The promise of AI in precision medicine and immunotherapy for diabetes
Robust datasets and computer algorithms are merged in the expanding field of artificial intelligence to tackle problems that humans typically solve. Healthcare is one area in which AI has taken hold.
Today is World Diabetes Day. To mark this observance, this article covers the many ways in which diabetes research and drug development stands to benefit from the use of AI.
About the condition
According to the World Health Organization, about 422 million people have diabetes, and that number increases each year.
Diabetes is a consequence of insulin deficiency, resulting in uncontrolled blood glucose. Symptoms include increased thirst, frequent urination, fatigue, blurred vision and slow healing wounds. (For more about the condition, see box below.)
Precision medicine is an approach that aims to optimize the prevention, diagnosis, prediction and treatment of conditions by integrating big data and accounting for individual differences based on unique genetic, environmental and lifestyle factors. Precision medicine promises to reduce healthcare costs and improve outcomes.
AI plays a pivotal role in enabling precision medicine for diabetes in the following ways:
Genomic analysis: Scientists can use AI-driven algorithms to analyze vast genomic data sets, identifying genetic markers and mutations associated with diabetes subtypes.
Early predictive diagnosis: AI models can predict an individual’s risk of developing diabetes by analyzing their unique genetics, family histories and lifestyles. These predictions enable early diagnosis, intervention and preventive measures.
Personalized treatment options: AI can be used to analyze data collected from individuals — such as glucose levels, response to treatment and lifestyle changes — to develop personalized treatment options tailored to each individual's unique needs.
Immunotherapy exploits the immune system to fight diseases. It has been extensively researched for diabetes treatment. AI has the potential to accelerate research, discovery and development of immunotherapy strategies for diabetes in the following ways:
Drug discovery: AI-powered algorithms can search through existing large databases of chemical compounds to identify potential immunotherapeutic. Also, it can be used to improve clinical trial design and optimization through predictions of drug dosages and efficacy.
- Personalized immunotherapies: Large amounts of data are generated from flow cytometry and other immunophenotyping techniques in diabetes research. AI can analyze big immunophenotypic profiles in individuals to identify biomarkers and other factors, allowing for more rapid development of personalized and effective treatments.
To exploit AI in diabetes research, scientists need access to large-scale datasets, including patient records, genetic information, experimental data and clinical trial results. These data would provide AI algorithms with robust information to identify patterns, correlations and treatment opportunities that might not be apparent to human researchers. If we had to examine these datasets with human capacity, it would take an enormous amount of effort and time. As noted by Guan et al. 2023 "The application of AI in diabetes care and research offers substantial benefits for basic biomedical research, translational sciences, and clinical practice.
It is important to mention that the integration of AI into diabetes research does have challenges, such as concerns with handling sensitive patient data, the need for robust regulatory frameworks and the potential for algorithmic bias.
Regardless, artificial intelligence has the potential to transform the lives of millions of individuals living with diabetes. As researchers continue to embrace AI in diabetes research and we observe World Diabetes Day, we can dream of a future when this condition is managed and treated with unprecedented precision and efficacy, offering renewed hope for patients and their families.
More about diabetes
There are three main types of the condition.
- Type 1 is an autoimmune disease in which beta cells are dysfunctional. The body does not make insulin or makes too little insulin, resulting in hyperglycemia. It is currently managed through the exogenous administration of insulin.
- In Type 2, the beta cells are functional, but the pancreas is not making enough insulin, or the body is not using insulin properly, resulting in insulin resistance. It can be managed through lifestyle changes and medications.
- Gestational diabetes develops during pregnancy and usually resolves after the baby is born, however it increases the risk of developing Type 2 diabetes later in life.
People living with diabetes need insulin. A major challenge is the lack of availability of insulin in low-income countries. In parts of the world in which it is available, it can be very expensive.
The matter of cost has become an ongoing conversation on X/Twitter using hashtag #insulin4all. One user recently posted: “Trying to afford my insulin before meeting my deductible is, hands down, the most stressful thing I’ve ever experienced in my life. I don’t want to do this forever.”
In addition, there is variability in clinical presentation, progression and response to insulin and other medications among individuals with diabetes.
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