Decrypting Security: How Machine Learning is Boosting Allergy Research and Treatment

 

By Mariana Meneses

The rise of allergies in industrialized countries is a phenomenon that has been observed for decades.

While the exact cause of this trend remains unclear, various factors have been proposed. These include changes in lifestyle and diet, increased exposure to environmental pollutants, and changes in the microbiome which is the genetic material comprising the community of microorganisms in our gut.

Additionally, the hygiene hypothesis suggests that a lack of early childhood exposure to microbes and infectious agents may also play a role in the development of allergies. As allergies continue to affect a growing number of individuals, it is crucial to continue research and exploration into the underlying causes to develop effective prevention and treatment strategies.

 

Why Evolution Hasn’t Gotten Rid of Allergies?

 

When the immune system encounters an allergen, it produces a type of antibody called Immunoglobulin E (IgE), which then binds to mast cells, which are immune cells that are present in the skin, lungs, and lining of the intestine.

When IgE antibodies recognize the allergen again, they trigger the mast cells to release histamine and other chemicals, leading to an allergic reaction. This response can cause a wide range of symptoms, including itching, sneezing, runny nose, hives, swelling, difficulty breathing, and even anaphylaxis, which is a severe and potentially life-threatening allergic reaction. 

 

 

While the exact cause of allergies is still not fully understood, some researchers have advanced the theory that the evolution of the IgE antibody was originally intended to protect the body against parasites.

Parasites can invade and harm the body, and so the immune system responds by producing IgE antibodies that trigger a response to eliminate the invaders. However, in modern times, the immune system may sometimes mistake relatively harmless substances, such as pollen or food proteins, as dangerous invaders, leading to an allergic reaction. This theory is still being explored by scientists, and more research is needed to fully understand the complex mechanisms behind allergies.

Allergenic proteins show similarity to parasite proteins, suggesting that allergies may be a side effect of the body’s mechanisms to defend against parasites.

This theory is supported by the observation that some allergenic proteins share similar structures with proteins found in parasites. For example, a protein found in dust mites has been found to have similarities to a protein produced by a type of worm that infects humans. It’s possible that our immune system has evolved to recognize and attack these parasitic proteins, but in some people this immune response also mistakenly targets other substances like pollen or food proteins, which leads to allergic reactions. 

Subsequent exposures to allergens are usually worse than the first, but subsequent exposures to viruses or bacteria are usually less dangerous as the body builds up a defense against them.

When the body is exposed to an allergen for the first time, it may not have a significant immune response, but it does produce IgE antibodies that are specific to that allergen.

In contrast, when the body is exposed to a virus or bacteria for the first time, the immune system mounts a response that includes the production of antibodies and memory cells that provide protection against future infections. This process is known as acquired immunity, and subsequent exposures to the same pathogen usually result in a less severe response.

Although there are still many unanswered questions in the field of allergology, machine-learning and artificial intelligence are now boosting scientific research and may soon lead to important breakthroughs.

For instance, a 2023 paper published in the journal Pharma Nutrition by Kim Kamphorst, from the University of Amsterdam, and co-authors, conducted a small pilot study in Europe to find predictors for allergies in children using machine learning algorithms. To analyze the data, the researchers employed a technique called recursive ensemble feature selection (REFS) and used eight different classifiers. As a result, the study identified 20 features that are predictive for an allergy.

The authors concluded that a combination of environmental exposures, like pollution or allergens in the air, and cytokines, which are proteins produced by the immune system, could predict whether a child will develop allergies in the future.

 

 

 A 2022 paper by professor Ana Ktona, from Tirana University, in Albania, and co-authors investigated how food allergies negatively affect quality of life and can result in over-diagnosis and unnecessary avoidance of certain foods.

The research examined the potential of using machine learning algorithms to discover relationships between features of food allergy data, with the goal of supporting patients in identifying their food allergens and avoiding unnecessary diagnostic testing, and also supporting patients by not eliminating foods that do not harm them, reducing the risk of nutritional deficits.

According to a 2022 paper published in the journal Pediatric Allergy and Immunology by Giovanna Cilluffov, from the Institute for Biomedical Research and Innovation in Italy, and co-authors, unsupervised machine learning (ML) techniques, such as clustering methods, can help identify distinct allergy subgroups within a population based on shared characteristics.

Additionally, supervised ML techniques, like regression or classification methods, can then be used to build predictive models based on these identified subgroups. These techniques can be particularly useful in healthcare, where identifying patient subgroups with similar disease characteristics can help personalize treatment plans and improve outcomes. 

According to the paper, ML techniques have been used to uncover phenotypes, which are traits of an organism which are influenced by both its genes and environment, of pediatric asthma.

Asthma is a chronic respiratory disease that affects millions of children worldwide.

By analyzing large datasets of clinical and biological data, researchers have been able to identify distinct subgroups of pediatric asthma patients with different disease characteristics and treatment needs. This approach has the potential to improve the accuracy of asthma diagnosis and treatment, as well as to better understand the underlying mechanisms of the disease.

ML techniques also have the potential to improve the accuracy of prediction models for asthmatic triggers.

By integrating multiple data sources, including patient demographics, clinical data, and environmental factors, ML algorithms can develop more accurate models for predicting asthma events. This could help healthcare providers to proactively intervene to prevent events, improving patient outcomes and reducing healthcare costs.

 

 

In her 2022 Masters dissertation at the University of Toledo, Leah Stevenson argued that the current methods for diagnosing food allergies, including skin prick tests and food challenge tests, have limitations that can cause physical and emotional discomfort to patients.

For instance, these tests purposefully induce an allergic reaction, which can be stressful for the individual undergoing the test. Furthermore, these methods are not always accurate and may produce false positive or negative results, leading to unnecessary dietary restrictions or inadequate treatment. 

Since gut microbiota, the community of tiny microorganisms that live in our gut, are increasingly associated with allergies, Stevenson hypothesized that utilizing machine learning to assess gut microbiota composition would provide a new diagnostic tool for food allergies that avoid patient distress. Although the research found that the level of accuracy obtained was below the required threshold due to the small sample size, this study highlights the potential of using machine learning to diagnose food allergies and warrants further exploration.

A 2020 review of how AI has been used in the field of allergy-immunology, by Nicholas L Rider and co-authors, had already shown that artificial intelligence and machine learning are helping researchers in various fields of science and medicine, such as genetics and clinical medicine.

They are being used to reduce complexity, identify patterns, and aid in disease and allergy detection, risk profiling, and decision-making. Although the methods are relatively new to the field of clinical immunology, there is much potential for further advancement. The authors argued that AI has great potential in healthcare, but there are challenges that need to be addressed. One of them is that the hype around AI exceeds the science, and technology cannot replace a well-trained clinician.

Algorithms that produce unexplained results are often considered a “black box,” which raises suspicion and mistrust.

There are also concerns about implicit bias, which may further worsen healthcare inequities, and therefore algorithms must be trained on diverse populations to minimize performance gaps. The field of AI science needs to overcome these challenges and others to fully realize the potential of AI in allergy-immunology and other areas of healthcare.

Recently, a framework for Augmented Intelligence in Allergy and Immunology practice and research was proposed by Paneez Khoury, senior clinician and director of the Allergy & Immunology Fellowship Training Program at the National Institute of Allergy and Infectious Diseases (NIAID), and her team.

For them, the use of AI and machine-learning is increasing in healthcare, and there are many potential applications for AI in allergy and immunology, from diagnosis to data reduction. However, challenges include incorporating data science and bioinformatics into training, as well as addressing issues of ethics, equity, and governance. They note that it is important for specialists to be involved in the design and implementation of AI in this field.

Although allergies have been identified and studied for over a hundred years, machine learning is opening new possibilities for accurate identification and prediction of allergies with improved personalized treatment options in the face of increasing prevalence. 

With the ability to analyze large amounts of data from electronic health records, genetic sequencing, and environmental factors, machine learning algorithms can identify patterns and risk factors that may contribute to the development of allergies. This can lead to more targeted prevention strategies, earlier interventions, and ultimately, better outcomes for patients. Additionally, machine learning can help identify new potential allergens and facilitate the development of new treatments to alleviate symptoms and improve quality of life for those with allergies.

 Craving intellectual stimulation? Don’t miss these captivating TQR articles:

• What If We Could Cure Cancer by Telling Cancer Cells to Get Better?
• Reversing the Ageing Process? New Discovery Points to The Body’s Relationship With Time
• New Understanding of How Cells Communicate May Supercharge Medical Advances
• Discovering the Human Code: the Most Complete Human Genome Ever Created
• CRISPR Technology: Editing the Genetic Code, From Plants to Humans