Do you know that Type 2 Diabetes affects 500 million people worldwide? Treatments for Type 2 Diabetes are sometimes reactive rather than proactive, so managing it can feel like walking a tightrope.
One missed workout, a wrong diet, or a stressful day can spike blood sugar levels out of control.
But what if any artificial intelligence technology predicts problems before they happen? Here comes digital twins technology in the scenario.
Imagine a virtual version of yourself inside a computer, where you and doctors can test several treatments, meal plans, and lifestyle changes.
By creating a digital replica of a human’s body, digital twins are constantly learning, adapting, and helping you to regulate your diabetes care in real time.
Explore how digital twins are revolutionising Type 2 Diabetes treatment and put patients back in control!
Understanding Type 2 Diabetes
Type 2 Diabetes is a chronic condition that occurs when your body can not use insulin properly. This led to constant high blood sugar (hyperglycemia), which is a condition that you have to manage your entire life.
Normal blood sugar levels are 70 to 99 milligrams per deciliter. If your blood sugar remains undiagnosed, you may experience a 126 mg/dl or high blood sugar level.
Type 2 Diabetes is very common in the UK, with almost 90% of diabetes cases being Type 2. Most people affected by Type 2 Diabetes are over 45; however, it also affects some younger people below 45.
Common Causes and Risk Factors
- Obesity
- Poor diet
- inactivity
- Genetics
- Older age (45+)
- High blood pressure & cholesterol
The Concept of Digital Twin Technology in Healthcare
A digital twin is a virtual model of a real-world system, and the concept of it has garnered attention widely in biomedical and clinical research.
In medicine, a digital twin is a virtual representation of a patient that integrates their clinical data, lifestyle information, and genomic data.
Using machine learning, mechanistic models, and AIs, digital twins process data and stimulate biological functions. The digital twin constantly evolves with the patient, updating to reflect the patient’s current health status.
Digital Twin is a digital model of a real person, crafted using data including age, weight, activity level, meal types, sleep, and medical history. Digital Twins are helping the healthcare system to predict diseases even before they happen.
This technology utilises data to provide novel perspectives for managing chronic diseases, such as diabetes. Digital Twin offers a personalised treatment plan and potentially reverses the condition.
Digital Twins for Type 2 Diabetes Prevention
1. Data Collection
Advanced AI technologies integrate multiple data sources from clinical laboratory tests, genomic information, and EMRs. Wearable sensors or apps collect constant data on glucose level, sleep type, heart rate, diet, activity, and more.
Through combining large datasets on lifestyle surveys, socioeconomic factors, and algorithms generated detailed information regarding a patient’s risk factors.
2. Virtual Model Creation
AI and machine learning combine this data to build a digital twin for an individual. A Digital twin model is a mirror of an individual’s specific metabolic system.
The Digital Twin model simulates how several foods, activities, and sleep habits affect the users’ metabolism. The model also predicts the blood sugar levels and disease risk.
3. Provides Personalised Guidelines
The technology provides personalised recommendations and digital nudges to inspire healthier choices. For example, it provides advice like “eat this food to reduce blood sugar level”, or how much exercise you actually need per week, and more.
4. Proactive Intervention
The Digital Twins technology understands an individual’s unique metabolic responses. Thus, it helps you to prevent high glucose spikes and inactivity, address the root cause of Type 2 Diabetes, not only symptoms.
5. Outcomes
Studies show that this model helps people with significant weight loss, reduced medication, and Type 2 Diabetes remission. This technology is shifting the T2D landscape from reactive care to truly personalised, predictive medication.
Digital Twins vs Traditional Prevention Methods for Type 2 Diabetes
Digital Twins Approach
- Personalised treatment and prevention plans: DTs create a virtual replica of a patient using continuous data (EHRs, activity, sleep habits, food habits) to create a digital model of an individual’s body and disease progression.
- Dynamic Recommendation: AI models provide real-time preventive measures on specific activities or food recommendations to control blood sugar spikes.
- Proactive care: This technology predicts issues and adapts preventive care dynamically, moving beyond generic advice.
Traditional Prevention Methods
- Generalised guidelines: Focus on broad advice on eating healthy, maintaining a healthy weight, and exercising.
- Periodic monitoring: Fully relies on periodic blood sugar checks, clinical visits, and getting advice from a medical practitioner.
- Reactive care: Very often, interventions are generalised and altered less frequently according to periodic data.
The Future of Digital Twins Technology in T2D Prevention
The transformative changes driven by Digital Twins technology will reshape a comprehensive approach to Type 2 Diabetes care.
It will focus more on emphasising prevention and proactive management instead of generalised guidelines and medications.
Since the capabilities and language models of Digital Twins are continuously evolving, it is expected that diabetes management will be redefined in the upcoming years.
The upgraded Digital Twins models will improve patient outcomes, reduce diabetes treatment costs, and offer a quality of life for individuals. We can expect three new advancements in T2D prevention and care:
- More advanced Digital Twins models Integration: Future research could explore a deeper integration of Digital Twins models and AI technologies for an enhanced diabetes prevention, management, and care.
- Human and AI Collaboration: Anticipating human interference with AI models will improve clinical decision-making to build trust and improve outcomes.
- Patient-centric Interfaces: Implementing adaptive and user-friendly AI interfaces is crucial for better patient involvement with this technology. This collaboration will empower self-management, providing real-time feedback and personalised health concerns.
How Individuals Can Benefit Today from Digital Twins Technology
Any individual with Type 2 Diabetes can benefit from Digital Twin models through highly personalised and real-time guidelines. Some key benefits include:
- Tailored recommendations that go beyond generalised advice
- Improved glycemic control
- Significantly reduce or even discontinue their anti-diabetic medications
- Weight management
- Stay motivated and adhere to their personalised healthcare plans
- Control of high blood pressure, fatty liver disease, and reducing the risks of cardiovascular disease and kidney disease
Conclusion
Digital Twins technology offers challenging pathways to enhance diabetes management, from lifestyle alteration to drug adherence, with the ultimate aim of remission.
DT models emphasise their potential in diagnosis, intervention, and monitoring, enabling personalised care with evidence-based decisions.
When it comes to Type 2 Diabetes management, more personalised approaches are essential for its prevention and daily care.
However, some technical challenges, such as model hallucinations and trustworthiness, need ongoing awareness.
Another major issue is data privacy, as this technology fully relies on sensitive personal health information; therefore, it should be securely protected.
Enhancing the accuracy of wearable devices and utilising high-quality data facilitates improved Type 2 diabetes management.