Objective
Autoimmune diseases—such as multiple sclerosis (MS), rheumatoid arthritis (RA), type 1 diabetes (T1D), and systemic lupus erythematosus (SLE)—appear very different on the surface. They affect different organs and cause different symptoms, yet they all arise from the same fundamental issue: the immune system mistakenly attacks the body’s own healthy cells. Increasing evidence suggests that these diseases also share common underlying risk factors. This study aims to better understand those shared mechanisms and use that knowledge to improve prevention, diagnosis, and treatment.
The first objective is to identify new potential treatment targets for these autoimmune diseases by integrating genetic information with other molecular data (such as proteins levels found in the blood). Since genetic studies have already identified many regions of the genome that increase the risk for RA, SLE, and T1D, we will use advanced statistical methods to link these genetic markers to the proteins they influence. We can then determine which proteins are likely to play a direct role in causing disease. Importantly, if these disease related proteins happen to be targets of existing medications, those medications could be repurposed—meaning they could be used to treat another autoimmune disease more quickly than developing a brand new drug.
The second objective is to build powerful prediction models that can identify people at high risk of developing MS, RA, SLE, or T1D long before symptoms begin. By analyzing national healthcare data—such as medical diagnoses, prescription histories, and family connections—we aim to create computer models that learn patterns in people’s health journeys. These models may reveal, for example, whether certain combinations of early life infections, family history, or other conditions can predict the later onset of an autoimmune disease. Adding genetic risk information and lifestyle factors (like smoking or obesity) should make these predictions even stronger.
The third objective is to take a deeper look at the genetic architecture of these diseases. This means identifying exactly which genes and DNA regions are involved in increasing disease risk, separating the influence of the well known HLA region from the rest of the genome, and determining which genetic risk factors are shared across diseases and which are unique. The study also aims to understand why certain genetic factors contribute to different disease patterns in different people. This includes exploring why some individuals develop severe disease, while others develop milder forms, or why some autoimmune diseases are far more common in women than in men.
Ultimately, the objectives of this study work together toward a unified goal: to illuminate the shared and disease specific biological mechanisms behind autoimmunity, identify new treatment opportunities, and develop tools that help detect disease earlier—offering hope for earlier interventions and better patient outcomes.
Background Rationale
Autoimmune diseases occur when the immune system, which usually protects the body from infections, becomes confused and starts attacking healthy cells. This inappropriate reaction can affect nearly any part of the body, leading to widely varying health problems. Some autoimmune diseases, like RA, cause joint inflammation, while others, like MS, damage the brain and nerves. Conditions such as SLE can affect many organs at once, and T1D results from the destruction of insulin producing cells in the pancreas.
Because these diseases look so different, it might seem like they have little in common. But research tells a different story: many autoimmune diseases tend to run in families. A person with a parent who has one autoimmune condition may have a higher risk of developing a different one. This suggests that underlying biological mechanisms—particularly genetic ones—are shared across diseases.
Twin studies support this idea. Identical twins, who share all their genes, have a much higher chance of both developing the same autoimmune disease compared to fraternal twins, who share only about half their genetic makeup. Thousands of genetic markers have been linked to autoimmune diseases, and many of these markers cluster in the same regions of the genome across multiple autoimmune manifestations. One region, known as the HLA region, stands out as the strongest contributor to autoimmunity.
Intriguingly, the same genetic variant can increase the risk for one disease while protecting against another. This shows how complex and finely balanced the immune system is. Beyond genetics, autoimmune diseases also share other patterns: many are more common in women, some are influenced by geography or shared environmental exposure, and many begin years before a diagnosis is made. During this “silent” phase, changes occur in the immune system long before symptoms appear, providing a possible window for early detection and intervention.
In recent years, scientists have begun using large scale biological datasets—genomics, proteomics, and other molecular data—to discover why some people develop autoimmune disease and others do not. These datasets can reveal which immune pathways go awry and which proteins may be driving disease. At the same time, advances in artificial intelligence allow researchers to build sophisticated predictive models that spot early warning signs in medical records.
Sweden provides a unique opportunity for this work. The country has nationwide healthcare registries that track medical history, prescriptions, family relationships, and more for nearly the entire population. Genetic and biological samples are also available for many individuals with autoimmune disease. When combined, these resources create one of the most powerful platforms in the world for understanding the causes of autoimmune disorders.
This study builds on all these strengths—genetics, health registries, and advanced computational methods— to uncover what autoimmune diseases share, what sets them apart, and how these insights can be used to improve patient care. This study also brings together unique expertise by scientists who have been working focused on one autoimmune disease at a time now sharing their knowhow in a joint project.
Description of Project
Autoimmune diseases occur when the body’s immune system mistakenly attacks healthy cells. Although conditions like multiple sclerosis (MS), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and type 1 diabetes (T1D) affect very different parts of the body, they share a common underlying problem: a breakdown in the immune system’s ability to distinguish “self” from “non self.” Because many autoimmune diseases run in families and share similar genetic risk factors, researchers believe they may arise from overlapping biological mechanisms. Understanding what these diseases have in common may reveal powerful new ways to prevent, diagnose, and treat them.
This study has three main objectives.
The first is to identify new treatment opportunities by combining genetic findings with other biological data (such as proteins in the blood). Large genetic studies have already identified many DNA regions linked to RA, SLE, and T1D. By examining how these genetic markers influence protein levels, we can pinpoint proteins that may play a direct role in causing disease. Some of these proteins might already be targeted by existing drugs, creating opportunities to repurpose medications originally designed for other conditions. This approach has already shown success in MS, where several promising targets and drug candidates were identified.
The second objective is to develop computer based prediction models that can identify who is at risk of developing autoimmune diseases long before symptoms appear. Many autoimmune conditions have a long “silent” phase, where the immune system is beginning to malfunction but obvious symptoms have not yet developed. Sweden’s nationwide health registries offer a unique resource for this kind of research. These registries contain decades of medical information, prescription records, family links, and demographic data for nearly the entire population. Using advanced machine learning methods, the research team aims to build prediction tools capable of recognizing early warning patterns. These models can then be enhanced using genetic information and lifestyle data to improve accuracy and help identify individuals who may benefit from early monitoring or preventive interventions.
The third objective is to better understand which genetic factors are shared across autoimmune diseases and which are unique to specific conditions. Many genetic risk markers—especially in the HLA region—affect multiple autoimmune diseases, while others influence only one. Even shared genetic variants can have opposite effects depending on the disease. By separating HLA based risk from other genetic influences and studying them across several autoimmune diseases, we aim to map out the biological pathways that underlie both common and distinct disease features. This will also help identify subgroups of patients with similar genetic profiles, which could explain why symptoms and treatment responses vary so much from person to person.
The anticipated outcomes of this study are wide reaching.
We expect to identify new biological targets for autoimmune treatments, including existing medications that can be repurposed. We also aim to develop early warning tools that can predict disease risk with high accuracy, giving clinicians the ability to monitor or intervene earlier. Finally, the study will generate detailed genetic maps and identify patient subgroups, paving the way for more personalized and effective treatment strategies.
Overall, this project aims to improve the understanding of autoimmune diseases, accelerate treatment discovery, and support earlier diagnosis—ultimately helping reduce long term disability and improving quality of life for those affected.
Anticipated Outcome
If successful, this study will transform how autoimmune diseases are understood, diagnosed, and treated. One major expected outcome is the discovery of new biological targets that contribute to RA, SLE, T1D, and related autoimmune conditions. These targets—often proteins that play key roles in immune function—could become the foundation for future drug development. Even more promising, some of these proteins may already be targeted by existing medications used for other diseases. If so, these drugs could potentially be quickly repurposed, offering new treatment options without the long delays and costs of developing new therapies from scratch.
Another anticipated outcome is the creation of powerful predictive models capable of identifying individuals at risk for autoimmune diseases years before symptoms appear. These models may integrate medical history, genetic data, lifestyle information, and family background to reveal patterns that are otherwise invisible. For example, the model might identify that certain childhood illnesses combined with a particular genetic profile lead to a higher chance of developing a certain autoimmune disease later in life. Such early warnings could help healthcare providers monitor at risk individuals more closely and potentially intervene early, reducing long term disability and improving quality of life.
The study will also compile high resolution genetic risk maps for each autoimmune disease. These maps will show which parts of the genome carry the greatest risk, how these risks differ between diseases, and which genetic features are shared across conditions. By separating the influence of the HLA region from the rest of the genome, we can better understand which risks are tied to immune recognition and which involve other biological pathways. Combining this information across diseases will highlight the common mechanisms that drive autoimmunity as well as the unique features that distinguish for example MS from SLE, or RA from T1D.
Another important outcome will be the identification of genetically defined subgroups of patients. Not all people with the same autoimmune disease have the same symptoms or disease course. Some progress quickly, others slowly; some respond well to treatment, others do not. By clustering patients based on their genetic risk patterns, the study may uncover groups with similar biological pathways driving their disease. This knowledge can guide more precise and personalized treatment strategies in the future.
Additionally, the research may shed light on why certain autoimmune diseases are more common in women, why some diseases frequently occur together, and why others do not. Insights into these questions could lead to new hypotheses about hormonal influences, environmental triggers, and immune system development.
Finally, the work is expected to produce resources that other researchers and clinicians can use, such as ranked lists of genetic variants linked to disease risk, shared biological pathways across conditions, and sex specific models of disease mechanisms. Together, these outcomes will advance the understanding of autoimmune diseases, support earlier detection, and pave the way for better, more targeted therapies—ultimately aiming to reduce suffering and improve the lives of people affected by these complex conditions
In a longer term perspective identification of novel, potentially shared drug targets between autoimmune diseases will open up for further developing of new drugs and testing of the effectiveness of already existing drugs that show potential for repurposing for autoimmune diseases. Common targets between autoimmune diseases will be prioritized.
Predictive models individuals at risk of autoimmune disease will need to be validated in other populations. If successful identification of preventative treatments/change of lifestyle will need to be tested for efficiency.
Relevance to T1D
Why This Research Matters for Autoimmune Diseases
Autoimmune diseases affect millions of people worldwide, often striking in early or mid‑adulthood and causing lifelong health challenges. What makes autoimmune diseases especially complex is that they come in many forms. MS affects the brain and nerves, RA affects the joints, T1D destroys insulin‑producing cells, and SLE can damage almost any organ. Despite this diversity, research increasingly shows that these diseases are more connected than they may appear.
This study is highly relevant because it aims to understand what autoimmune diseases have in common, what makes each one unique, and how that knowledge can be used to improve people’s lives.
Understanding Shared Causes
Even though autoimmune diseases target different organs, many shares common roots. Family studies have shown that having a parent with one autoimmune disease can increase the risk of developing a different one. This suggests that shared genes or shared environmental triggers is involved.
Genetics provides strong clues. Thousands of genetic variations are known to increase the risk of various autoimmune diseases. Some of the most influential lie within a part of our DNA called the HLA region, which plays a central role in how the immune system recognizes threats. Interestingly, the same genetic variant can raise the risk of one autoimmune disease while lowering the risk of another, underscoring how interconnected—but also how delicate—the immune system’s balance is.
This research aims to clarify these connections, helping uncover why certain people develop one autoimmune disease, others develop a different one, and some develop several.
Earlier Diagnosis and Better Prediction
Most autoimmune diseases don’t appear suddenly, they develop slowly over years, moving through a “silent” phase where the immune system is misbehaving long before symptoms emerge. This creates an important opportunity for identifying people at risk early, they might be monitored more closely, be recommended lifestyle changes, or even intervention before irreversible damage occurs.
Using Sweden’s unique population‑wide health data, researchers can track medical history, family links, medication use, and more across entire lifetimes. The team has already built a model that can predict MS years before symptoms, and now they aim to build similar early‑warning systems for RA, SLE, and T1D. These predictive tools could revolutionize how autoimmune diseases are diagnosed and managed.
Improving Treatments and Discovering New Ones
Another key reason this research is so relevant is its focus on identifying possible new treatments—and even repurposing drugs that already exist. By connecting genetic risk factors to specific proteins in the body, we can identify which biological pathways are likely driving disease. If a protein involved in these is already a drug that targets it, it could potentially be used to treat another autoimmune condition. This speeds up treatment development dramatically compared to creating new drugs from scratch.
Moving Toward Personalized Medicine
Autoimmune diseases affect people very differently. Some have mild symptoms, while others face severe or rapidly progressing illness. Some respond well to treatment, others do not. We study aims to identify subgroups of patients with similar disease pathways. This knowledge can lay the groundwork for more personalized, targeted treatment options in the future—moving away from the current “one size fits all” approach.
In short, this research is deeply relevant because it tackles autoimmune diseases from every angle—genetics, environment, prediction, and treatment. It seeks to reveal why these diseases happen, how they relate to each other, and how we can diagnose and treat them earlier and more effectively. The ultimate goal is simple but transformative: to reduce suffering and improve the lives of people affected by autoimmune diseases.