Abstract
Background
Coronary artery diseases may be affected by several genetic and nongenetic factors. Single-nucleotide polymorphism (SNP) rs599839 and type 2 diabetes mellitus (T2DM) can affect the occurrence and severity of coronary artery disease (CAD).
Methods
Our aim was to investigate how T2DM and the rs599839 variant affected serum lipid levels and the degree of CAD patients' coronary artery stenosis. rs599839 polymorphism genotyping was done on Saudi patients with coronary angiography performed previously. Patients enrolled were divided into group A (360 DM patients), group B (225 DM patients with CAD), and group C (190 healthy volunteers as control).
Results
Individuals with diabetes and CAD who possessed the GG genotype in rs599839 exhibited markedly reduced means of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG; 224.5, 116.2, and 221.4 versus 251.6, 131.3, and 261.7 mg/dl, p=0.003, 0.007, and 0.025, respectively) than AA genotype. The odds ratio and the confidence interval of 95% for G allele carriers of rs599839 were OR = 0.62, 95% CI: 0.41–0.82, and p=0.003, among diabetic patients with CAD.
Conclusions
In patients with diabetic CAD, the locus 1p13.3 polymorphism rs599839 was found to be substantially correlated with serum lipid levels. Furthermore, among Saudi patients with diabetes, the G allele of rs599839 variant lowers the CAD risk.
1. Introduction
There is an overwhelming evidence that diabetes mellitus (DM) has become an epidemic on a global scale and that its prevalence is gradually increasing [1]. Cardiovascular diseases (CVD) are the leading cause of morbidity and death among diabetic patients, and type 2 diabetes mellitus (T2DM) is considered a significant independent risk factor for these conditions [2]. Furthermore, DM elevates the coronary artery disease (CAD) risk by a factor of two to six [3]. Acute thrombotic cardiovascular events are the primary cause of mortality in CAD [4]. Moreover, a number of other established risk factors for CAD exist, including genetics, smoking, hypertension, dyslipidemia, obesity, and sedentary lifestyle [5].
Genome-wide association studies (GWAS) have recently shown that a number of genetic loci represent risk or protective factors of CVD, and these loci are associated with multiple single-nucleotide polymorphisms (SNPs) mapped at 1p13.1, whereas including this region, five genes; cadherin, myosin-binding protein H-like (MYBPHL), proline-serine-rich coiled-coil 1 (PSRC1), sortilin 1 (SORT1), and EGF LAG Seven-Pass G type receptor 2 (CELSR2) [6].
Sortilin 1 protein, encoded by the SORT1 gene, is engaged in a number of lipid-associated processes, including the secretion of very low density lipoprotein-cholesterol (VLDL-C), the formation of atherosclerotic plaques, the metabolism of LDL cholesterol, and the secretion of PCSK9 [7]. It also contributes to other pathophysiological processes such as insulin resistance, vascular calcification, inflammation, dyslipidemia, and foam cell formation [8]. The rs599839 SNP locates between PSRC1 and CELSR2 genes at a noncoding region [9].
Saudi healthcare resources are heavily burdened by the increasing frequency of diabetes and CAD. For better understanding the role of rs599839 and assess its potential to either induce or protect against CAD, this study sets out to investigate the relationship between rs599839 and the risk, severity, and eventual clinical outcomes of CAD in type 2 diabetes.
2. Subjects and Methods
2.1. Participants
A number of 585 T2DM patients with diagnoses made in accordance with ADA2017 participated in a cross-sectional study, while 190 healthy volunteers served as the control group who, based on their age and sex, were matched with cases. The study participants underwent elective coronary angiography for suspected CAD. Diabetic patients were further subdivided angiographically into two groups, group A: 360 patients (CAD-free), group B: 225 diabetic patients with CAD, while group C: The control group is CAD-free. Patients were recruited at Al-Noor specialized hospitals as well as King Abdullah Medical City (KAMC) in Mecca from March 1, 2019, to April 31, 2021.
A statistical specialist used the open Epi version 6 program to determine the sample size at a 95% confidence level and 80% study power [10], using the prevalence of CAD and DM announced by the Ministry of Health (MOH) Yearbook of Statistics, and the frequency of the mutant genotype in Gulf nations was found to be around 47.4% [11].
We excluded individuals diagnosed with decompensated heart failure, rheumatic valvular heart diseases, diabetes mellitus type 1 (T1DM), patients with eGFR lower than 60 ml/min/1.73 m2, or patients with decompensated liver disease and prior myocardial infarction from this study.
Participants underwent a comprehensive clinical evaluation and took their history in great detail. Measurements of weight, height, hips, and waist were obtained. Systolic and diastolic blood pressure (SBP and DBP) were recorded. Weight (in kg)/height (in m2) was used to calculate BMI. The use of existing heart, lipid-lowering, and antidiabetic medications was documented.
2.2. SYNTAX Score
An angiographic grading system called the SYNTAX score is a tool for assessing the complexity of CAD. It represents the total points allotted to each lesion found in the coronary artery tree that narrows more than 50% of the vessel's diameter, when the diameter is greater than 1.5 mm. Depending on the degree of disease present, each segment receives a score of 1 or 2. After that, this score is then weighted using a chart, with values ranging from 0.5 for smaller branches to 5.0 for the left main and 3.5 for the proximal left anterior descending artery (LAD). Points are added for further description of the lesions. Next, each of these features is added up by the SYNTAX score algorithm to get the overall SYNTAX score [12]. An algorithm for SYNTAX scores was used to determine the overall SYNTAX score available on the SYNTAX website (http://www.syntaxscore.com). Our study protocol was approved by the Umm Al-Qura University Ethical Committee, and an informed consent form was signed by each participant.
2.3. Sample Collection and Biochemical Assay
Following an overnight fast, the subjects had venous blood samples drawn to measure triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), fasting serum insulin (FSI), and fasting and postprandial blood glucose (Cobas, Roche Diagnostics, Mannheim, Germany). Additionally, a whole blood sample obtained via venous EDTA was tested for HbA1c using the colorimetric method. The following formula: (fasting plasma glucose (FPG; mg/dl) × FSI (mU/ml)/405), was made for calculation of the homeostasis model assessments of insulin resistance (HOMA-IR) according to our earlier protocol [13].
2.4. Molecular Analysis
As the manufacturer recommends, a whole blood sample was used for DNA extraction (ThermoFisher Scientific, Waltham, MA, USA). Testing for DNA quality was done with the NanoDrop2000 device (ThermoFisher Scientific, Wilmington, DE, USA) for the detection of optical density values and 1.25% agarose gel electrophoresis. Using 100 ng of genomic DNA and probes of allele-specific TaqMan genotyping technique (Applied Biosystems, Foster City, California, USA), participants were genotyped for rs599839.
Using the StepOne real time PCR system, the genotyping and PCR amplification were carried out (ThermoFisher Scientific, Wilmington, DE, USA) by relying on default conditions as optimized by the supplier company. Reaction mix per well was as follow: 2 µl of genomic DNA was added to master mix consisting of 0.5 µl TaqMan 40X Assay mix (probes and primers), 10 µl TaqMan Genotyping master mix, and 7.5 µl nuclease-free water to obtain totally 20 µl reaction mix. After covering the plate via transparent adhesive film and momentary spin, the plate was immediately inserted in the machine to start the run and get results after 90 min.
2.5. Statistical Analysis
We used SPSS version 21 for all statistical analyses (Chicago, IL, USA). We represented quantitative variables as the median ± interquartile range (IQR) or the mean ± standard deviation, but we used percentages for expression of qualitative variables. The χ2 test was used to look at gender, smoking status, hypertension, and the SNP site. The independent samples t test was used to examine the serum level of the quantitative variables, and the Mann–Whitney test was used to examine the remaining baseline characteristics related to the study population's demographics. We used one-way ANOVA test to compare the different genotypes regarding the quantitative variables, while the remaining baseline characteristics were tested by the Kruskal–Wallis test. The alleles distribution between the two groups was tested by Fisher's exact test. The association significance between CAD and SNP rs599839 was performed by logistic regression analysis. Additionally, we computed odds ratios (OR) and the 95% confidence intervals (CI). The Hardy–Weinberg law examines the balance of allele and genotype frequencies in two groups. A statistically significant difference between the two groups was defined as p < 0.05.
3. Results
The control group's mean age was 60.3 ± 6.3 years, with 62.1% of the participants being male. The mean age of the diabetic group (group A) was 59.3 ± 5.8 years, with 61.1% of the participants being male. The diabetic group with CAD (group B) had mean age of 61.4 ± 7.4 years, and 57.3% of the participants were men. Age, ethnicity, and sex were matched between the control and T2DM groups.
3.1. Anthropometric and Laboratory Characteristics
When compared to the control group, the lipid profile (TG, TC, and LDL-C) and SBP and DBP values of group A were significantly higher. Additionally, the fasting glucose, insulin, HbA1c, and HOMA-IR values of the diabetic patients were significantly higher when compared to those of the control group. In contrast, patients with type 2 diabetes had lower HDL-C levels in comparison to those in the control group (Table 1).
Table 1.
Baseline demographic and biochemical characteristics of groups under study.
| Variable | Group A DM (360) | Group B DM + CAD (225) | Group C healthy volunteers (190) |
|---|---|---|---|
| Age | 59.3 (5.8) | 61.4 (7.4) | 60.3 (6.3) |
| Gender (male n, %) | 289 (61.1%) | 182 (57.3%) | 118 (62.1%) |
| BMI (kg/m2) | 29.3 (3.1) ∗ | 30.2 (3.3) ∗ | 25.3 (2.6) |
| Body weight (kg) | 81.1 (4.3) ∗ | 85 (5.8) ∗ | 74.2 (2.6) |
| Waist circumference (cm) | 82.1 (5.4) ∗ | 102.3 (4.1) ∗ | 81.4 (3.1) |
| Smoking (%) | 108 (30%) ∗∗ | 101 (44.9%) ∗∗ | 22 (11.6%) |
| Sedentary life (%) | 120 (33.3%) ∗∗ | 120 (53.3%) ∗∗ | 30 (15.8%) |
| Systolic BP (mmHg) | 126.7 (4.7) ∗ | 138.5 (6.7) ∗ | 118.2 (3.7) |
| Diastolic BP (mmHg) | 88.5 (3.6) ∗ | 92.3 (4.1) ∗ | 76.4 (2.9) |
| Fasting glucose (mg/dl) | 126.7 (6.9) ∗ | 135.3 (4.2) ∗∗ | 92.3 (5.1) |
| Fasting insulin (lU/ml) | 7.52 (1.8) ∗ | 25.58 (11.6) ∗ | 8.945 (1.07) |
| HbA1c (%) | 9.44 (1.89) ∗ | 10.8 (0.95) ∗∗ | 4.8 (0.23) |
| HOMA-IR | 1.84 (0.9) ∗ | 7.75 (5.7) ∗∗ | 1.8 (0.35) |
| DM duration (years) | 12.4 (5.3) | 20.23 (5.9) ∗∗ | — |
| Total cholesterol (mg/dl) | 223.1 (14.6) ∗∗ | 235.6 (19.5) ∗ | 151.67 (12.4) |
| HDL-C (mg/dl) | 35.2 (8.1) ∗∗ | 28.3 (9.3) ∗∗ | 39.6 (10.6) |
| LDL-C (mg/dl) | 120.7 (21.4) ∗∗ | 131.3 (25.3) ∗∗ | 93.6 (12.6) |
| Triglycerides (mg/dl) | 231.1 (17.2) ∗∗ | 280.3 (14.5) ∗ | 182.1 (13.3) |
| SYNTAX score | 21.8 (2.6) ∗∗ | 28.3 (2.9) ∗ | 17 (1.4) |
| DM treatment | |||
| Oral drugs | 101 (28.1%) | 85 (37.8%) ∗ | — |
| Insulin | 43 (11.9%) | 37 (16.4%) | — |
| Mixed | 33 (9.2%) | 25 (11.1%) ∗ | — |
| Cardiac medication | |||
| β-Blocker | 94 (26.1%) | 88 (39.1%) ∗∗ | — |
| ACEI/ARB | 115 (31.9%) | 92 (40.8%) ∗∗ | — |
| Statin | 121 (33.6%) | 96 (42.7%) ∗∗ | — |
CAD, coronary artery disease; FSI, fasting serum insulin; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model assessments of insulin resistance; LDL-C, low-density lipoprotein cholesterol; T2DM, type 2 diabetes mellitus; and ACEI/ARB, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker. Student t test and χ2 test between groups A against B and A against C. ∗p < 0 : 01, ∗∗p < 0 : 001.
Diabetic patients with CAD (group B), when compared to the diabetic group (group A), had significantly higher serum lipids and values of fasting glucose, FSI, HbA1c, and HOMA-IR. Furthermore, when compared to group A, group B had substantially longer durations of diabetes, smoking, and sedentary lifestyle.
3.2. CAD Severity by SYNTAX Score
Applying SYNTAX score, and based on coronary angiography results, CAD severity was assessed. In patients with diabetes, the SYNTAX score as mean ± standard deviation were 21.8 ± 2 which was significantly lower than group of diabetes with CAD (28.3 ± 2.9, p=0.002). Forty-seven patients had a high score (≥33) and 62 patients showed an moderate score (23–32), while 116 patients revealed a low score (0–22; Figure 1).
Figure 1.
Estimated SYNTAX score in diabetic patients with CAD, evaluating the degree of CAD based on results from coronary angiography. A number of 47 patients showed a high score (≥33), 62 patients revealed an moderate score (23–32), and 116 patients showed a low score (0–22).
3.3. Genotype and Allele Frequencies
With a p value > 0.05, the genotype frequency was complied with the Hardy–Weinberg equilibrium (HWE). Table 2 displays the rs599839 polymorphism genotype distribution for each subject. Across all research participants, the AA genotype for rs599839 was more common (70.8% for group A and 75.6% for group B, respectively). The percentage of minor G allele for rs599839 was 18.1% and 14.7% in groups A and B, respectively. The χ2 test proved that the presence of at least one minor G allele in the dominant genetic model plays a protective role in diabetic patients with CAD (OR = 0.62, 95% CI: 0.41–0.82, and p=0.003). G allele when compared to A allele separately, showed a protective role in CAD diabetic patients (OR = 0.64, 95% CI: 0.47–0.86, and p=0.006; Table 2).
Table 2.
Genotypes and alleles frequencies among study subjects.
| Genotype | Group A DM (360; %) | Group B DM + CAD (225; %) | OR (95% CI) | p |
|---|---|---|---|---|
| AA | 255 (70.8) | 170 (75.6) | 1 | — |
|
| ||||
| AG | 80 (22.3) | 44 (19.6) | 0.82 (0.54–1.72) | 0.314 |
|
| ||||
| GG | 25 (6.9) | 11 (4.8) | 0.58 (0.27–0.84) | 0.002 |
|
| ||||
| Dominant model | ||||
| AG + GG | 105 (29.2) | 55 (24.4) | 0.62 (0.41–0.82) | 0.003 |
|
| ||||
| Recessive model | ||||
| AA + AG | 335 (93.1) | 214 (95.1) | 1.4 (1.06–1.92) | 0.613 |
|
| ||||
| Allele | ||||
| A | 590 (81.9) | 384 (85.3) | 1 | 0.006 |
| G | 130 (18.1) | 66 (14.7) | 0.64 (0.47–0.86) | |
Bold values indicates a statistically significant difference.
3.4. Genotypes Association with Lipid Parameters
Serum lipid concentrations were assayed according to the rs599839 genotypes and were adjusted for hypertension, smoking, BMI, and antidiabetic and statin therapy. Table 3 demonstrates that the GG genotype in diabetic patients with CAD was associated with substantially lower means of TC, TG, and LDL-C levels than AA genotype (224.5, 116.2, and 214.2 versus 251.6, 131.3, and 261.7 mg/dl, p=0.003, 0.007, and 0.025, respectively) and higher mean HDL-C (43.4 versus 24.6 mg/dl, p=0.026). The minor G allele of rs599839 was linked to significantly lower means of TC, TG, and LDL-C levels as well as higher HDL-C, when compared to the A allele.
Table 3.
Lipid profile estimated according to the genotypes of the studied patients.
| Genotypes | TC | LDL-C | HDL-C | TG |
|---|---|---|---|---|
| AA | 251.6 ± 15.3 | 131.3 ± 8.6 | 24.6 ± 4.6 | 261.7 ± 15.9 |
| GA | 231.1 ± 14.5 | 122.3 ± 6.4 | 32.35 ± 8.1 | 233.6 ± 21.1 |
| GG | 224.5 ± 12.3 ∗∗ | 116.2 ± 4.6 ∗∗ | 43.4 ± 6.5 ∗∗ | 221.4 ± 12.9 ∗ |
| G allele | 221.4 ± 10.3 ∗∗ | 110 ± 3.5 ∗ | 44.2 ± 5.1 ∗ | 214.2 ± 10.5 ∗ |
∗ANOVA test p value < 0.05 ∗∗ p value < 0.001.
Lipid parameters have been compared between group A and group B. It was found that group B (diabetic patients with CAD) had significantly higher means of TC, TG, and LDL-C levels and lower HDL-C level than group A (diabetic without CAD; Figure 2).
Figure 2.
Estimated lipid parameters in patients groups. Group B with CAD had significantly higher means of TC, TG, and LDL-C levels and lower HDL-C level than group A.
3.5. Relationships between Study Participants' Genotype and Cardiac Risk Factors
Additionally, we measure how the genotype and CAD risks affect the SYNTAX score that represents the severity of the disease. Regression analysis was used to separate the effects of rs599839 variant, age, gender, BMI, lipid parameters, duration of diabetes, hypertension, and other independent variables on SYNTAX score as the dependent factor in all CAD patients. As indicated in Table 4, the findings demonstrate that five of the independent variables have a statistically significant impact on the SYNTAX score: TC (p=0.002), LDL-C (p=0.008), TG (p=0.005), DM duration (p=0.025), and rs599839 (p=0.015).
Table 4.
Analysis of regression for related risk factors.
| Variable | Unstandardized coefficients | Standardized coefficients | ||
|---|---|---|---|---|
| B | SE | β | p | |
| Age | 0.612 | 0.027 | 0.018 | 0.327 |
| Gender | 0.524 | 1.432 | 0.650 | 0.266 |
| BMI | 0.618 | 0.129 | 0.18 | 0.093 |
| TC | 0.061 | 0.641 | 1.92 | 0.002 |
| LDL-C | 0.033 | 0.214 | 0.22 | 0.008 |
| TG | 0.021 | 0.816 | 1.89 | 0.005 |
| DM duration | 0.352 | 0.411 | 0.38 | 0.025 |
| HPN | 0.378 | 0.316 | 0.19 | 0.060 |
| rs599839 | −0.231 | 0.712 | −0.09 | 0.015 |
At levels of β-coefficient at two-tailed testing p < 0.05 is significant. Bold values indicates a statistically significant difference.
4. Discussion
DM (T2DM) is the most frequent cause of morbidity and death in patients with diabetes and a significant independent risk factor for CAD. Subjects with type 2 diabetes had a higher risk of heart failure by 112%, 53% of MI, 58% of stroke, and 10% higher risk of CAD. T2DM is thus a significant risk factor for CVD and its effects [14].
Improved knowledge of the etiology of T2DM and CVD is crucial for better clinical management, early patient identification and prediction, and identification of at-risk individuals. Our study aimed to explore the function of rs599839, assessing how this SNP may either induce or protect against CAD, and assessing the relationship between rs599839 and the risk, severity, and eventual clinical consequences of CAD in type 2 diabetes.
Our findings showed that in diabetic patients, the minor G allele of the variant rs599839 exerted a protective role against CAD in patients with T2DM (OR = 0.62, 95% CI: 0.41–0.82, and p=0.003). The genotypic frequencies of rs599839 decreased in the dominant model in patients with T2DM and CAD, which revealed that rs599839 AG + GG were accompanied by a decreased risk of CAD in patients with T2DM (OR = 0.64, 95% CI: 0.47–0.86, and p=0.003). The rs599839 minor G allele was linked to significant lower means of TC, TG, and LDL-C levels and higher HDL-C than A allele.
Maching with our findings, Arvind et al. [9] discovered that in Indians, the minor G allele has been linked to decreased levels of TC and LDL-C and a decreased risk against CAD (OR = 0.422 and 95% CI: 0.181–0.981). The protective role of minor G allele was proven through several genetic models in a Mexican population (OR additive model = 0.72, 95% CI: 0.56–0.92, and p=0.009 and OR dominant model = 0.66, 95% CI: 0.49–0.89, and p=0.007) [15].
Also, it was discovered to decrease serum levels of LDL-C in Japanese population (OR = 0.7 and 95% CI: 0.57–0.85) [16] and to decrease coronary stenosis risk in Arab CAD individuals (OR = 0.51 and 95% CI: 0.30–0.92) [17].
In meta-analyses, it has been found that the major A allele of rs599839 elevates the CAD risk in Asians (OR = 1.31 and 95% CI: 1.17–1.47) [18] and Europeans (OR = 1.11 and 95% CI: 1.08–1.15) [19].
Han et al. [20] reported higher risk of CAD for A allele in the Chinese population when patients and healthy controls were compared (OR = 8.37 and 95% CI: 1.70–41.0) [20].
Given that rs599839 is located on 1p13.3 chromosomal region, which has been connected to LDL-C in multiple recent GWAS, it may have a cardioprotective function [21, 22, 23, 24, 25, 26] and harbors four genes: PSRC1, CELSR2, MYBPHL, and sortilin 1 (SORT1).
It has been proven that SORT1 controlled the intracellular breakdown and endocytosis of lipoprotein lipase (LPL), which limits the rate with which TGs are hydrolyzed in lipoproteins [27]. Recently, sortilin has also been connected to the apoA-V endocytosis with its chylomicrons content [28].
Both TG metabolism and LDL-C metabolism were found to be significantly correlated with the rs599839 polymorphism by Kleber et al. [29]. They verified that the rs599839 polymorphism is linked to CAD and MI by observing a correlation between the radius of LDL-C particles and the genotype.
5. Conclusion and Recommendations
Our study highlighted the role of rs599839 polymorphism in diabetic patients when comparing group of diabetic patients with another diabetic group with CAD diagnosed angiographically. We proved the cardioprotective and lipid-lowering effect of rs599839 polymorphism in diabetic patients.
6. Strengths and Limitations
This study, as far as we know, is the first of its kind in Saudi Arabia to correlate the rs599839 polymorphism to T2DM and CAD. G allele of rs599839 polymorphism may alleviate the CAD risk in patients with T2DM which itself is an independent risk factor for CAD. Tight glycaemic control in diabetic patients having A allele is a must.
However, our study does have some limitations. First, we have a limited sample size, this may hinder results generalisation across the whole country. Larger cohort studies are needed. Additionally, participants were enlisted from two hospitals located in Saudi Arabia's western region. Selection bias would also have been difficult to avoid in a multicenter study.
Acknowledgments
University of Umm Al-Qura in Mecca, in the Kingdom of Saudi Arabia, and “Saleh Hamza Serafi” Chair of Coronary Heart Diseases Research provided support for this work. The KAMC and Al-Noor Specialty Hospital are to be thanked by the authors for their assistance with the sample collection process. We are also grateful for Samar N Ekram who served as scientific advisor, Amr A Amin for general administrative support, Mustafa Bogari for writing assistance, and Dema Neda Bogari for proofreading and language editing. A research grant numbered (-uqu-2023-UQU-R-3-1-HW-13013) from the Kingdom of Saudi Arabia's was presented from Research, Development and Innovation Authority (RDIA).
Data Availability
Upon reasonable request, the corresponding author can supply the datasets utilized and/or analyzed in the current work.
Ethical Approval
The principles outlined in the Declaration of Helsinki were adhered to in the conduct of this investigation. The Mecca region's Ministry of Health Ethics Committee approved and registered the approval number (H-02-K-076-0324-1099).
Consent
Every individual participant included in the study provided informed consent.
Conflicts of Interest
There is nothing relevant to disclose regarding the authors' non-financial or financial interests.
Authors' Contributions
Neda M. Bogari and Reem M. Allam contributed to concept and design. Zohor Asaad Azher, Hussain Banni, Abdulelah Awaji Madkhali, and Ahmed Alahmadi contributed to acquisition, analysis, or interpretation of data. Zohor Asaad Azher, Reem M. Allam, Abdulelah Awaji Madkhali, and Ahmed Alahmadi contributed to drafting of the manuscript. Abdellatif Bouazzaoui, Ahmad O. Babalghith, and Ahmed Hasan Mufti contributed to critical revision of the manuscript for important intellectual content. Reem M. Allam, Ahmad Hasan Mufti, and Hussain Banni contributed to statistical analysis. Neda M. Bogari and Ahmad O. Babalghith contributed to final approval of the version to be published. Reem M. Allam is accountable for all aspects of the work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Upon reasonable request, the corresponding author can supply the datasets utilized and/or analyzed in the current work.