Diseases and Disorders

Precision Medicine in the Diagnosis, Care, and Prognosis of Multiple Sclerosis

Helen Kim


Multiple sclerosis (MS) is an immune-driven demyelination of neurons leading to neurological defects. It is currently one of the leading causes of neurological disability in young adults. With the advent of genotyping and advanced technology, precision medicine has risen to the forefront of MS research. Due to the heterogeneity of clinical expression, disease progression, and response to drugs displayed by patients diagnosed with MS, precision medicine could be the key to formulating and tailoring treatment that can not only provide symptomatic relief but also reverse MS. Precision medicine structures healthcare around individual patients’ unique needs, taking into account their varying genes, lifestyles, and disease progression. This review paper will explore current and potential diagnostic measures for MS, weigh the methods in which the likely outcome and progression of MS can be predicted, and analyze how MS patients can be monitored. This paper will also provide examples of the current trends and limitations of using precision medicine for MS and conclude that a universalized system for MS data collection and analysis is imperative to revolutionize clinical MS care.



Multiple sclerosis (MS) is a chronic, inflammatory disease of the brain and spinal cord where the immune system attacks the myelin covering nerve fibers [1]. While most patients present with paresthesias, optic neuritis, diplopia, ataxia, vertigo, and muscle weaknesses due to axonal damage [2], MS patients exhibit highly heterogeneous symptoms [3]. While the cause is unknown, the onset of MS is suspected to be due to a combination of factors, such as an abnormal immune response, environmental factors like low vitamin D and smoking, previous infection of the Epstein-Barr virus, and genetic factors [1].

Affecting approximately 400,000 people in the United States and 2.1 million people worldwide, MS is the most common disabling neurological disease of people in their 20s and 30s [4]. Twice as many women are affected as men [4]. According to the McDonald criteria, the key requirement for diagnosing an individual with MS is the presence of neurological damage that is disseminated in time and space [5].

Currently, there is a lack of therapies that can cure or effectively modify the disease. In other words, the lack of disease-modifying treatments indicates that an individual with MS cannot eliminate their illness. As a result, symptomatic treatments are provided to target the early inflammatory process, prevent neurodegeneration, and potentially improve the patient’s quality of life by providing symptomatic relief to acute episodes [2]. Furthermore, because symptoms of MS present during early adulthood, patients continue to live with this long-term disease for decades [6]. Therefore, research must discover more therapies and potential cures to relieve debilitation for the increasing number of people with MS and ease the financial burden on the healthcare system. With the advent of genotyping, precision medicine could be the key to finding disease-modifying therapies that are individualized for patients living with widely heterogeneous forms and patterns of MS. 


Precision Medicine

According to the Precision Medicine Initiative, precision medicine is an emerging strategy for disease treatment and prevention. Precision Medicine enables tailored strategies that account for an individual’s lifestyle, genome sequence, health history, microbiome composition, and other unique characteristics [7]. From treating cancer patients to fighting rare inherited diseases, precision medicine is increasingly at the forefront of medicine. 

In MS, where clinical expression and treatment response is highly variable from case to case, focusing on individual patients is not just effective, but essential. Precision medicine for MS starts with an accurate diagnosis so that prognosis, treatment, and monitoring can follow an evidence-based framework. A hybrid of clinical and biological data is used to construct the framework.

Precision medicine for MS would not have been relevant twenty years ago when treatment options were very limited. However, increasing treatment options and a more informed patient population calls for personalized care. Additionally, personalized medicine aids in finding the optimal balance between effective management and has minimized the risk of adverse effects [8].


Current Problem

Predicting the likely outcome of MS, or providing a prognosis, is currently lacking. This is not ideal because all treatment comes with a cost, whether that be the literal financial cost, decreased quality of life, or side effects as a result of nonspecific and excessive treatment. The health care team can only provide the best treatment plan when all information about the patient’s genetics and medical information are known.

Additionally, despite advancements in research, there is no current curative treatment for progressive MS. Exploration is active but unsuccessful in identifying new specific biomarkers for MS that could reveal potential drug and diagnostic markers [3]. The purpose of this review paper is to summarize the trends and limitations of current research on the use of precision medicine in MS and propose a redefined direction for future MS research. A review of current papers published on precision medicine in MS reveals a need for (1) big data collection in MS care and (2) greater access to tools for precision medicine.


Diagnosis of Multiple Sclerosis

In a 2016 paper, Gafson et al. presented the latest approaches to diagnosing MS for patients exhibiting unconventional symptoms. The paper effectively informs the community of MS researchers and practitioners of a summary of successful, unsuccessful, and potential diagnosis methods.

In most cases, clinical symptoms, laboratory tests, and imaging are used for diagnosing MS syndrome. However, relying on only these steps cannot accurately rule out the potential of other diseases. Additionally, it is imperative to consider the various clinical and immunopathological subtypes of MS when diagnosing individual patients, which often cannot be discerned with simple laboratory tests [9].

The paper found that in a high percentage of clinics, cerebrospinal fluid examinations are utilized to identify distinct sub-syndromes of MS. High levels of astrocyte-derived chitinase 3-like protein 1 (CHI3L1) are often associated with a strong prediction of primary progressive (PP) MS [8]. In other cases, to further confirm and specialize in the diagnosis, auto-antibody testing was used. These tests identify the type of idiopathic demyelinating disorders in a patient. For example, Neuromyelitis Optica spectrum disorders (NMOSD) are accurately identified by serum antibodies against aquaporin [8]. This study highlighted the potential of combining both clinical and biomarker data when giving an early diagnosis and offering specific therapeutic advice to MS patients.


The Rise of PET Imaging

Poutiainen et al. presented a novel approach to precision medicine in MS by using positron emission tomography (PET) technology to detect inflammation and reactive astrocytes in the nervous system [3]. PET imaging is a non-invasive and precise imaging method that has recently shown potential in enhancing the early diagnosis of MS [10]. PET imaging provides functional information of molecular biology, allowing follow up of disease progression and treatment response [10]. 

The particular research study primarily focused on the modulation of different receptor systems and activation of glial cells, which serves an important function in the inflammatory aspect of MS (Figure 1). Findings suggested positive results with tracing the P2X7 receptor, adenosine receptors, cholinergic activity, cannabinoid receptors CB2, metabotropic glutamate receptors, and more [3]. These explorations, though positive, are not entirely validated. The paper acknowledges that though PET imaging is a powerful method for dynamic imaging, the full potential is not yet seen due to the lack of validated tracers. However, the multitude of ongoing PET imaging studies reveals that precision medicine could become more effective in MS. Overall, information from various biomarkers and imaging studies can be used for not just disease diagnosis, but potential prognosis.


Identification of Prognostic Factors

A 2015 paper published in the neurology journal Brain exploring high, medium, and low impact prognostics factors for developing MS is notably one of the few articles focused on the prognostic aspect of MS precision medicine [11]. While many advances have been made in this area, there are still limitations. For instance, magnetic resonance imaging (MRI) measures by gadolinium contrast enhancement or T2-hyperintense load have been valuable in predicting the risk of clinically definite disease [11]. Researchers are heavily reliant on MRI measures of disease activity in relation to age and sex as the primary method of prognostic diagnosis [12]. However, recent literature studies have found that the sensitivity of MRI can be as low as 35% [12]. Other epidemiological research studies have suggested that obesity, serum vitamin D, exposure to sunlight, and other lifestyle factors, like smoking, can impact prognosis [13]. However, this statement is limited as models that define quantitative interactions with individual susceptibilities are absent [13]. For instance, how could it be known if the impact of obesity is higher in people carrying a certain allele or with early presentation of the disease? 

To overcome the limitations that come from many confounding variables, a study by Tintore et al. in 2015 and many other researchers identified and stratified baseline characteristics of subjects. Some categories included demographic, biological, clinical, and radiological characteristics. Various statistical tests such as t-test and chi-squared test were employed to analyze the data and evaluate the high, medium, and low impact prognostic factors for developing MS. In short, the research study expressed that because there was no highly accurate predictive marker; precision medicine for MS is currently built on the foundation of analyzing multiple markers [11]. 


Precision Medicine Through Monitoring

The absence of a strong prospective marker turns neurologists to treatment monitoring and personalized medicine for patients. This is best seen in a 2012 study that focused on the monitoring of the natalizumab treatment, which is therapy through titers of anti-JC virus antibody [14]. Following this, physicians have been able to record treatment duration and previous history of immunosuppressive therapy in order to predict the patients’ risk of progressive multifocal leukoencephalopathy (PML) [14]. This personalization of medicine was internationally recognized when PML was identified as a complication. 

Though monitoring for risk was shown to be successful, monitoring for effectiveness has not yet seen a breakthrough. Additional biomarkers are being researched actively, but most reports are based on smaller sample sizes and have later failed to replicate. For example, researcher Kroksveen reported that 180 have proposed the success of the CSF MS biomarkers [15]. However, only 5% of the reports were validated [15]. More research that quantifies the success of treatment for individual patients is important as it would allow physicians to change therapy plans as needed.


A Need for Data Collection in MS Care

One of the limitations to progressing MS research is the lack of data collection. Without data that reveals how MS progresses in patients and how patients respond to treatment, it becomes even more difficult to identify biomarkers and other prognostic factors. Moreover, the potential that new and powerful technology like PET imaging cannot be exploited for furthering precision medicine in MS.

As mentioned earlier, MS is a long-term disease that patients must endure for decades. A large amount of important medical data accumulates throughout the year. Therefore, much of the gathered information such as symptoms, diagnostic measures, and therapeutic measures is susceptible to being lost. Even in the case of documentation, the responses to immunomodulatory therapy are not easily quantifiable. Moreover, psychological symptoms (like depression and fatigue) and other potential data sets (like urology and neuroradiology) are more often than not left out [16]. In order to account for all these challenges, there needs to be a complex documentation platform and process. 

Many studies have been calling for a comprehensive electronic database system. Additionally, MS experts are increasingly recommending the use of scales to quantify MS observations like the Expanded Disability Status Scale (EDSS) and the Multiple Sclerosis Functional Composite (MSFC) [17]. Employing scales such as these would allow easier sharing of information and would potentially advance and mobilize research at a faster and more accurate pace.

Despite the benefits of implementing a universal standard when collecting MS data, implementation into clinical practice has proven difficult. Differing practices from clinic to clinic was often a barrier for MS databases [18]. However, major sources of MS big data have recently appeared to gain more traction. With the use of clinical registries, electronic health record data, and administrative databases, more medical imaging, biomarkers, and other points of data are accessible and can easily be collected.


Computational Analysis

Along with an expanded database, machine learning algorithms should be integrated into the profiling and diagnosis of patients with MS. Dr. Ariel Miller, an expert in Neuroimmunology, emphasizes that three key information concepts will need to be integrated to successfully transition to personalized medicine in MS:

  1. Integrating genomic, molecular, and epigenetic data about each patient in a unified framework

  2. Effectively analyzing the data using complex queries and data mining methods

  3. Applying computational procedures that predict the patient’s response to treatment based on their genomic make-up, epigenetic tendencies, and environmental data” (Figure 2) [19]

Current barriers to big data analysis include a shortage of high-quality clinical data, legal and regulatory aspects of patient data privacy, and failure to employ newer MS techniques to the clinical workflow [20]. To make precision medicine a reality in MS, more advances must be made in bioinformatics and biological computation systems to process large sets of complex data. 


Greater Access to Tools and Approaches for Personalized Medicine in MS 

The modern MS community, including researchers, practitioners, industry, payers, and patients, is not on a standardized system. One country's approach to personalized medicine of MS may be completely different from another country’s approach. For example, the observation of MS disease phenotypes varies widely from patient to patient; therefore, assessment of the symptoms varies widely from clinic to clinic. There is no standardization when monitoring patients. However, the Multiple Sclerosis Performance Test (MSPT) has recently emerged at the forefront of MS research, showing promise in providing a better platform for assessing patients [21]. The MSPT is a computer program that attempts precise measurements of MS severity on observations of factors like manual dexterity, visual function, walking speed, and cognitive processing speed [21]. If universalized, the MSPT may bear fruitful progress in personalized MS care.

Another great way to stimulate progress in MS research would be the widespread sharing of anonymized, individual subject level data. Transparency in viewing and studying individual data, including clinical trial reports, would be a practical step towards personalized medicine. This would allow for more dynamic datasets and a healthcare system that continuously improves.

Addressing the wider entirety of the healthcare field, sharing the best of practices, and striving for the latest breakthroughs will support equal access to medicine. Helping all MS patients receive individualized and cost-optimal treatment is an essential objective to bear in mind. 



MS is multifaceted. Every patient is different, and there are countless factors to account for when optimizing a treatment option for an individual. The prevalence of MS is increasing, but there is relatively little data to personalize treatments and increase cost-effectiveness. 

Future research should concentrate on areas that have limited MS research. First, data collection and data analysis models must become the forefront of MS research. Computational models will aid in studying the many different potential biomarkers for diagnosis and prognosis. In addition, data and development must be shared on a standardized system. Coordination in the MS community would not only increase the effectiveness of research but also strengthen the quality of treatment to more patients debilitated by MS.


  1. (12/06/2020). http://www.mayoclinic.org/diseases-conditions/multiple-sclerosis/symptoms-causes/syc-20350269. Retrieved: 31/08/2020.

  2. Goldenberg, M. (03/2012). Multiple sclerosis review. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3351877/. Retrieved: 31/08/2020.

  3. Poutiainen, P. (15/09/2016). Precision Medicine in Multiple Sclerosis: Future of PET Imaging of Inflammation and Reactive Astrocytes. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5023680/. Retrieved: 31/08/2020.

  4. Dilokthornsakul, P. (15/03/2016). Multiple sclerosis prevalence in the United States commercially insured population. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4799713/. Retrieved: 31/08/2020.

  5. AJ, T. (01/06/2018). McDonald criteria. https://www.mstrust.org.uk/a-z/mcdonald-criteria.  Retrieved: 31/08/2020.

  6. Ziemssen, T. (02/08/2016). Multiple sclerosis: Clinical profiling and data collection as prerequisite for personalized medicine approach. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4971685/. Retrieved: 31/08/2020.

  7. White House Precision Medicine Initiative. https://obamawhitehouse.archives.gov/precision-medicine. Retrieved: 31/08/2020.

  8. Gafson, A. (2017). Personalised medicine for multiple sclerosis care. https://pubmed.ncbi.nlm.nih.gov/27672137/. Retrieved: 31/08/2020.

  9. Polman, C. (02/2011). Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3084507/ Retrieved: 31/08/2020.

  10. (25/08/2020). http://www.mayoclinic.org/tests-procedures/pet-scan/about/pac-20385078. Retrieved: 31/08/2020.

  11. Tintore, M. (21/04/2015). Defining high, medium and low impact prognostic factors for developing multiple sclerosis. https://academic.oup.com/brain/article/138/7/1863/253729. Retrieved: 31/08/2020.

  12. Tillema, J. (2013). Neuroradiological evaluation of demyelinating disease. https://pubmed.ncbi.nlm.nih.gov/23858328/. Retrieved: 31/08/2020.

  13. Ascherio, A. (2007). Environmental risk factors for multiple sclerosis. Part II: Noninfectious factors. https://pubmed.ncbi.nlm.nih.gov/17492755/. Retrieved: 31/08/2020.

  14. Bloomgren, G. (17/05/2012). Risk of Natalizumab-Associated Progressive Multifocal Leukoencephalopathy: NEJM. https://www.nejm.org/doi/full/10.1056/nejmoa1107829 Retrieved: 31/08/2020.

  15. Kroksveen, A. (2014). Cerebrospinal fluid proteomics in multiple sclerosis. https://pubmed.ncbi.nlm.nih.gov/25526888/ Retrieved: 31/08/2020.

  16. Ross, A. (2012). Assessing relapses and response to relapse treatment in patients with multiple sclerosis: A nursing perspective. https://pubmed.ncbi.nlm.nih.gov/24453746/. Retrieved: 31/08/2020.

  17. Ebers, G. (2008). Disability as an outcome in MS clinical trials. https://pubmed.ncbi.nlm.nih.gov/18480462/. Retrieved: 31/08/2020.

  18. Rauch, A. (2008). How to apply the International Classification of Functioning, Disability and Health (ICF) for rehabilitation management in clinical practice. https://pubmed.ncbi.nlm.nih.gov/18762742/.Retrieved: 31/08/2020.

  19. Miller, A. (2008). Translation towards personalized medicine in Multiple Sclerosis. https://pubmed.ncbi.nlm.nih.gov/18789804/. Retrieved: 31/08/2020.

  20. Raghupathi, W. (2014). Big data analytics in healthcare: Promise and potential. https://pubmed.ncbi.nlm.nih.gov/25825667/. Retrieved: 31/08/2020.

  21. Rudick, R. (2014). The Multiple Sclerosis Performance Test (MSPT): An iPad-based disability assessment tool. https://pubmed.ncbi.nlm.nih.gov/25046650/. Retrieved: 31/08/2020.

Helen Kim

Helen Kim

Helen Kim is a high school senior in Southern California. She has strong interests in medicine, biology, and public health. In her free time, she loves running, walking, and listening to music.