Discovering Novel Biochemical and Genetic Markers for Coronary Heart Disease in Qatari Individuals: The Initiative Qatar Cardiovascular Biorepository.

BACKGROUND: We aimed to describe the creation and challenge of a DNA and plasma biorepository (Qatar Cardiovascular Biorepository) with linkage to the electronic health record of cardiovascular risk factors to facilitate discovery of novel genetic and proteomic biomarkers for coronary heart disease in Qatari individuals.
METHODS: A prospective case-control study was conducted between October 2013 and February 2018. CHD was defined as a history of an acute coronary syndrome (myocardial infarction [MI]/unstable angina) or coronary revascularization. Controls were identified from blood donors who had no history of coronary heart diesase. After informed consent, blood samples were obtained for DNA and plasma. Demographic, laboratory, and clinical variables were derived from the electronic medical record, and information regarding history of cardiovascular diseases and risk factors was collected from surveys. Challenges in establishing the biorepository were noted, and processes to promote use of the biorepository by Qatari investigators were put in place.
RESULTS: During the study period, 2671 individuals were approached; of them, 2087 participants were recruited (1029 patients and 1058 controls). Relevant risk factors were ascertained from the electronic health record and surveys. The mean age was 49 ± 16 years, with 61% males. Challenges included setting up the infrastructure for qatar cardiovascular biorepository, developing an informed consent document in Arabic/English, and meeting target recruitment goals. The prevalence of diabetes mellitus, hypertension, dyslipidemia, and smoking was 41%, 44.5%, 40%, and 19%, respectively. History of myocardial infarction, percutaneous coronary intervention, and coronary artery bypass surgery was 55%, 68%, and 17%, respectively, among patients.
CONCLUSIONS: This study addresses the challenges in setting up qatar cardiovascular biorepository, the first cardiovascular genomics biorepository in the Arab Middle Eastern region. QCBio is a unique resource for identifying genetic susceptibility variants and novel circulating markers for coronary heart disease in Qatari adults and enables individualized assessment of risk for coronary heart disease. Copyright:

INTRODUCTION

Significant progress has been made in reducing mortality from coronary heart disease (CHD) in the western world during the past decade.[1] Despite these successes, CHD remains a leading cause of death worldwide. The first manifestation of CHD could be sudden death, and a substantial number of individuals develop acute coronary syndrome (ACS) at a relatively young age.[2] CHD results from combined effects of genetic factors and environmental exposures.

Early onset of CHD is particularly common in urban centers of developing countries,[3] including rapidly developing high-income countries such as Qatar. Qatar has a high prevalence of obesity, metabolic syndrome, diabetes mellitus (DM), and CHD.[45] Considering the alarming risk of CHD in the Middle Eastern countries, there is an urgent need to identify biomarkers for early detection, prognostication, and identification of novel drug targets in diverse ethnic groups.[678]

Most of the studies conducted on CHD in developing countries were limited to retrospective analysis based on local or regional registries. To translate recent advances in cardiovascular genomics and proteomics into clinical practice, newly discovered genetic variants and biomarkers need to be evaluated in patients of diverse ethnic groups.[910]

Little is known about genetic susceptibility variants and circulating biomarkers in individuals from the Middle East cohorts. As step toward identifying newer molecular biomarkers, we established a DNA and plasma biorepository with linkage to cardiovascular risk factors to facilitate discovery of novel genetic and proteomic biomarkers for CHD. In addition, we documented challenges in setting up such a biorepository in this Arab Middle Eastern region.

METHODS

This is a prospective case–control study to establish a Qatar Cardiovascular Biorepository (QCBio) of plasma and DNA of Qatari patients with CHD and ethnicity-matched controls between October 2012 and February 2018. CHD cases include patients with coronary artery disease (CAD) which were identified from the Cardiac Catheterization Laboratory/Coronary Care Unit, Heart Hospital Clinics at Hamad Medical Corporation (HMC). HMC provides cardiac care to over 95% of patients in Qatar. Approximately 3200 ACS cases are admitted annually; 4000 total procedures and 900 primary percutaneous coronary interventions are being performed in the cath laboratory each year.

Coronary heart disease cases

Qatari patients with CHD, patients presenting with ACS, or patients seen at the cardiology clinic with confirmed diagnoses of CHD were approached for participation in the biorepository. To minimize phenotypic heterogeneity, only patients with a history of ACS were included in the study.

Controls

Ethnicity-matched controls were recruited from the blood bank and mobile blood donation campaign of HMC. Approximately 10,000 individuals donate blood annually at HMC. The individuals were typically screened using survey questionnaire, and those with chronic or infectious diseases, history of CHD, and vulnerable populations were excluded.

All participants were enrolled in the study after obtaining written informed consent. The informed consent document conformed to the guidelines regarding Bioethics Resources and human subject research (http://nih.gov/sigs/bioethics) and International Society of Biological and Environmental Biorepositories (http://www.isber.org). The participants were informed about the study objectives, risks and potential benefits of participation, and storage and future use of the samples. The consent form had separate check-off boxes seeking consent for biospecimens to be reused or shared with collaborating investigators. The individuals were informed about lack of immediate health benefit, potential for future CHD risk stratification, and choice of withdrawal from the study any time after participation.

Study survey

A survey questionnaire was used to collect information regarding sociodemographic characteristics (age, sex, marital status, level of education, and employment status), body mass index (BMI), conventional risk factors (DM, hypertension, dyslipidemia, obesity, and smoking), cardiovascular history, physical activity, lifestyle, past history, family history of diseases, and current treatment. The data from questionnaire were reviewed, validated, and stored in the QCBio database. We elicited a history of myocardial infarction (MI), coronary artery bypass surgery (CABG), stroke, carotid artery surgery, angioplasty or stent for limbs, limb amputation, and cardiac arrest.

Hypertension was defined as either systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg at two serial measurements within 3 months closest to the enrollment[11] or a prior diagnosis of hypertension with use of antihypertensive medication. Diabetes was defined as fasting blood glucose ≥126 mg/dL, random glucose ≥200 mg/dL, hemoglobin A1C ≥6.5%, or a prior diagnosis with oral hypoglycemic or insulin therapy. Dyslipidemia was defined as total cholesterol ≥220 mg/dL, or high-density lipoprotein cholesterol ≤40 mg/dL in men or ≤45 mg/dL in women, triglycerides ≥200 mg/dL, or the use of lipid-lowering medications. Smoking status was ascertained by survey questionnaire, and smokers were defined as either current or past smokers.

Collection, aliquoting, and storage of peripheral blood samples

Blood was collected in the fasting state by venipuncture; 25 ml was drawn into appropriate collection tubes (EDTA tubes, serum-separating tubes, and sodium citrate tubes) and labeled with a HMC-generated barcode ID number. The blood specimens were processed at the Hamad Molecular Genetics Laboratory and Interim Translational Research Institute (TRI) Laboratory.

The samples at the laboratory were centrifuged at 3750 rpm for 10 min at 4°C and were aliquoted under biohood into cryovials according to preassigned volumes and sample type [Figure 1]. Plasma and serum were aliquoted in 500 μl cryovials and stored at −80°C. DNA was extracted from 5 ml blood using Gentra AutoPure chemistries kit and quantified by UV DNA sample to be brought up to 250 ng/μl concentrations. DNA samples were stored in TE buffer in one master tube at −80°C. Furthermore, white blood cells (buffy coat) from a 10 ml tube of heparinized blood were isolated and stored at −80°C.

Figure 1

Blood sampling methods

Quality assurance

A patient numbering program was used to identify patients enrolled in the study and track patient recruitment. Accurate sample handling was achieved by adherence to standard operating procedures and by following general laboratory quality assurance and control standards. The time from blood withdrawal to storage was limited to <1 h, to maintain integrity of blood samples and minimize variability in results that might be affected by sample degradation. All tubes containing blood and blood products were barcode-labeled initially when received in the laboratory to ensure precise sample tracking.

Sample processing

Samples were collected and processed as follows: The purity of both DNA and RNA extractions has been measured by calculating the 260/280 ratio. A nucleic acid sample with 260/280 ratio of around 1.8 is considered as pure DNA and around 2.0 is regarded as pure RNA. Our results showed good yield and purity of nucleic acids, which is ideal for future genomic studies.

Confidentiality

Informatics and data security features were used to maintain patient confidentiality. Only members of the research team had access to the research data. Participants were identified by code numbers only in the database and in transcripts; all data are anonymous and tracked by linkable study numbers. No identifiable information was released outside the designated study personnel to maintain data integrity and confidentiality.

The study protocol was approved by the Institutional Review Board, Medical Research Center at HMC (NPRP: 5-1024-3-225), and ethical assurance was obtained from the Qatar Supreme Council of Health.

Trial registration: ClinicalTrials.gov Identifier: NCT03427489. Registered February 9, 2018. Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03427489.

Challenges of the study setting up

We encountered several challenges in setting up QCBio: primarily (a) establishing the infrastructure for obtaining, processing, and storing samples; (b) ethical issues related to developing an informed consent document in Arabic; and (c) meeting target recruitment goals.

Statistical analysis

Data were presented as proportions, median (range), or mean (±standard deviation) as appropriate. Patients and controls were compared for demographics, conventional risk factors, family history, and lifestyle. Differences between categorical and continuous variables were analyzed using Chi-square and Student's t-test, respectively, along with 95% confidence intervals. A significant difference was considered when the two-tailed P < 0.05. Data analysis was carried out using the Statistical Package for the Social Sciences version 18 (SPSS Inc., Chicago, IL, USA).

RESULTS

Between October 2013 and February 2018, ≈2671 individuals were approached; of them, 2087 participants were recruited (1029 CHD patients and 1058 non-CHD controls) [Figure 2]. Seven patients and three controls were withdrawn from the study (n = 10). The withdrawal was as follows: the subjects consented to participate in the study but refused to give blood as they perceived the amount of blood to be drawn was high. In addition, three patient samples were hemolyzed.

Figure 2

Study design

Demographics and clinical characteristics

The mean age of the CHD cases was 59.0 ± 11.0 versus 40.6 ± 13.7 years; P = 0.001 and was significantly higher as compared to controls. There were 61.2% male and 38.8% female participants in the study. Males (75%) predominated in the patient group whereas females outnumbered men recruited in the control group (P = 0.001).

The most prevalent risk factors were DM, hypertension, dyslipidemia, obesity, and smoking [Table 1]. When compared to controls, patients with CHD had higher frequency of DM, hypertension, dyslipidemia, obesity, and smoking as compared to controls (P < 0.001 for all) [Figure 3]. In CHD patients, the frequency of risk factors was comparable among males and females, except for obesity, which was higher in females, whereas smoking was observed more in the males. In controls, there was high prevalence trend of diabetes, dyslipidemia, and hypertension in females; still, males were more likely to smoke.

Table 1

Patients demographics, characteristics and risk factors

Variable	Overall (n=2087)	Controls n=1058 (51%)*	Patients n=1029 (49%)*	P
Age (years), mean±SD	49.1±15.6	40.6±13.7 (39.8-41.5)	59.0±11.0 (58.3-59.7)	0.001
Gender
Male	61.2%	53.5% (50.7-56.6)	70.0% (67.1-72.9)	0.001 for all
Female	38.8%	46.5% (43.6-49.5)	30.0% (27.0-32.9)
Cardiovascular health history
DM
Yes	40.4%	18.0% (15.7-20.3)	66.8% (63.8-69.8)	0.001 for all
Borderline	1.8%	1.0% (0.4-1.6)	2.7% (1.7-3.8)
Age of diagnosis of DM	45.1±11.1	43.4±11.7 (20.7-68.3)	45.6±10.9 (41.1-47.8)	0.03
Oral medication for DM	38.8%	17.1% (14.9-19.3)	64.3% (60.3-66.4)	0.001
Age of start of oral DM medication	45.3±11.1	43.8±11.8 (26.6-66.4)	45.7±10.9 (41-46.9)	0.06
Hypertension	44.5%	17.4% (15.2-19.7)	76.3% (72.8-78.3)	0.001
Age of diagnosis of hypertension	47.6±11.1	45.4±10.7 (33.7-66.7)	48.1±11.2 (42.6-48.9)	0.01
Dyslipidemia
No	51.0%	79.1% (73.6-78.6)	18.2% (14.9-19.8)	0.001 for all
Yes	40.3%	16.8% (14.0-18.4)	67.3% (62.0-68.1)
Don’t know	8.7%	4.1% (2.8-5.1)	14.0% (11.2-15.5)
Age of diagnosis of dyslipidemia (years)	49.6±10.4	47.7±10.6 (31.1-78.4)	50.4±10.2 (46.7-52.5)	0.02
Current dyslipidemia medication	45.2%	19.3% (17.1-22.9)	73.1% (67.0-73.7)	0.001
Highest cholesterol (mmol/L)
<5.17	29.2%	22.8% (19.9-25.7)	35.3% (32.1-38.6)	0.001 for all
5.17-6.18	5.8%	5.3% (3.7-6.8)	6.4% (4.7-8.0)
>6.18	4.1%	3.6% (2.3-4.8)	4.7% (3.3-6.2)
Don’t know	60.8%	68.4% (65.2-71.6)	53.5% (50.2-56.9)
Highest triglyceride level (mmol/L)
<1.70	28.3%	23.0% (20.1-25.8)	33.5% (30.3--36.7)	0.001 for all
1.70-2.20	4.3%	3.1% (1.9-4.3)	5.4% (3.8-6.9)
>2.20	3.9%	3.5% (2.3-4.8)	4.3% (2.9-5.6)
Don’t know	63.5%	70.4% (67.3-73.5)	56.8% (53.5-60.2)
Lower HDL cholesterol	8.0%	4.6% (3.1-6.0)	11.8% (9.5-14.1)	0.001
Lowest HDL cholesterol (mmol/L)
<1.0	37.2%	30.0% (26.5-33.5)	43.9% (40.2-47.5)	0.001 for all
>1.0	7.1%	6.0% (4.2-7.8)	8.1% (6.1-10.1)
Don’t know	55.6%	64.0% (60.3-67.6)	48.1% (44.4-51.7)
Laboratory
DNA concentration (μg/μl)	0.28±0.19	0.25±0.17 (−0.09-0.50)	0.29±0.20 (0.25-0.37)	0.01
DNA (μg)	26.04±24.0	26.9±25.8 (−6.55-34.8)	24.9±21.6 (17.7-26.2)	0.06
DNA purity 260/280	1.92±0.14	1.91±0.04 (1.84-1.93)	1.92±0.15 (1.91-1.93)	0.53
RNA concentration (ng/μl)	48.6±39.4	49.9±36.6 (50.7-63.7)	48.2±40.2 (44.6-51.3)	0.61
Total RNA (μg)	3.5±2.8	3.7±2.6 (3.55-4.46)	3.4±2.8 (3.13-3.59)	0.26
RNA purity	2.02±0.12	2.05±0.1 (2.03-2.06)	2.02±0.12 (2.01-2.03)	0.02
Vol in biobank (μl)	16.2±14.1	17.7±15.5 (13.9-18.0)	15.6±13.4 (12.9-14.7)	0.05

*95% CI. CI: Confidence interval, DM: Diabetes mellitus, SD: Standard deviation, HDL: High-density lipoprotein

Figure 3

Cardiovascular risk factors

The other comorbidities such as MI, stroke, lower limb amputation, cardiac arrest, and vascular interventions are described in Table 2.

Table 2

Presentations and co-morbidities

Variable	Overall (n=2087)	Controls n=1058 (51%)*	Patients n=1029 (49%)*	P
MI	25.2%	0.0% (0.0%-0.0%)	54.7% (51.6-57.9)	0.001
Age at which MI occur	54.5±10.6	-	54.5±10.6 (51.8-58.8)	-
Hospitalized for MI	48.4%	0.0% (0.0%-0.0%)	76.6% (73.4-79.8)	0.001
Angioplasty	31.6%	0.0% (0.0%-0.0%)	68.6% (65.6-71.6)	0.001
Age at angioplasty	55.1±10.2	-	55.1±10.1 (52.7-59.5)	0.91
Angioplasty (number of times)
1	84.9%	0.0% (0.0%-0.0%)	84.9% (82.1-87.7)	-
2	8.9%	0.0% (0.0%-0.0%)	8.9% (6.7-11.1)
3	3.0%	0.0% (0.0%-0.0%)	3.0% (1.7-4.4)
4	2.7%	0.0% (0.0%-0.0%)	2.7% (1.4-3.9)
5 or greater	0.6%	0.0% (0.0%-0.0%)	0.6% (−0.6-1.01)
Heart bypass surgery/CABG	8.1%	0.0% (0.0%-0.0%)	17.5% (15.0-19.9)	0.001
Age on CABG	53.6±9.4	-	53.6±9.4 (51.2-58.8)	-
CABG one time	100%	-	100%	-
Stroke, cerebral hemorrhage, or brain attack	1.5%	0.2% (−0.07-0.4)	3.0% (1.9-4.1)	0.001
Age of stroke	58.9±12.4	62±0	58.7±12.6 (52.9-64.5)	0.80
Need hospitalization	4.9%	1.0% (−0.1-2.1)	8.5% (5.4-11.6)	0.001
Carotid artery surgery due to arterial blockage	0.2%	0.0% (0.0%-0.0%)	0.3% (−0.04-0.7)	0.58
Age on carotid artery surgery	68±6.2	-	68±6.2 (52.5-83.5)	-
Angioplasty or stent due to arterial blockage of the leg	0.3%	0.0% (0.0%-0.0%)	0.7% (0.13-0.19)	0.007
Age on angioplasty or stent	53.2±1.2	-	53.2±1.2 (51.3-55.3)	-
Angioplasty or stent (leg) one time	100%
Bypass surgery due to arterial blockage of the leg	0.1%	0.0% (0.0%-0.0%)	0.2% (−0.08-0.5)	0.12
Age on bypass surgery	55±0	-	55±0	-
Bypass surgery (leg) one time	100%	0.0% (0.0%-0.0%)	0.2% (−0.08-0.5)	0.12
Lower limb amputation	0.3%	0.0% (0.0%-0.0%)	0.6% (0.07-1.03)	0.01
Age on amputation	54.5±1.3	-	54.5±1.3 (52.5-56.6)	-
Cardiac arrest and resuscitation	0.4%	0.2% (−0.07-0.4)	0.6% (0.07-1.03)	0.17
Age on cardiac arrest	48.7±5.8	46±11.3 (−55.6-147.7)	50±2.4 (46.1-53.9)	0.48
Hospitalization for heart failure	2.6%	1.6% (0.8-2.3)	3.8% (2.5-4.9)	0.002
Age on heart failure	56.0±15.1	43.6±11.5 (32.9-54.2)	61.1±13.5 (54.2-68.1)	0.007
Heart transplantation	0.2%	0.1% (−0.08-0.3)	0.3% (−0.04-0.7)	0.23

*95% CI. MI: Myocardial infarction, CABG: Coronary artery bypass surgery, CI: Confidence interval

The pattern of physical labors such as vigorous activity, moderate activity, and walking (≥10 min) within the last 7 days of survey was assessed. Controls were more likely to be involved in vigorous activity as compared to patients. However, patients were involved in physical activity as most reported moderate activity and walking than the controls. Time spent (<1 h and1–3 h) sitting during a week day was higher in patients, whereas controls were more likely to sit for longer hours (>4 h). Ever smoking (>100 cigarettes in entire life) was similar among cases and controls. Current smoking prevailed in the control group (P = 0.02).

Family history of heart attack before the age of 65 years showed that full brothers are more likely to have one or two episodes of heart attack at early age among CHD patients than controls. History of coronary artery angioplasty or bypass surgery, stroke, and aortic aneurysm was comparable for different blood-related family members between the two groups.

A higher frequency of parental death was observed in CHD patients as compared to controls [Table 3]. The most frequent cause of maternal death was heart attack, which was more evident in CHD patients. Similarly, heart attack (23%) and stroke (8.8%) were the predominant causes of paternal death.

Table 3

Survival status of parents and cause of death

Variable	Overall (n=2087)	Controls n=1058 (51%)*	Patients n=1029 (49%)*	P
Mother
Alive	56.0%	70.4% (67.5-73.4)	36.5% (32.9-40.1)	0.001 for all
Deceased	44.0%	29.6% (26.6-32.5)	63.5% (59.9-67.1)
Current age of mother	63.2±11.9	61.5±11.8 (60.1-62.9)	68.4±10.6 (66.4-70.9)	0.001
Age at death	65.8±13.4	65.6±13 (61.5-69.7)	65.9±13.7 (62.9-68.9)	0.91
Cause of death
Accident	2.4%	0.0% (0.0-0.0)	2.6% (−0.3-5.6)	0.60 for all
Heart attack	52.0%	45.5% (16.0-74.9)	52.6% (43.4-61.8)
Congestive heart failure	3.2%	9.1% (−7.9-26.1)	2.6% (−0.3-5.6)
Stroke	3.2%	9.1% (−7.9-26.1)	2.6% (−0.3-5.6)
Cancer	8.0%	9.1% (−7.9-26.1)	7.9% (2.9-12.8)
Others	10.4%	18.2% (−4.6-40.9)	9.6% (4.2-15.1)
Don’t know	20.8%	9.1% (−7.9-26.1)	21.9% (14.3-29.5)
Father
Alive	40.3%	55.0% (51.8-58.2)	20.1% (17.1-23.1)	0.001 for all
Deceased	92 (59.7%)	45.0% (41.8-48.2)	79.9% (76.9-82.9)
Current age of father	67.4±12.2	65.6±11.9 (63.8-67.3)	72.9±11.2 (70.1-75.7)	0.001
Age at death	68.1±12.3	69.2±14.6 (63.6-74.7)	67.9±11.9 (66.2-69.6)	0.61
Cause of death
Accident	5.4%	3.8% (0.8-6.9)	6.8% (2.9-10.7)	0.002 for all
Heart attack	23.0%	23.7% (17.0-30.4)	22.4% (15.9-28.8)
Congestive heart failure	5.0%	9.6% (4.9-14.2)	0.6% (−0.6-1.8)
Stroke	8.8%	7.7% (3.5-11.9)	9.9% (5.3-14.6)
Emphysema	0.6%	1.3% (−0.4-3.0)	0.0% (0.0-0.0)
Cancer	14.8%	16.0% (10.3-21.8)	13.7% (8.4-18.9)
Others	10.7%	12.8% (7.6-18.1)	8.7% (4.3-13.0)
Don’t know	31.5%	25.0% (18.2-31.8)	37.9% (30.4-45.4)

*95% CI. CI: Confidence interval

History of maternal heart attack or MI was significantly higher in patients (P = 0.04) and majority occurred at the age of ≥66 years. However, the risk of paternal heart attack or MI was comparable among the two groups. The risk of paternal heart attack in patients was significantly higher at the age of ≥66 years than the controls. Notably, the history of CABG, stroke, carotid endarterectomy, leg angioplasty and bypass, and aortic aneurysm was comparable among parents of the two groups, except coronary artery angioplasty, which was more evident in controls.

Interestingly, parental history of comorbidities such as diabetes, hypertension, dyslipidemia, and smoking did not differ significantly among cases and controls.

The banked biospecimens are processed and stored with specific study numbers for future genomic and proteomic analysis.

DISCUSSION

To the best of our knowledge, QCBio is the first biorepository of CHD with linkage to relevant clinical covariates in the Arab Middle East. The biorepository creates a unique resource for conducting genomic and proteomic studies to identify and validate biomarkers for diagnosis, prognostication, and response to therapy in Qatari White ethnicity. Samples are currently distributed among biobank (genomic analysis), antidoping center (metabolomic analysis), and Qatar TRI (proteomic analysis).

This study initially encountered multiple logistical challenges such as handling of blood specimens and extraction of DNA, which were eventually overcome by a proper coordination among the research team. The other challenge was raised during obtaining the written informed consent and extraction of blood specimens due to cultural barriers and myths about the genetic research. Moreover, many individuals were reluctant to disclose family medical information during questionnaire survey.

Few genetic researches were conducted in Qatar so far, and therefore, the patient recruitment was challenging as the potential participants were not so familiar to DNA extractions and genetic testing for research purpose. In our study, patient recruitment took more than double of the estimated time as a result of multiple challenges.

The main causes of refusals were fear of blood drawing and concerns about privacy violations in DNA storage and genetic testing. The ICF clearly stated options about the blood samples of the participants, either to permit or refuse to store their samples for future studies.

Previous genetic studies in Qatar were specific to certain gene variants and were not collecting detailed data on the diseases affecting other family members. The present study collected extensive information about the family members through survey. This included information about diabetes, hypertension, dyslipidemia, MI, angioplasty, CABG, stroke, and sudden death among parents or siblings. Providing such information about the family members was challenging and was one of the reasons to decline. Family members of a participant also played a major role in decision about consenting for the study. Interestingly, few participants joined the study by approaching the investigator, as one of their family members being admitted with MI.

Initially, it was proposed to draw 50 cc of blood from the participants to cover proteomic, metabolic, and genetic testing. Some of the subjects were concerned about this volume as they perceived this as high and refused to participate. Afterward, we reduced the blood volume required to 25 cc.

To address the challenges in recruitment, we utilized the print and visual media (i.e., newspaper and social media) to raise the awareness about the study. In addition, more flyers and posters briefing the study were placed in the waiting areas of Heart Hospital. Moreover, more engagements and discussions with families were conducted. The informed consent was drafted based on the informed consent document for Mayo Vascular Disease Biorepository. An Arabic version was created in conjunction with a bioethicist and the Hamad IRB.

The proposal for developing a biorepository for genetic research and its implications in early detection and prevention of cardiovascular diseases were briefed in the ICF. The ICF iterated the voluntary nature of participation as well as how the privacy and confidentiality of the patients will be ensured.

Cardiovascular disease in the Middle East

Despite the development of effective therapies, cardiovascular diseases remain the leading causes of death globally and in the Arab Middle East including Qatar. Patients presenting with ACS from the Gulf region tended to be younger than such patients in the Western world.[12] In the present study, the average age of patients was 59 years which is consistent with a registry based data from the Saudi Arabia showing a mean age of 58 years.[12] Previously, we compared ACS patients from the Arab Middle Eastern region (the Gulf Registry of Acute Coronary Events [Gulf-RACE]) to those enrolled in a multinational non-Arabian registry (the Global Registry of Acute Coronary Events [GRACE]).[13] The average age of patients in Gulf-RACE was nearly a decade younger than that in the GRACE study (i.e., 55 vs. 66 years old).

Moreover, a male predominance has been observed in patients with ACS from Qatar, which is consistent with our earlier report of male predominance (75%) in patients presented with ACS.[14] In comparison to males, females are underdiagnosed to have ACS in the Arab Middle Eastern region.[1415]

The most prevalent risk factors were DM, hypertension, dyslipidemia, obesity, and smoking. When compared to controls, the patients with CHD had higher frequency of these CVS risk factors. Patients from the Middle East were also more likely to have DM, highlighting the possible contribution of genetic factors in mediating CHD susceptibility in the Middle East.[13] This is further supported by the high incidence of consanguinity of marriages in Qatar and the Gulf region. Bener et al.[16] reported 51% rate of consanguinity among 876 married Qatar females surveyed. The most common type of consanguineous marriage was first-cousin marriage (26.7%). High rates of consanguinity have also be reported in the Arab population such as Egypt, Jordan, Kuwait, United Arab Emirates, Yemen, Oman, and Iran.[17] To date, most of the cardiovascular studies in the developing countries including the Arab Middle East are mainly epidemiologic based on registry data.[18] To move a step ahead, the QCBio supplements these registries and provides opportunities for further biomarker studies in the high-risk population existing as small isolated communities in the region.

Genetic research in Qatar

A genetic study based on 168 Qatari nationals by Hunter-Zink et al.[19] revealed that Qatari can be largely divided into three primary affinity groups namely of Arab origin (Bedouin tribes and affinity with Iranian “Persian'), the eastern populations (Central Asia), and the Bantu-speaking Africans. Another study suggested that individuals of indigenous Arab ancestry are descendants of the ancient Eurasian populations.[20] Fakhro et al.[21] performed a genome sequencing on 1161 Qatari subjects that revealed an average of 1.79% novel gene variations per individual genome. This finding could be used as a reference for future genetic disease research in Qatar.

A recent case–control study of type 2 diabetes (T2D) in Qatari population reported variability in the genetic risks of T2D in Qatari as compared to European and Asian populations.[22] These findings highlighted the importance of population-specific variation in the pathogenesis of common conditions, such as T2D and CAD, and the need for genetic studies in diverse populations. Consistent with previous findings, only two out of 23 loci associated with BMI in other populations have been also been linked to obesity in Qataris.[23] A prospective observational case–control study from Qatar investigated the role of single nucleotide polymorphisms (SNPs) with the risk of CAD.[24] The authors reported a significantly association for G allele of rs2483207 SNP with the severity and risk of CAD. Another study reported role of SNP (rs646776 and rs599839) with low-density lipoprotein-cholesterol and other lipid parameters in CAD patients.[25]

Cardiovascular biorepositories

Disparities in the global prevalence of CHD could be attributed to the genetic susceptibility and circulating biomarkers. Over the last years, genome-wide association studies (GWASs) have generated a flood of robust and replicable SNP associations for complex traits/diseases, including at least 120 loci influencing various atherosclerotic vascular diseases and related intermediate traits.[26]

The need for genetic predisposition and biomarker studies of cardiovascular diseases has led to the establishment of biorepositories in several countries; most of them are general and some longitudinal, predominantly in the developed world.[2728]

The Qatar Biobank[5] was established as the first Qatar national population-based prospective cohort study, which targeted recruitment of up to 60,000 men and women Qatari nationals and long-term residents, with extensive baseline clinical, metabolic, and behavioral phenotypic data, and blood, urine, and saliva samples were collected and stored. To date, the pilot phase has been reported which included 1209 participants (42% men and 58% women) with a median age of 39 years, and most of these participants were healthy.[5] It has been suggested that in the future, International Harmonization of National Biobanks (e.g., through ventures such as the Promoting Harmonisation of Epidemiological Biobanks in Europe; https://www.fhi.no/en/projects/fp6---phoebe---promoting-harmonisat/) will further increase statistical power and aid in comparison among the various biobanks.

Recently, a number of cardiovascular disease-specific biorepositories have been initiated to study the association of genetic susceptibility variants for atherosclerosis. The current cardiovascular biorepository complements established cardiovascular repositories, such as the Mayo VDB and the Duke CATHGEN Cardiovascular Genomics Biorepository.[29] In the Mayo Clinic, Vascular Repository[30] was established in 2006 which included more than 3000 patients with peripheral arterial disease, aortic aneurysm, carotid artery disease, pulmonary hypertension, and fibromuscular dysplasia. An electronic medical record (EMR)-based GWAS identified an SNP in PKAG2 to be associated with peripheral arterial disease (PAD) and two other SNPs were identified to be associated with poorly compressible arteries.

At HMC, the EMR archives clinical information, laboratory and imaging results, medications, and physician's notes, thereby serving as a resource for future genotype-phenotype studies. QCBio, by including CHD and controls of the understudied Qatari ethnicity, supplements these general and disease-specific biorepository studies. QCBio will foster GWAS as well as whole-genome/exome sequencing to discover novel CHD susceptibility variants and disease pathways in Qatari patients. The biorepository of plasma and DNA linked to demographic and clinical variables will facilitate biomarker studies of CHD risk, progression, and outcome. It will also help to identify biomarkers for early detection and prognostication and to explore newer targets for drug developments in this unique population and allow comparative studies with other ethnicities in this emerging field.

Limitations, major challenges, and opportunities

Arabs including Qatari nationals are genetically heterogeneous (three genotypic clusters of Arabian, Persian, and African origin) with higher rates of consanguinity.

The QCBio has a fully functional website for QCBio (http://qcbio. org/) as a resource of information of the project in the public domain. This secured website can be accessed by Qatari investigators to retrieve customized information for the biorepository database for future biomarker research. In addition, summarized description of the QCBio study has been published in the local media (Qatar Newspapers) and at social media level through Twitter account for public awareness. The future plan to embark upon the QCBio is to perform genomic association studies involving classic GWAS with the “Common Disease-Common variants,” pathway analysis, gene-based GWAS with the “Common Disease-Multiple Rare Variants.”

CONCLUSIONS

The establishment of QCBio provides a unique and robust infrastructure for cardiovascular research that facilitates individualized risk assessment for Qatari adults. It is an unparalleled and unique resource for conducting genomic and proteomic studies to identify and validate biomarkers for diagnosis, prognostication, and response to therapy in Qatari patients with CHD. Further studies using QCBio will foster collaborative research and help to develop a cardiovascular biomarker research program in Qatar.

Financial support and sponsorship

The QCBio was funded by National Qatar Research Fund NPRP No. 5-1024-3-225.

Conflicts of interest

There are no conflicts of interest.