Delayed MS treatment leads to faster disease progression.
Treatment plans seek to minimize tissue damage and brain atrophy caused by MS.
Excerpted from: G. Giovannoni, et al 2015 Brain health: Time Matters in Multiple Sclerosis
One Test. One Answer.
A definitive MS diagnosis using conventional methods typically requires multiple clinic visits, multiple tests and often takes months to years to confirm.
IsolateMS™ is a breakthrough RNA test that can rule in or rule out a suspected multiple sclerosis (MS) diagnosis in as little as seven days.
IsolateMS gives healthcare providers a new tool to confirm suspected MS diagnoses much sooner than traditional testing methods. By confirming the suspected autoimmune disease faster, doctors can initiate treatment of MS patients earlier in the disease progression. Earlier treatment can help slow the illness and allow better management of its long-term physical, emotional and financial effects.
NOTE: Initially, the IsolateMS test will not be available in the following states: California, Florida, Maryland, Pennsylvania, Rhode Island and New York. IQuity will update this list as these licenses are obtained.
The need for early diagnosis is clearly stated in a position paper produced in 2015 - “Brain Health: Time Matters" which is endorsed by major organizations and foundations that advocate for MS research. In the executive summary of the report, the authors state:
“a therapeutic strategy that offers the best chance of preserving brain and spinal cord tissue early in the disease course needs to be widely accepted – and urgently adopted; significant delays often occur before a person with symptoms suggestive of MS sees a neurologist for diagnosis and treatment; and early intervention is vital.”
At IQuity, our research has focused on defining the pathway to early diagnosis. Our patient profile sample population for IsolateMS consisted of approximately 1,000 subjects, including:
- patients with MS
- patients with other neurologic disorders
- healthy patients
- unaffected family members of patients with MS
Using machine learning techniques to analyze the patient sample library, we were able to identify distinct gene expression patterns consistent with MS patients versus healthy patients or those with other neurological diseases. Standard calculations of sensitivity and specificity were employed to determine the accuracy of the results, which are greater than 90%.
How to Order a Test
IsolateMS is Now Available.
All of IQuity's tests must be ordered by a provider.
- Provider registers with IQuity for access to the ordering portal.
- Patient visits provider who orders the test.
- Patient's blood sample is taken and sent to IQuity CLIA-certified laboratory.
- IQuity lab processes the sample, analyzes the data and returns the result to the provider.
If you are interested in additional information, contact IQuity at iquity.com or call 855.899.9551.
To stay up-to-date on our progress, join our community.
Increasing the Certainty of an MS Diagnosis is of Value
“Multiple Sclerosis (MS) remains a clinical diagnosis, and the certainty of the diagnosis is enhanced by imaging and electrodiagnostic studies. Any test which will increase the certainty of an MS diagnosis is of value to the treating physician. The results are likely to influence both making the diagnosis of MS and in implementing therapies that benefit the patients.”
Subramaniam Sriram, MD,
Director, MS Center,
Vanderbilt University Medical Center
Identification of Molecular Biomarkers for Multiple Sclerosis
Quantitative real-time polymerase chain reaction analysis was used to identify a minimum number of genes of which transcript levels discriminated multiple sclerosis patients from patients with other chronic diseases and from controls.
Gene-expression signatures: biomarkers toward diagnosing multiple sclerosis
Results indicate that gene-expression differences in blood accurately exclude or include a diagnosis of MS and suggest that these approaches may provide clinically useful prediction of MS.
Using biomarkers to predict progression from clinically isolated syndrome to multiple sclerosis
Using machine learning techniques, including support vector machines, this study demonstrates that gene expression signatures can accurately identify subjects with a clinically isolated syndrome who later progress to multiple sclerosis.