; Hess, S.; Bosch, J.; Yusuf, S.; Gerstein, H.; Pare, G. ACE and Type 2 Diabetes Risk: A Mendelian Randomization Study. Diabetes Care 2020, 43, 835–842. [Google Scholar] [CrossRef] [PubMed]
  23. Merlo, S.; Novak, J.; Tkacova, N.; Nikolajevic Starcevic, J.; Santl Letonja, M.; Makuc, J.; Cokan Vujkovac, A.; Letonja, J.; Bregar, D.; Zorc, M.; et al. Association of the ACE rs4646994 and rs4341 polymorphisms with the progression of carotid atherosclerosis in slovenian patients with type 2 diabetes mellitus. Balk. J. Med. Genet. 2015, 18, 37–42. [Google Scholar] [CrossRef] [Green Version]
  24. O’Brien, J.; Hayder, H.; Zayed, Y.; Peng, C. Overview of MicroRNA Biogenesis, Mechanisms of Actions, and Circulation. Front. Endocrinol. 2018, 9, 402. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Mir, R.; Elfaki, I.; Khullar, N.; Waza, A.A.; Jha, C.; Mir, M.M.; Nisa, S.; Mohammad, B.; Mir, T.A.; Maqbool, M.; et al. Role of Selected miRNAs as Diagnostic and Prognostic Biomarkers in Cardiovascular Diseases, Including Coronary Artery Disease, Myocardial Infarction and Atherosclerosis. J. Cardiovasc. Dev. Dis. 2021, 8, 22. [Google Scholar] [CrossRef] [PubMed]
  26. Prabu, P.; Poongothai, S.; Shanthirani, C.S.; Anjana, R.M.; Mohan, V.; Balasubramanyam, M. Altered circulatory levels of miR-128, BDNF, cortisol and shortened telomeres in patients with type 2 diabetes and depression. Acta Diabetol. 2020, 57, 799–807. [Google Scholar] [CrossRef]
  27. Medrano, R.F.; de Oliveira, C.A. Guidelines for the tetra-primer ARMS-PCR technique development. Mol. Biotechnol. 2014, 56, 599–608. [Google Scholar] [CrossRef]
  28. Darawi, M.N.; Ai-Vyrn, C.; Ramasamy, K.; Hua, P.P.; Pin, T.M.; Kamaruzzaman, S.B.; Majeed, A.B. Allele-specific polymerase chain reaction for the detection of Alzheimer’s disease-related single nucleotide polymorphisms. BMC Med. Genet. 2013, 14, 27. [Google Scholar] [CrossRef] [Green Version]
  29. Amle, D.; Mir, R.; Khaneja, A.; Agarwal, S.; Ahlawat, R.; Ray, P.C.; Saxena, A. Association of 18bp insertion/deletion polymorphism, at -2549 position of VEGF gene, with diabetic nephropathy in type 2 diabetes mellitus patients of North Indian population. J. Diabetes Metab. Disord. 2015, 14, 19. [Google Scholar] [CrossRef] [Green Version]
  30. Kim, Y.J.; Chung, W.C.; Jun, K.H.; Chin, H.M. Genetic polymorphisms of vascular endothelial growth factor (VEGF) associated with gastric cancer recurrence after curative resection with adjuvant chemotherapy. BMC Cancer 2019, 19, 483. [Google Scholar] [CrossRef]
  31. Zafar, M.I.; Mills, K.; Ye, X.; Blakely, B.; Min, J.; Kong, W.; Zhang, N.; Gou, L.; Regmi, A.; Hu, S.Q.; et al. Association between the expression of vascular endothelial growth factors and metabolic syndrome or its components: A systematic review and meta-analysis. Diabetol. Metab. Syndr. 2018, 10, 62. [Google Scholar] [CrossRef]
  32. Skrypnik, D.; Mostowska, A.; Jagodzinski, P.P.; Bogdanski, P. Association of rs699947 (-2578 C/A) and rs2010963 (-634 G/C) Single Nucleotide Polymorphisms of the VEGF Gene, VEGF-A and Leptin Serum Level, and Cardiovascular Risk in Patients with Excess Body Mass: A Case-Control Study. J. Clin. Med. 2020, 9, 469. [Google Scholar] [CrossRef] [Green Version]
  33. Wang, Y.; Huang, Q.; Liu, J.; Wang, Y.; Zheng, G.; Lin, L.; Yu, H.; Tang, W.; Huang, Z. Vascular endothelial growth factor A polymorphisms are associated with increased risk of coronary heart disease: A meta-analysis. Oncotarget 2017, 8, 30539–30551. [Google Scholar] [CrossRef]
  34. Huebschmann, A.G.; Huxley, R.R.; Kohrt, W.M.; Zeitler, P.; Regensteiner, J.G.; Reusch, J.E.B. Sex differences in the burden of type 2 diabetes and cardiovascular risk across the life course. Diabetologia 2019, 62, 1761–1772. [Google Scholar] [CrossRef] [Green Version]
  35. Nordstrom, A.; Hadrevi, J.; Olsson, T.; Franks, P.W.; Nordstrom, P. Higher Prevalence of Type 2 Diabetes in Men Than in Women Is Associated With Differences in Visceral Fat Mass. J. Clin. Endocrinol. Metab. 2016, 101, 3740–3746. [Google Scholar] [CrossRef] [Green Version]
  36. Bradley, D.; Hsueh, W. Type 2 Diabetes in the Elderly: Challenges in a Unique Patient Population. J. Geriatr. Med. Gerontol. 2016, 2, 14. [Google Scholar] [CrossRef] [PubMed]
  37. Buraczynska, M.; Ksiazek, P.; Baranowicz-Gaszczyk, I.; Jozwiak, L. Association of the VEGF gene polymorphism with diabetic retinopathy in type 2 diabetes patients. Nephrol. Dial. Transplant. 2007, 22, 827–832. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Bleda, S.; De Haro, J.; Varela, C.; Esparza, L.; Ferruelo, A.; Acin, F. Vascular endothelial growth factor polymorphisms are involved in the late vascular complications in Type II diabetic patients. Diab. Vasc. Dis. Res. 2012, 9, 68–74. [Google Scholar] [CrossRef]
  39. Tavakkoly-Bazzaz, J.; Amoli, M.M.; Pravica, V.; Chandrasecaran, R.; Boulton, A.J.; Larijani, B.; Hutchinson, I.V. VEGF gene polymorphism association with diabetic neuropathy. Mol. Biol. Rep. 2010, 37, 3625–3630. [Google Scholar] [CrossRef] [PubMed]
  40. Awata, T.; Inoue, K.; Kurihara, S.; Ohkubo, T.; Watanabe, M.; Inukai, K.; Inoue, I.; Katayama, S. A common polymorphism in the 5′-untranslated region of the VEGF gene is associated with diabetic retinopathy in type 2 diabetes. Diabetes 2002, 51, 1635–1639. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Churchill, A.J.; Carter, J.G.; Ramsden, C.; Turner, S.J.; Yeung, A.; Brenchley, P.E.; Ray, D.W. VEGF polymorphisms are associated with severity of diabetic retinopathy. Investig. Ophthalmol. Vis. Sci. 2008, 49, 3611–3616. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Han, L.; Zhang, L.; Xing, W.; Zhuo, R.; Lin, X.; Hao, Y.; Wu, Q.; Zhao, J. The associations between VEGF gene polymorphisms and diabetic retinopathy susceptibility: A meta-analysis of 11 case-control studies. J. Diabetes Res. 2014, 2014, 805801. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Amoli, M.M.; Hasani-Ranjbar, S.; Roohipour, N.; Sayahpour, F.A.; Amiri, P.; Zahedi, P.; Mehrab-Mohseni, M.; Heshmat, R.; Larijani, B.; Tavakkoly-Bazzaz, J. VEGF gene polymorphism association with diabetic foot ulcer. Diabetes Res. Clin. Pract. 2011, 93, 215–219. [Google Scholar] [CrossRef] [PubMed]
  44. Li, X.; Lu, Y.; Wei, P. Association between VEGF genetic variants and diabetic foot ulcer in Chinese Han population: A case-control study. Medicine 2018, 97, e10672. [Google Scholar] [CrossRef]
  45. Wu, L.E.; Meoli, C.C.; Mangiafico, S.P.; Fazakerley, D.J.; Cogger, V.C.; Mohamad, M.; Pant, H.; Kang, M.J.; Powter, E.; Burchfield, J.G.; et al. Systemic VEGF-A neutralization ameliorates diet-induced metabolic dysfunction. Diabetes 2014, 63, 2656–2667. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Al-Serri, A.; Ismael, F.G.; Al-Bustan, S.A.; Al-Rashdan, I. Association of the insertion allele of the common ACE gene polymorphism with type 2 diabetes mellitus among Kuwaiti cardiovascular disease patients. J. Renin Angiotensin Aldosterone Syst. 2015, 16, 910–916. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Al-Rubeaan, K.; Siddiqui, K.; Saeb, A.T.; Nazir, N.; Al-Naqeb, D.; Al-Qasim, S. ACE I/D and MTHFR C677T polymorphisms are significantly associated with type 2 diabetes in Arab ethnicity: A meta-analysis. Gene 2013, 520, 166–177. [Google Scholar] [CrossRef] [PubMed]
  48. Mahwish, U.N.; Ponnaluri, K.C.; Heera, B.; Alavala, S.R.; Devi, K.R.; Raju, S.B.; Latha, G.S.; Jahan, P. Link between ACE I/D Gene Polymorphism and Dyslipidemia in Diabetic Nephropathy: A Case-control Study from Hyderabad, India. Indian J. Nephrol. 2020, 30, 77–84. [Google Scholar]
  49. Luptakova, L.; Bencova, D.; Sivakova, D.; Cvicelova, M. Association of CILP2 and ACE gene polymorphisms with cardiovascular risk factors in Slovak midlife women. BioMed Res. Int. 2013, 2013, 634207. [Google Scholar] [CrossRef] [Green Version]
  50. Bindom, S.M.; Hans, C.P.; Xia, H.; Boulares, A.H.; Lazartigues, E. Angiotensin I-converting enzyme type 2 (ACE2) gene therapy improves glycemic control in diabetic mice. Diabetes 2010, 59, 2540–2548. [Google Scholar] [CrossRef] [Green Version]
  51. Motohashi, N.; Alexander, M.S.; Shimizu-Motohashi, Y.; Myers, J.A.; Kawahara, G.; Kunkel, L.M. Regulation of IRS1/Akt insulin signaling by microRNA-128a during myogenesis. J. Cell Sci. 2013, 126 (Pt 12), 2678–2691. [Google Scholar] [CrossRef] [Green Version]
  52. Ciccacci, C.; Morganti, R.; Di Fusco, D.; D’Amato, C.; Cacciotti, L.; Greco, C.; Rufini, S.; Novelli, G.; Sangiuolo, F.; Marfia, G.A.; et al. Common polymorphisms in MIR146a, MIR128a and MIR27a genes contribute to neuropathy susceptibility in type 2 diabetes. Acta Diabetol. 2014, 51, 663–671. [Google Scholar] [CrossRef]
  53. Simeoli, R.; Fierabracci, A. Insights into the Role of MicroRNAs in the Onset and Development of Diabetic Neuropathy. Int. J. Mol. Sci. 2019, 20, 4627. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Aryal, B.; Singh, A.K.; Rotllan, N.; Price, N.; Fernandez-Hernando, C. MicroRNAs and lipid metabolism. Curr. Opin. Lipidol. 2017, 28, 273–280. [Google Scholar] [CrossRef] [PubMed]
  55. den Braver, N.R.; de Vet, E.; Duijzer, G.; Ter Beek, J.; Jansen, S.C.; Hiddink, G.J.; Feskens, E.J.M.; Haveman-Nies, A. Determinants of lifestyle behavior change to prevent type 2 diabetes in high-risk individuals. Int. J. Behav. Nutr. Phys. Act. 2017, 14, 78. [Google Scholar] [CrossRef] [PubMed]
  56. Asif, M. The prevention and control the type-2 diabetes by changing lifestyle and dietary pattern. J. Educ. Health Promot. 2014, 3, 1. [Google Scholar] [CrossRef]
  57. Elfaki, I.; Knitsch, A.; Matena, A.; Bayer, P. Identification and characterization of peptides that bind the PPIase domain of Parvulin17. J. Pept. Sci. Off. Publ. Eur. Pept. Soc. 2013, 19, 362–369. [Google Scholar] [CrossRef] [PubMed]
  58. Jaremko, L.; Jaremko, M.; Elfaki, I.; Mueller, J.W.; Ejchart, A.; Bayer, P.; Zhukov, I. Structure and dynamics of the first archaeal parvulin reveal a new functionally important loop in parvulin-type prolyl isomerases. J. Biol. Chem. 2011, 286, 6554–6565. [Google Scholar] [CrossRef] [Green Version]
  59. Kumar, R.; Moche, M.; Winblad, B.; Pavlov, P.F. Combined X-ray crystallography and computational modeling approach to investigate the Hsp90 C-terminal peptide binding to FKBP51. Sci. Rep. 2017, 7, 14288. [Google Scholar] [CrossRef] [Green Version]
Cimb 43 00130 g001 550
Figure 1. Summary of the study explaining the procedure from sample collection until statistical analyses. Abbreviations. HCs: healthy controls. ARMS-PCR: amplification mutation system PCR. MSP: mutation specific PCR.
Figure 1. Summary of the study explaining the procedure from sample collection until statistical analyses. Abbreviations. HCs: healthy controls. ARMS-PCR: amplification mutation system PCR. MSP: mutation specific PCR.
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Cimb 43 00130 g002 550
Figure 2. Genotyping of the VEGF rs699947 C/A using ARMS-PCR in T2D subjects.
Figure 2. Genotyping of the VEGF rs699947 C/A using ARMS-PCR in T2D subjects.
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Figure 3. Genotyping of miR128 rs11888095 C/T using ARMS-PCR in T2D patients. Legend: M—100 bp DNA ladder; Heterozygous C/T—P7, P8, P9 & P15; Homozygous CC—P1, P2, P3, P5, P6, P10, P11, P12, P13, P14; Homozygous TT—P4.
Figure 3. Genotyping of miR128 rs11888095 C/T using ARMS-PCR in T2D patients. Legend: M—100 bp DNA ladder; Heterozygous C/T—P7, P8, P9 & P15; Homozygous CC—P1, P2, P3, P5, P6, P10, P11, P12, P13, P14; Homozygous TT—P4.
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Figure 4. Mutation specific PCR for genotyping of VEGF I/D polymorphism in T2D subjects.
Figure 4. Mutation specific PCR for genotyping of VEGF I/D polymorphism in T2D subjects.
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Figure 5. Genotyping of ACE I/D rs4646994 by mutation specific PCR in T2D. Legend: M—100 bp DNA ladder; Homozygous II genotype—P7; Homozygous DD genotype—P2, P3, P4, P5, P8, P10, P11; Heterozygous ID genotype—P1, P6, P9, P12.
Figure 5. Genotyping of ACE I/D rs4646994 by mutation specific PCR in T2D. Legend: M—100 bp DNA ladder; Homozygous II genotype—P7; Homozygous DD genotype—P2, P3, P4, P5, P8, P10, P11; Heterozygous ID genotype—P1, P6, P9, P12.
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Table
Table 1. Genotyping primers of gene polymorphisms.
Table 1. Genotyping primers of gene polymorphisms.
Primers for VEGF-rs699947 (−2578) C/A Gene PolymorphismBandBand SizeA.Tm
VEGF Fo5-CCTTTTCCTCATAAGGGCCTTAG-3 Control band 353 bp58 °C
VEGF Ro5-AGGAAGCAGCTTGGAAAAATTC-3
VEGF FI A5-TAGGCCAGACCCTGGCAA-3A-allele149bp
VEGF RI C5-GTCTGATTATCCACCCAGATCG-3C-allele243bp
ARMS primers for miR128a rs11888095 C/T
miR128 Fo5-AGTATGGAATTTTTACTGTGTTGTCTGT-3 Control band 441 bp55 °C
miR128 Ro5-GCCAATTATTGCAAAATATTAAATGTATATGG-3
miR128 FI5-ATGTATGCTTTGAATACTGTGAAGGAT-3T-allele202 bp
miR128 RI5-ATACTATACCACACTCCTTATATGCATTG-3C-allele295 bp
Primers for VEGF -2549 insertion/deletion (I/D) gene polymorphism
VEGF F5′-GCTGAGAGTGGGGCTGACTAGGTA-3′D-allele211 bp 58.8 °C
VEGF R5′-GTTTCTGACCTGGCTATTTCCAGG-3′I-allele229 bp
Primer sequence of ACE I/D rs4646994
ACE F5′- GTGGAGACCACTCCCATCCTTTCT -3′D190bp58 °C
ACE R5′- GATGTGGCCATCAACTTCGTCACGAT -3′I490bp
Table
Table 2. The distribution of VEGF rs699947 C/A genotypes in T2D cases and controls.
Table 2. The distribution of VEGF rs699947 C/A genotypes in T2D cases and controls.
SubjectsnCC%CA%AA%DfX2CAp-Value
Cases1224234.47057.4108.229.930.630.370.007
Controls12658464838.12015.9 0.600.40
Table
Table 3. The distribution of VEGF-2549 I/D polymorphism genotypes in T2D cases and controls.
Table 3. The distribution of VEGF-2549 I/D polymorphism genotypes in T2D cases and controls.
SubjectsnI%ID%D%DfX2IDp-Value
Cases13320157153.44231.6260.42 0.58 0.049
Controls1333022.67757.92619.5 0.520.48
Table
Table 4. Genotype distribution of ACE I/D rs4646994 between T2D cases and controls.
Table 4. Genotype distribution of ACE I/D rs4646994 between T2D cases and controls.
SubjectsnII %ID %DD %IDDfX2p-Value
Cases15287 (57.23%)55 (36.2%)10 (6.57%)0.750.25292.43<0.0001
Controls15018 (12%)60 (40%)72 (48%)0.320.68
Table
Table 5. Genotype distribution mir128 rs11888095 C/T genotypes in T2D cases and controls.
Table 5. Genotype distribution mir128 rs11888095 C/T genotypes in T2D cases and controls.
SubjectsnCCCTTTCTDfX2p-Value
Cases12935 (27.13%)68 (52.71%)26 (20.16)0.540.46220.060.0001
Controls11262 (55.35%)38 (33.92%)12 (10.70%)0.730.27
Table
Table 6. Association of VEGF rs699947 C/A SNP with T2D.
Table 6. Association of VEGF rs699947 C/A SNP with T2D.
GenotypesHealthy ControlsT2D CasesOR (95% CI)Risk Ratio (RR)p-Value
(n = 126)%(n = 122)%
Codominant
VEGF–(C)58464234.41 (ref.)1 (ref.)
VEGF-(CA)4838.17057.42.01 (1.17–3.45)1.42 (1.08–1.87)0.011 *
VEGF-(A)2015.9108.20.69 (0.29–1.62)0.87 (0.64–1.17)0.363
Dominant
VEGF (C)58464234.41 (ref.)1 (ref.)
VEGF-(CA + A)68548065.61.62 (0.97–2.71)1.26 (0.99–1.60)0.05
Recessive
VEGF-(C + CA)1068411291.81 (ref.)1 (ref.)
VEGF–(A)2016108.20.47 (0.211–1.05)0.72 (0.54–0.97)0.06
Allele
VEGF-(C)164 154 1 (ref.)1 (ref.)
VEGF-(A)88 90 1.08 (0.75–1.57)1.04 (0.86–1.25)0.64
* Statistically significant difference.
Table
Table 7. Association of VEGF rs699947 C/A genotypes with T2D patients’ characteristics.
Table 7. Association of VEGF rs699947 C/A genotypes with T2D patients’ characteristics.
Clinical
Feature		 CC 42CA 70AA 10X2DFp-Value
Gender
Male8218 (14.75%)58 (47.54%)6 (4.91%)19.3220.0001 *
Female4024 (19.67%)12 (9.83%)4 (3.27%)
Age
>409136 (29.50%)50 (40.98%)5 (4.09%)6.320.042 *
>25316 (4.8%)20 (9.6%)5 (1.9%)
HbA1c%
>610236 (29.50%)60 (49.18%)6 (4.91%)4.4320.100
<6206 (4.91%)10 (8.19%)4 (3.27%)
TG mg/dl
<2004925 (51%)20 (40.81%)4 (8.16%)10.1620.005 *
>2007317 (23.28%)50 (68.49%)6 (8.21%)
TC mg/dl
<2006412 (18.75%)45 (70.31%)7 (10.93%)14.1220.006 *
>2005830 (51.72%)25 (43.10%)3 (5.17%)
LDL-C mg/dl
<1006632 (57.57%)30 (45.45%)4 (6.06%)12.620.0018 *
>1005610 (10.85%)40 (71.42%)6 (10.71%)
HDL-C mg/dl
<55499 (18.36%)34 (69.38%)6 (12.24%)8.3220.015 *
>557331 (42.46%)38 (52%)4 (5.47%)
VITD ng/mL
<30166 (5.8%)10 (9.6%)0 (0%)1.34340.854
>30145 (4.8%)8 (7.7%)1 (1%)
The association of VEGF rs699947 C/A genotypes with T2D patients characteristics. The gender, age, HbA1c%, cholesterol mg/dl, LDL-C mg/dl and HDL-C mg/dl are based on data for 122 cases. For the vitamin D ng/mL (VITD), data was collected for 30 cases. * Statistically significant difference.
Table
Table 8. Association of VEGF-2549 I/D polymorphism with T2D.
Table 8. Association of VEGF-2549 I/D polymorphism with T2D.
GenotypesHealthy ControlsT2D CasesOR (95% CI)Risk Ratio (RR)p-Value
(n = 133)%(n = 133)%
Codominant
VEGF–(I)3022.552015.031 (ref.)1 (ref.)
VEGF-(ID)7757.897153.381.38 (0.72–2.65)1.15 (0.87–1.51)0.32
VEGF-(D)2619.544231.572.42 (1.14–5.11)1.56 (1.07–2.28)0.010 *
Dominant
VEGF (I)3022.552015.031 (ref.)1 (ref.)
VEGF-(ID + D)10377.4411384.961.64 (0.88–3.07)1.25 (0.96–1.64)0.090
Recessive
VEGF-(I + ID)10780.459168.421 (ref.)1 (ref.)
VEGF–(D)2619.544231.571.89 (1.08–3.33)1.37 (0. 98–1.91)0.025 *
Allele
VEGF-(I)13310011183.451 (ref.)1 (ref.)
VEGF-(D)12997155116.541.43 (1.02–2.03)1.01 (0.83–1.21)0.037 *
* Statistically significant difference.
Table
Table 9. Association of VEGF-2549 I/D genotypes with T2D patients’ characteristics.
Table 9. Association of VEGF-2549 I/D genotypes with T2D patients’ characteristics.
Clinical FeaturenD %I %ID %X2DFp-Value
Gender
Female407 (6.5%)9 (8.4%)24 (22.4%)1.49820.473
Male6714 (13.1%)9 (8.4%)44 (41.1%)
Age
>25164 (3.7%)1 (0.9%)11 (10.3%)1.60720.448
>409117 (15.9%)17 (15.9%)57 (53.3%)
HbA1c%
<610 (0.0%)0 (0.0%)1 (0.9%)0.57920.749
>69621 (19.6%)18 (16.8%)67 (62.6%)
TC mg/dl
<2006512 (11.2%)7 (6.5%)46 (43.0%)11.41140.022 *
>200176 (35.29%)2 (11.76%)9 (52.94%)
TG mg/dl
<2006512 (18.46%)7 (10.76%)46 (70.76%)11.020.004 *
>2003112 (38.70%)8 (16.12%)11 (35.48%)
LDL-C mg/dl
<100344 (3.7%)2 (1.9%)26 (24.3%)11.36440.023 *
>1005114 (13.1%)8 (7.5%)29 (27.1%)
HDL-C mg/dl
<552617 (15.9%)9 (8.4%)45 (42.1%)13.10640.011 *
>55121 (0.9%)0 (0.0%)11 (10.3%)
VIT D ng/ml
<30223 (2.8%)2 (1.9%)17 (15.9%)2.77740.596
>30153 (2.8%)2 (1.9%)10 (9.3%)
* Statistically significant difference. Association of VEGF-2549 I/D Genotypes with T2D Patient’s Characteristics.
Table
Table 10. Association of ACE I/D polymorphism with T2D.
Table 10. Association of ACE I/D polymorphism with T2D.
GenotypesHealthy ControlsT2D CasesOR (95% CI)Risk Ratio (RR)p-Value
(n = 150)(n = 152)
Co-dominant
ACE–II18871 (ref.)1 (ref.)
ACE–ID60550.18 (0.1–0.35)0.32 (0.208–0.518)0.0001 *
ACE–DD72100.128 (0.01–0.1)0.19 (0.1272–0.2996)<0.0001 *
Dominant
ACE–II18871 (ref.)1 (ref.)
ACE–(DI+DD)132650.10 (0.06–0.18)0.25 (0.1661–0.3940)<0.0001 *
Recessive
ACE–(II+DI)781421 (ref.)1 (ref.)
ACE–DD72100.076 (0.04–0.15)0.40 (0.3–0.49)<0.0001 *
Allele
ACE–I962291 (ref.)1 (ref.)
ACE–D204750.10 (0.1–0.2)0.40 (0.33–0.48)0.0001 *
Over dominant
ACE–II+DD90971 (ref.)1 (ref.)
ACE–ID60550.85 (0.5–1.4)0.92 (0.73–1.16)0.4910
* Statistically significant difference.
Table
Table 11. Association of ACE I/D polymorphism genotype with patients’ characteristics.
Table 11. Association of ACE I/D polymorphism genotype with patients’ characteristics.
Clinical Featurenn = 152IIDIDDX2DFp-Value
Gender
Male442517020.4920.78
Female108623808
Age
>259641460922.6220.0001 *
<2556460901
HBA1c%
>61035245067.7920.020 *
<649351004
TG mg/dl
<200804625096.7420.0344 *
>20072413001
TC mg/dl
<200995043066.4920.039 *
>2005337124
LDL-C mg/dl
<100823730017.1920.022 *
>10070502509
HDL-C mg/dl
<551045842045.4720.64
>5548291306
* Statistically significant difference.
Table
Table 12. Association of miR128 rs11888095 C/T with T2D.
Table 12. Association of miR128 rs11888095 C/T with T2D.
GenotypesHealthy
Controls		T2D CasesOR (95% CI)Risk Ratio (RR)p-Value
(n)(n)
Codominant
miR128 –(C)6235RefRef
miR128-(CT)38683.16(1.8–5.6)1.78(1.3–2.4)0.0001 *
miR128-(T)12263.83(1.7–8.5)2.0 (1.2–3.3)0.0010 *
Dominant
miR128-(C)6235RefRef
miR128-(CT + T)50943.3(1.9–5.7)1.84(1.4063–2.4097)<0.0001 *
Recessive
miR128-(C + CT)100103RefRef
miR128–(T)12263.3(1.9–5.7)1.55(0.96–2.5)<0.0001 *
Allele
miR128-(C)112129RefRef
miR128-(T)621201.68(1.13–2.5)1.36 (1.1–1.7)0.0105 *
* Statistically significant difference.
Table
Table 13. Association of miR128 rs11888095 C/T genotypes with T2D patients’ characteristics.
Table 13. Association of miR128 rs11888095 C/T genotypes with T2D patients’ characteristics.
Clinical Featuren CCCTTTX2DFp-Value
Gender 129356826
Male40728056.9520.031 *
Female89284021
Age
>251092659257.5320.021 *
<2520100901
HBA1c%
>6821249211820.0001 *
<647231905
TG mg/dl
<2008214482011.8420.0027 *
>2004721206
TC mg/dl
<200852840170.5220.776
>2004417189
LDL-C mg/dl
<100560834148.3720.0152 *
>10073273412
HDL-C mg/dl
<55982554190.9620.6188
>5531101407
* Statistically significant difference.
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Elfaki, I.; Mir, R.; Duhier, F.M.A.; Alotaibi, M.A.; Alalawy, A.I.; Barnawi, J.; Babakr, A.T.; Mir, M.M.; Altayeb, F.; Mirghani, H.; et al. Clinical Implications of MiR128, Angiotensin I Converting Enzyme and Vascular Endothelial Growth Factor Gene Abnormalities and Their Association with T2D. Curr. Issues Mol. Biol. 2021, 43, 1859-1875. https://doi.org/10.3390/cimb43030130

AMA Style

Elfaki I, Mir R, Duhier FMA, Alotaibi MA, Alalawy AI, Barnawi J, Babakr AT, Mir MM, Altayeb F, Mirghani H, et al. Clinical Implications of MiR128, Angiotensin I Converting Enzyme and Vascular Endothelial Growth Factor Gene Abnormalities and Their Association with T2D. Current Issues in Molecular Biology. 2021; 43(3):1859-1875. https://doi.org/10.3390/cimb43030130

Chicago/Turabian Style

Elfaki, Imadeldin, Rashid Mir, Faisel M. Abu Duhier, Maeidh A. Alotaibi, Adel Ibrahim Alalawy, Jameel Barnawi, Abdullatif Taha Babakr, Mohammad Muzaffar Mir, Faris Altayeb, Hyder Mirghani, and et al. 2021. "Clinical Implications of MiR128, Angiotensin I Converting Enzyme and Vascular Endothelial Growth Factor Gene Abnormalities and Their Association with T2D" Current Issues in Molecular Biology 43, no. 3: 1859-1875. https://doi.org/10.3390/cimb43030130

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