The Impact of Type 2 Diabetes Mellitus on Cardiovascular Events in Saudi Arabia Now and Future
NCT ID: NCT06674161
Last Updated: 2024-11-05
Study Results
Results pending
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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Recruitment Status
NOT_YET_RECRUITING
Total Enrollment
80 participants
Study Classification
OBSERVATIONAL
Study Start Date
2024-11-30
Study Completion Date
2030-12-31
Brief Summary
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Cardiovascular disease (CVD) and Type 2 diabetes mellitus (T2DM) are major global health challenges with significant morbidity and mortality. In Saudi Arabia, the prevalence of CVD and T2DM is increasing. This poses a significant burden on the healthcare system. Despite extensive global research, there remains a lack of comprehensive studies examining the combined impact of CVD and T2DM in the Saudi population, particularly in the Western region. Objectives: The primary objectives of the study are: (1) quantify the incidence and mortality rates of CVD and T2DM across various demographics in the Western region of Saudi Arabia; (2) analyse the demographic, clinical, and lifestyle factors contributing to cardiovascular risk in individuals with both conditions; (3) understand the influence of cultural, social, and religious beliefs on health behaviours related to CVD and T2DM using social media and survey data; (4) develop a predictive model for forecasting future cardiovascular events in the Saudi population; and (5) evaluate the impact of the ICD11 classification of CVD. Methodology: The project will include team of experts from King Faisal Specialist Hospital and Research Centre, Madinah, Taibah University, Islamic University of Madinah, University of Prince Mugrin, and King's College University, London. We will work under four workstreams (WS) which are: WS 1: This group will supervise the data collection, analysis and ensure efficient running of the project. WS 2: In year 1, retrospective data will be collected on the service needs, long term outcomes, and mortality of patients with CVD and T2DM, CVD only, T2DM only, and neither. Between Year 1 to 5, we will collect data prospectively using the new ICD-11 criteria. Both, retrospective and prospective data will be used to develop an artificial intelligence (AI) based predictive model. WS 3: Social media survey will be undertaken to understand health beliefs and behaviours influencing health related outcome to CVD and T2DM. WS 4: Undertake economic analyses into long-term resource utilisation and cost of care for CVD and T2DM.
Expected Outcomes:
The study is expected to provide a detailed incidence and all-cause mortality CVD and T2DM in the Western region of Saudi Arabia. This will help in identifying risk factors and predictive for CVD and understand health beliefs and behaviours in Saudi Arabia. The data will help in developing policies, guidelines and awareness programmes in collaboration with policy makers. In conclusion, this study will impact by improving epidemiological knowledge and understanding of CVD and T2DM in Saudi Arabia. The project will support the overall mission of Saudi Vision 2030 with regard to increasing the health and well-being of the population.
Expected Outcomes:
The study is expected to provide a detailed incidence and all-cause mortality CVD and T2DM in the Western region of Saudi Arabia. This will help in identifying risk factors and predictive for CVD and understand health beliefs and behaviours in Saudi Arabia. The data will help in developing policies, guidelines and awareness programmes in collaboration with policy makers. In conclusion, this study will impact by improving epidemiological knowledge and understanding of CVD and T2DM in Saudi Arabia. The project will support the overall mission of Saudi Vision 2030 with regard to increasing the health and well-being of the population.
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Detailed Description
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Innoviation:
The proposed study will introduce a novel theoretical framework and methodology for comprehensive investigation of the interrelationship between the various combinations of CVD and T2DM within Saudi Arabia.
Key features of the innovative theoretical concept include:
1. Population specific focus: Our research will be focused on the population of Saudi Arabia based demographic, cultural and environmental factors influencing the incidence and outcomes of CVD and T2DM.
2. Health belief: Our study will investigate health beliefs and behaviour related to CVD and T2DM. This would involve using artificial intelligence data analytic per direct questionnaires, published literature, and social media.
3. Predictive modelling: We aim to conduct a predictive modelling and develop a functional model that predicts future CVE and diabetes related cardiovascular complications.
Key innovative methodologies and approaches include:
1. Retrospective population-based study: A large population-based retrospective study will be conducted to determine the incidence and mortality of CVD with and without T2DM. This methodology will thus provide robust epidemiological data by adjusting for possible confounders using Cox regression models in determining hazard ratios.
2. Combination of CVD with/without T2DM: The results will be compared across the four groups: individuals with no CVD and T2DM, CVD only, T2DM only, and both conditions.
3. Risk factor insight: Understanding the risk factors concerning CVD and T2DM in Saudi Arabia.
4. Using AI to evaluate health belief: Conducting large scale survey on health belief using AI to analyse data obtained from existing literature, digital resources, social media platforms, and direct questionnaires.
5. Development of predictive model for future CV events: This model will be based on detailed analysis and interpretation of collected data. This will facilitate the forecasting of disease patterns and guiding preventive approaches.
6. Collaborative approach: The project involves a collaborative effort by experts from King Faisal Specialist Hospital and Research Centre, Madinah, Taibah University, Islamic University of Madinah, University of Prince Mugrin, and King's College University, London. This multidisciplinary approach ensures a comprehensive analysis that utilises diverse expertise
Methodology:
Aims and objectives We aim to improve the lives of people with CVD, T2DM and other risk factors through innovative data collection, use of data, and design personalised and optimised management strategies. We will accomplish this by creating four workstreams (WS), which will run in parallel and work collaboratively throughout the programme.
1. Workstream 1 (WS1): We will engage with those who have suffered from CVD. They will grouped into three main groups: those with Stroke, IHD and other CVD. The three groups will be subdivided into two subcategories: those with and without T2DM. This group will have individuals who are survivors of this disease, carers, public health experts, clinicians and policymakers. This group will shape the data collection, analysis and use of the data.
2. Workstream 2 (WS2): We will work towards estimating the impact of the WHO ICD11 definition of stroke, atherosclerotic CAD and T2DM. The group will also try to understand the long-term outcome and care needs of CVD survivors both individually and as a population.
3. Workstream 3 (WS3): We will use social media, community forums and healthcare facilities to understand the health beliefs of the individual and population groups. We will try to understand the effect on the health outcome of the individual and the population of these health beliefs.
4. Workstream 4 (WS4): This stream will estimate the economic impact of care, service models, and options with the aim of improving decision-making for policymakers, survivors, and individuals at higher risk, as well as their families.
The proposed study will introduce a novel theoretical framework and methodology for comprehensive investigation of the interrelationship between the various combinations of CVD and T2DM within Saudi Arabia.
Key features of the innovative theoretical concept include:
1. Population specific focus: Our research will be focused on the population of Saudi Arabia based demographic, cultural and environmental factors influencing the incidence and outcomes of CVD and T2DM.
2. Health belief: Our study will investigate health beliefs and behaviour related to CVD and T2DM. This would involve using artificial intelligence data analytic per direct questionnaires, published literature, and social media.
3. Predictive modelling: We aim to conduct a predictive modelling and develop a functional model that predicts future CVE and diabetes related cardiovascular complications.
Key innovative methodologies and approaches include:
1. Retrospective population-based study: A large population-based retrospective study will be conducted to determine the incidence and mortality of CVD with and without T2DM. This methodology will thus provide robust epidemiological data by adjusting for possible confounders using Cox regression models in determining hazard ratios.
2. Combination of CVD with/without T2DM: The results will be compared across the four groups: individuals with no CVD and T2DM, CVD only, T2DM only, and both conditions.
3. Risk factor insight: Understanding the risk factors concerning CVD and T2DM in Saudi Arabia.
4. Using AI to evaluate health belief: Conducting large scale survey on health belief using AI to analyse data obtained from existing literature, digital resources, social media platforms, and direct questionnaires.
5. Development of predictive model for future CV events: This model will be based on detailed analysis and interpretation of collected data. This will facilitate the forecasting of disease patterns and guiding preventive approaches.
6. Collaborative approach: The project involves a collaborative effort by experts from King Faisal Specialist Hospital and Research Centre, Madinah, Taibah University, Islamic University of Madinah, University of Prince Mugrin, and King's College University, London. This multidisciplinary approach ensures a comprehensive analysis that utilises diverse expertise
Methodology:
Aims and objectives We aim to improve the lives of people with CVD, T2DM and other risk factors through innovative data collection, use of data, and design personalised and optimised management strategies. We will accomplish this by creating four workstreams (WS), which will run in parallel and work collaboratively throughout the programme.
1. Workstream 1 (WS1): We will engage with those who have suffered from CVD. They will grouped into three main groups: those with Stroke, IHD and other CVD. The three groups will be subdivided into two subcategories: those with and without T2DM. This group will have individuals who are survivors of this disease, carers, public health experts, clinicians and policymakers. This group will shape the data collection, analysis and use of the data.
2. Workstream 2 (WS2): We will work towards estimating the impact of the WHO ICD11 definition of stroke, atherosclerotic CAD and T2DM. The group will also try to understand the long-term outcome and care needs of CVD survivors both individually and as a population.
3. Workstream 3 (WS3): We will use social media, community forums and healthcare facilities to understand the health beliefs of the individual and population groups. We will try to understand the effect on the health outcome of the individual and the population of these health beliefs.
4. Workstream 4 (WS4): This stream will estimate the economic impact of care, service models, and options with the aim of improving decision-making for policymakers, survivors, and individuals at higher risk, as well as their families.
Conditions
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Mortality of Patients with CVD
Mortality of Patients with CVD and T2DM
Mortality of Patients with T2DM
Mortality of Patients with Neither
Morbidity of CVD
Cardiovascular Diseases
Cardiovascular Diseases Risk
Study Design
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Observational Model Type
ECOLOGIC_OR_COMMUNITY
Study Time Perspective
OTHER
Study Groups
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CVD with type 2 diabetes
No interventions assigned to this group
CVD without type 2 diabetes
No interventions assigned to this group
T2DM only without CVD
No interventions assigned to this group
No T2DM and no CVD
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
All adult patients with available records in the western region of Saudi Arabia
Exclusion Criteria
age below 18 years
Minimum Eligible Age
18 Years
Eligible Sex
ALL
Accepts Healthy Volunteers
No
Sponsors
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King Faisal Specialist Hospital & Research Center
OTHER
Sponsor Role
lead
Responsible Party
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Fahad Ahmed Wali
Consultant, Endocrinology
Responsibility Role
PRINCIPAL_INVESTIGATOR
Central Contacts
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References
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Related Links
Access external resources that provide additional context or updates about the study.
https://diabetesatlas.org/
IDF Diabetes Atlas \[Internet\]. \[cited 2024 Jul 31\]
https://www.statista.com/statistics/617136/digital-population-worldwide/
Internet and social media users in the world 2024 \| Statista \[Internet\]. \[cited 2024 Aug 1\].
https://www.statista.com/topics/9947/social-media-usage-in-saudi-arabia/
Social media usage in Saudi Arabia - statistics \& facts
https://link.springer.com/article/10.1007/s13300-019-00698-9
Prevalence of Established Cardiovascular Disease in Patients with Type 2 Diabetes Mellitus in the UK
https://www.who.int/publications/i/item/9789240027053
World health statistics 2021. monitoring health for the SDGs, sustainable development goals. Geneva: World Health Organization 2021. Licence: CC BY-NC-SA 30 IGO.
Other Identifiers
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RAC/M 2024-31
Identifier Type: -
Identifier Source: org_study_id
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Risk Factors of Incidence of Type 2 Diabetes and Cardiovascular Diseases---Shanghai Baoshan Study
NCT00949416
UNKNOWN
Effect the Glycemic Control on Cardiac Function
NCT06761820
NOT_YET_RECRUITING
The Role of Insulin Resistance in Patients With Heart Failure and Type 2 Diabetes
NCT02559128
UNKNOWN
Diabetes Complications and the Risk of Cardiovascular and Cerebrovascular Diseases
NCT04983277
NOT_YET_RECRUITING
Arterial Stiffness and Complication Risk in Type 2 Diabetes
NCT02001532
COMPLETED
Frequency of Asymptomatic Left Ventricular Diastolic Dysfunction in Diabetes Mellitus Type 2
NCT03196687
UNKNOWN
Impact of Glycemic State on Patients ST Elevation Myocardial Infarction With Primary Percutaneous Coronary Angioplasty
NCT03266978
UNKNOWN
Non-traditional Cardiovascular Risk Factors and Atherosclerosis in Type 2 Diabetes
NCT00256607
COMPLETED
Mechanisms and Reversibility of Heart Failure Associated With Diabetes
NCT01970319
COMPLETED
Cohort Study in Type 2 Diabetes in China
NCT01076023
UNKNOWN
Exercise-induced Blood Glucose Response in Patients With Type 2 Diabetes
NCT03335930
COMPLETED
NA
Prevalence of Macrovascular Disease in Type 2 Diabetes Mellitus
NCT00298844
UNKNOWN