1,721,137 research outputs found
The Qatar Biobank: background and methods
Get PDFBackground: The Qatar Biobank aims to collect extensive lifestyle, clinical, and biological information from up to
60,000 men and women Qatari nationals and long-term residents (individuals living in the country for ≥15 years)
aged ≥18 years (approximately one-fifth of all Qatari citizens), to follow up these same individuals over the long
term to record any subsequent disease, and hence to study the causes and progression of disease, and disease
burden, in the Qatari population.
Methods: Between the 11th-December-2012 and 20th-February-2014, 1209 participants were recruited into the pilot
study of the Qatar Biobank. At recruitment, extensive phenotype information was collected from each participant,
including information/measurements of socio-demographic factors, prevalent health conditions, diet, lifestyle,
anthropometry, body composition, bone health, cognitive function, grip strength, retinal imaging, total body dual
energy X-ray absorptiometry, and measurements of cardiovascular and respiratory function. Blood, urine, and saliva
were collected and stored for future research use. A panel of 66 clinical biomarkers was routinely measured on
fresh blood samples in all participants. Rates of recruitment are to be progressively increased in the coming period
and the recruitment base widened to achieve a cohort of consented individuals broadly representative of the
eligible Qatari population. In addition, it is planned to add additional measures in sub-samples of the cohort,
including Magnetic Resonance Imaging (MRI) of the brain, heart and abdomen.
Results: The mean time for collection of the extensive phenotypic information and biological samples from each
participant at the baseline recruitment visit was 179 min. The 1209 pilot study participants (506 men and 703
women) were aged between 28–80 years (median 39 years); 899 (74.4 %) were Qatari nationals and 310 (25.6 %)
were long-term residents. Approximately two-thirds of pilot participants were educated to graduate level or above.
Conclusions: The pilot has proven that recruitment of volunteers into the Qatar Biobank project with intensive
baseline measurements of behavioural, physical, and clinical characteristics is well accepted and logistically feasible.
Qatar Biobank will provide a powerful resource to investigate the major determinants of ill-health and well-being in
Qatar, providing valuable insights into the current and future public health burden that faces the country.Qatar Foundation for Education, Science and Community Development and the Supreme Council of Healt
Qatar Biobank: COVID-19 biorepository project
Get PDFBackground: The rapid spread of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its resulting disease (COVID-19) is one of the greatest global public health crisis of the recent decades 1 . The COVID-19 Biorepository is a national project aimed to support the high demand of biomedical research by multiple groups and the need to have access to high quality, curated clinical data, and specimens contributing to the understanding of, and response to, the COVID-19 pandemic and its impacts in Qatar 2, 3 . Methods/Case presentation: Patients with a laboratory diagnosis of COVID-19, who were Qatar residents that could communicate in Arabic, English, Hindi, and Urdu were eligible to participate in the study. COVID-19 diagnosed patients were recruited at the time of their disease period from the main three public hospitals (Communicable Disease Center, Cuban, and Hazm Mebaireek Hospitals) serving as isolation facilities of symptomatic patients in Qatar, during a 7-month period from March 2020 until September 2020. Consented participants were followed up on a weekly basis until recovery, and then monthly for a year. Sociodemographic and clinical data were collected in electronic questionnaires via a face-to-face interview by trained Qatar Biobank (QBB) staff. Results: A total of 2097 consented participants were recruited up to September 2020, males (N = 1050) and females (N = 1047), with a mean age of 41 years (SD: 15.5). 61.0% of the participants had at least one follow up while 27% adhered to monthly follow up visits. Data was collected for 99.7% of the participants, while the follow up process is still ongoing. In total there are 107,171 high quality specimens in the biorepository including plasma, erythrocytes, buffy coat, serum, PAXgene whole blood, nasopharyngeal secretions, and DNA. Conclusion: The COVID-19 Biorepository is a national asset to illuminate the pathophysiological and identify markers of disease prognosis as well as to describe the clinical features and epidemiology of COVID-19 in Qatar and worldwide.qscienc
Qatar Biobank Cohort Study: Study Design and First Results.
No full textThe authors describe the design, implementation and results of Qatar Biobank (QBB) for the first 10,000 participants. QBB is a prospective population-based cohort study in Qatar, established in 2012. QBB's primary goal was to establish a cohort accessible to the local and international scientific community providing adequate health data and biological samples enabling evidence-based research. The study design is based on an agnostic hypothesis, collecting data using questionnaires, biological samples, imaging data and omics. QBB aims to recruit 60,000 participants, men and women, adult (age ≥ 18 years) Qataris or long-term residents (≥ 15 years living in Qatar) and follow up with them every 5 years. Currently, QBB has reached the 28% (n=17,065) of the targeted population and more than 2 million biological samples. QBB is a multinational cohort including 33 different nationalities with a relatively young population (mean age, 40.5 years), highly educated (50% university-educated) with high monthly incomes. The four main non-communicable diseases found among QBB population are Dyslipidemia, Diabetes, Hypertension and Asthma with a 30%, 17.3%, 16.7% and 9% prevalence, respectively. QBB repository can provide data and biological samples sufficient to demonstrate valid associations between the genetic and/or environmental exposure and disease development to the scientists worldwide
Qatar Biobank and Qatar Genome Programs Road Map
Get PDFQatar Biobank “QBB” is a large-scale, long term medical research initiative for the population of Qatar, which will serve as a platform and driver of biomedical research to achieve benefits for Qatar’s communities. Following on from the announcement of the Qatar Genome Project in 2013 by Her Highness Sheikha Moza bint Nasser, Chairperson of Qatar Foundation “QF”, a National Genome Committee “NGC” is tasked with the role of driving and advising the development of the Genome Project in Qatar. To ensure the successful implementation and completion of a project of this complexity, it was important to consider carefully the organizational structure of Genome Qatar to enable success, so the decision was to establish QG project within the existing framework of the government of QBB. A road map constitutes of seven key building blocks, were identified to address the critical success factors and be managed and overseen by the Board of Trustee for both QBB and QG program. The seven building blocks are 1-Develop a National Health Information System, 2-Enhancement of the National Biobank, 3-Develop Genomics Infrastructure,4- Develop Policy Framework for genomics and precision medicine, 5-Workforce Development ,6- Research and Partnership and 7- Clinical/Medical implementation. To successfully accomplish this ambitious role a road map is initiated through a pilot phase to establish the infrastructure and human capacity for 12-18 months in order to tackle problems, identify loopholes, analyze the needs and optimize systems that are needed for the entire Qatar Genome project. The aim is to sequence 1000-3000 genomes to develop a good model of practice for regulatory compliant, sample collection and storage, high quality data generation, analysis and annotation pipeline development and data warehouse establishment
Predicting hypertension using machine learning: Findings from Qatar Biobank Study
Get PDFHypertension, a global burden, is associated with several risk factors and can be treated by lifestyle modifications and medications. Prediction and early diagnosis is important to prevent related health complications. The objective is to construct and compare predictive models to identify individuals at high risk of developing hypertension without the need of invasive clinical procedures. This is a cross-sectional study using 987 records of Qataris and long-term residents aged 18+ years from Qatar Biobank. Percentages were used to summarize data and chi-square tests to assess associations. Predictive models of hypertension were constructed and compared using three supervised machine learning algorithms: decision tree, random forest, and logistics regression using 5-fold cross-validation. The performance of algorithms was assessed using accuracy, positive predictive value (PPV), sensitivity, F-measure, and area under the receiver operating characteristic curve (AUC). Stata and Weka were used for analysis. Age, gender, education level, employment, tobacco use, physical activity, adequate consumption of fruits and vegetables, abdominal obesity, history of diabetes, history of high cholesterol, and mother's history high blood pressure were important predictors of hypertension. All algorithms showed more or less similar performances: Random forest (accuracy = 82.1%, PPV = 81.4%, sensitivity = 82.1%), logistic regression (accuracy = 81.1%, PPV = 80.1%, sensitivity = 81.1%) and decision tree (accuracy = 82.1%, PPV = 81.2%, sensitivity = 82.1%. In terms of AUC, compared to logistic regression, while random forest performed similarly, decision tree had a significantly lower discrimination ability (p-value<0.05) with AUC's equal to 85.0, 86.9, and 79.9, respectively. Machine learning provides the chance of having a rapid predictive model using non-invasive predictors to screen for hypertension. Future research should consider improving the predictive accuracy of models in larger general populations, including more important predictors and using a variety of algorithms
The Implementation of an Integrated Management System at Qatar Biobank
Get PDFQatar Biobank (QBB) is a platform that will make vital health research possible through its collection of samples and information on health and lifestyle from the local population of Qatar. The goal of QBB is to collect, process, store, and finally share high-quality biological samples and associated data for research purposes with the research community. To do this, a series of standardized procedures following evidence-based practices are required, and QBB is achieving this by implementing an integrated management system (IMS) that incorporates ISO 9001: 2015 and ISO 27001: 2013 standards. ISO 9001 is one of the most commonly implemented quality management systems as it is applicable to any size of organization. ISO 27001: 2013 is increasingly popular as organizations look to manage their data and information security, especially in the light of the recent General Data Protection Regulation legislation and an ever-changing digital landscape. QBB has achieved certification in both ISO 9001: 2015 (originally 2008 standard) and ISO 27001: 2013 since 2014. In 2016, during preparations for recertification of both standards in 2017, QBB chose to integrate both of the management systems in preference to running them in parallel, without compromising the goals and objectives of QBB. The IMS has ensured that rigorous processes and controls are implemented to not only manage the quality of internal and external processes and services provided, but the privacy and confidentiality of data collected during a participant visit are consistently protected as well as a proactive approach to identifying and managing risk within the organization. This article will explore the impact of implementing an IMS on the continuous improvement of services within QBB
Common indications for Referral to the Healthcare system for COVID-19 recovered patients versus Qatar Biobank study population: A descriptive analysis
No full textBackground and Aim of the Work: Qatar Biobank (QBB) is actively acquiring data on the range of short-and long-term health impacts associated with COVID-19. This is performed through the COVID-19 biorepository National project. In this report, we describe the most common indications for the referral to Qatar's healthcare system of COVID-19 biorepository participants in comparison with the Qatar Biobank (QBB) general population study. Methods: Patients with a laboratory diagnosis of COVID-19, who were Qatar residents that could communicate in Arabic, English, Hindi and Urdu were eligible to participate in the COVID-19 biorepository project. Biological samples of Consented participants were collected on a weekly basis until recovery, and then monthly for a year. Participants were also offered a bone density scan three months after recovery and non-contrast MRI brain and whole-body scan six months after recovery. Number of participants requiring referral for medical follow up after recovery for any abnormal clinically significant findings were recorded and statistically compared to general population referred participants. Results: The majority of referrals for the general population study was for osteopenia versus diabetes for the COVID-19 biorepository project Conclusion: Descriptive analysis of the referral data of the COVID-19 participants and QBB general population (not previously affected by the virus) shows a clear difference between the two popu-lations' reasons for referrals. Diabetes for COVID 19 recovered participants versus osteopenia for general population. (www.actabiomedica.it).Scopu
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Conception, Implementation, and Integration of Heterogenous Information Technology Infrastructures in the Qatar Biobank
No full textThe overall goal of the Qatar Biobank (QBB) is to collect, manage, and distribute high-quality human biospecimens with appropriate clinical and/or research annotation and associated phenotypic data, aiming to be an important and essential resource of medical research and evidence-based health care system policies in Qatar. To manage and collect large volumes of data, the QBB has been investing in a number of information management solutions, trying to avoid inflexibility of traditional systems and accommodate changes in data sources and workflows. This article aims to present the information technology solutions of QBB based on a free, open-source software solution, considered a reliable alternative to commercial solutions. After evaluating the free, open-source software solutions available for biobanks, Onyx from ObiBa was utilized to develop custom components to interface various clinical devices, LIMS and Picture Archiving and Communication System, which has varying integration capabilities. This is a showcase for biobanks to carefully evaluate and select hardware and software to automate their operations providing the functions required for business continuity
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