Innovative Diagnostic Techniques for Detecting Autoimmune Diseases
Autoimmune disease is a complex group of diseases, where symptoms can vary, wax and wane, and overlap with those of other conditions. This often means the time to diagnosis can take several months to years. Current tests available for autoimmune disease testing include antinuclear antibody (ANA) testing, C-reactive protein tests, ELISAs, and erythrocyte sedimentation rate tests. More information about these tests can be found here.
Why is Autoimmune Disease Challenging to Diagnose?
David Pisetsky, Professor of Medicine at Duke University School of Medicine, cites three common challenges when it comes to diagnosing autoimmune diseases: non-specific symptoms, uncertainty in screening tests, and a clinical situation known as pre-clinical immunity.
“Patients can sense there’s something wrong and go to physicians. They don’t have localized signs and symptoms, and there’s uncertainty. It’s not unusual that people can go for a while before they get a diagnosis,” says Pisetsky. Even within the same autoimmune disease, there can be a lot of variation in symptoms.
While there are blood tests to detect antibodies associated with autoimmune diseases, some of these screening tests, such as the ANA test, can be what Pisetsky describes as “problematic” since up to 20% of healthy individuals will have a positive result.
“It becomes very unclear whether the patient has the disease, whether the patient may get the disease, or whether this is just an unknown phenomenon of uncertain significance,” he says.
“It’s both a laboratory issue and a health system issue of how do you diagnose diseases earlier, especially when the symptomatology is more vague,” adds Pisetsky.
How are New Biomarkers Discovered and Validated?
There are many types of biomarkers, or measurable biological indicators, of disease. Biomarkers such as blood glucose level or high blood pressure are easily measured and can help pinpoint a disease diagnosis. But not all biomarkers are straightforward, and often doctors need to evaluate several biomarkers to make a confident diagnosis.
“For autoimmune cerebral ataxia, there are upwards of 30 biomarkers at this point,”
says Andrew McKeon, a Professor of Neurology and Professor of Laboratory Medicine at Mayo Clinic who works on identifying antibody biomarkers associated with autoimmune neurological diseases.
Genomic and proteomic techniques can help pinpoint specific changes in genetic sequences or protein levels associated with disease. McKeon notes that with the increasing number of biomarkers discovered for particular diseases, it’s possible to offer more sensitive testing. “Because the field is evolving so rapidly and there’s so many biomarkers, we’re really in a space that’s a little bit more like the field of diagnostic genetics where there’s a profile or a panel of targets that’s been designed by a laboratorian and offered as a clinical test,” he says.
When a biomarker is discovered, its route towards clinical utility involves several steps. McKeon describes the process. “Once we’ve screened and detected an antibody of interest, we do three or four additional confirmatory techniques to show [that the antibody is truly a biomarker],” he says. “But in order for it to enter clinical practice, that’s a whole different ball game.” To be used clinically, a potential biomarker must be evaluated alongside the type of test used to detect it. The first step here involves determining which is the best test to use to detect the biomarker in the clinic. Once that’s established, more formal validation studies can include testing a large number of patients and healthy individuals without disease to make sure that the test is accurate, precise, sensitive, and selective. Lastly, the test must be approved by a regulatory agency.
What Imaging Techniques are Used to Diagnose Autoimmune Diseases?
For autoimmune diseases, imaging techniques are particularly helpful to detect early signs of disease before symptoms show up.
The types of imaging used for autoimmune disease diagnostics includes:
- Magnetic resonance imaging (MRI) uses magnetic fields and radio waves to visualize organs, tissues, and blood vessels. It can help detect early inflammation. Innovations in nanotechnology-based contrast agents (vs. traditional gadolinium-based contrast agents) can help overcome the adverse effects and low specificity of traditional gadolinium-based contrast agents.
- Positron Emission Tomography (PET) uses radioactive tracers to visualize metabolic activity in tissues and can detect tissue inflammation and immune cell activity. For example, a recent study used total body PET to assess the distribution of T cells in the body by using radiolabeled antibodies that specifically bind T cells (1). While this study was done for COVID-19, this application can be applied to autoimmune diseases as well.
- Computed tomography (CT) uses x-rays to image cross-sections of the body. It’s helpful for understanding the structural changes in tissues and organs and can help guide biopsy procedures.
- Ultrasound uses high frequency sound waves to examine joint inflammation and blood flow in inflamed tissues. Acoustically active microbubble contrast agents can give better ultrasound imaging and better quantify inflammation (2).
- Single-photon emission computed tomography (SPECT) creates 3D images and uses radiolabeled peptides, proteins, or antibodies to track specific biomarkers of disease at sites of inflammation (3).
How can Wearable Devices Monitor Autoimmune Conditions?
Wearable devices that track our physical activity, heart rate, and sleep can have great impacts in monitoring the current state and progression of autoimmune diseases. Since autoimmune disease symptoms can ebb and flow, wearables can help a patient understand what is considered normal for them. “Wearable devices can give an indication of whether you need to book an appointment because something is not going right,” says Linda Li, a senior scientist at Arthritis Research Canada and Professor at the University of British Columbia. She adds that augmenting data from wearable devices with symptom tracking makes it even more powerful.
One pilot study found that patterns in physical activity could predict rheumatoid arthritis flares over 95% of the time (4).
While wearables can be great tools, it’s also important to use them in moderation. “There is a balance between being obsessed about tracking versus tracking information sufficiently, so that you can get useful information and see the trends of your health,” says Li. “Behavioral scientists are investigating what is the optimum engagement with these wearable devices.”
Aside from the fitness and activity trackers that are popular on the market, several biotech companies are developing new ways to monitor autoimmune disease, including monitoring sleep disturbances and wrist movements associated with scratching in atopic dermatitis (5), and measuring involuntary body motions to assess balance and neurophysiological impairment associated with multiple sclerosis (6).
Can Artificial Intelligence Help With Diagnosing Autoimmune Diseases?
With the ability to produce increasingly large amounts of data, scientists are turning to artificial intelligence to help transform this vast amount of data into actionable results. “If we’re able to collect more data over time and make sense out of it, we may actually be able to set more precise parameters for people tracking their symptoms and disease activity to determine whether it’s a flare or not,” says Li.
By identifying patterns in data, AI can help scientists and clinicians:
- Predict autoimmune disease risk based on genetic and epigenetic data (7)
- Diagnose autoimmune disease with AI-assisted ultrasound (8)
- Identify patients who would benefit from autoimmune testing based on their electronic medical records (9)
“These big data approaches offer the opportunity to better characterize the immune profile of people with autoimmune disease, so that these approaches have a lot of promise. The challenge is reducing the information in these tests into a format that’s more easily utilizable and easily interpreted,” says Pisetsky.
About the Author
A microbiologist turned freelance science writer who works with life science companies, nonprofits, and academic institutions on anything from news stories, explainer articles, and content marketing. She shares the wonderful world of microbes on her blog The Microbial Menagerie.
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