Category Archives: Research Decoder

Research Decoder: Gut Microbiome

hOur digestive tract contains trillions of bacteria encompassing thousands of different species that collectively make up the gut microbiome. In fact, the number of bacterial cells that have colonized our bodies outnumbers the human cells that make up our bodies by a factor of ten to one, and the majority of those bacteria reside in our gut! These little passengers aren’t just along for the ride, and you could say that we’ve entered into a mutually beneficial contract with them; they enjoy a place to call home and a constant supply of food, while we benefit from their ability to break down our food into vital nutrients.

It turns out, however, that the gut microbiome plays an even larger role than helping us digest our food, and many of our body’s biological functions – from our metabolic processes to our immune system – are exquisitely sensitive to changes in the composition of the bacteria that reside in our gut. Disruption of this delicate balance has been implicated in a host of health conditions such as diabetes, obesity and autoimmune diseases like inflammatory bowel disorder and multiple sclerosis. I will return to the link between the gut microbiome and MS in a moment.

The microbial ecosystem in our guts is shaped by the various exposures that we come across in life, such as the food we eat, the air we breathe, any infections we’ve acquired or drugs we’ve taken, along with a host of other environmental factors. These environmental exposures interact with our body’s own physiological processes, such as metabolism and stress, to further alter the balance of our microbial communities. There’s even evidence to suggest that our genes have a hand in influencing the composition and abundance of the gut microbiome, and that these “heritable” strains of bacteria are associated with health and disease. What this means is that each individual’s gut microbiome is unique, acting as a microbial “fingerprint” that carries the potential in the future to help predict risk of various diseases or offer new therapeutic targets.

Credits: Daniel Mietchen / CC BY 2.0 (Wikimedia Commons

Credits: Daniel Mietchen / CC BY 2.0 (Wikimedia Commons)

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Research Decoder: Outcome measures I – Clinical

hClinical trials are an indispensable part of the process that determines which disease-treating interventions (including drugs, procedures, technologies, or lifestyle modifications) can move from the experimental stage to the public realm. Health care decision makers that establish health policy, such as the approval of new drugs or repurposing existing drugs for new indications, rely on the findings of clinical trials about the safety and effectiveness of an intervention since they provide some of the most reliable evidence of the value of an intervention. But how do the investigators who conduct the trials determine whether an intervention really works?

When designing a clinical trial, research investigators chose appropriate outcome measures, or endpoints, that they will monitor in order to determine the effectiveness of the intervention they are studying. Outcome measures come in a variety of forms, and can be either objective (such as the results of a laboratory test), subjective (such as a participant’s perception of their fatigue) or oftentimes a combination of the two. They can also be classified as primary or secondary. Primary endpoints measure outcomes that answer the key question being asked by the trial, and the number of participants enrolled in the trial is chosen in order to have enough statistical power to detect differences in this primary measure. Secondary endpoints capture measures that, although not as imperative as the primary endpoint, are still relevant to the study and can sometimes even lead to follow-up trials in which investigators have the chance to fine-tune the outcome measures and study design.

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Research Decoder: Biomarkers

hThe term “biomarker” is a popular buzzword in the biomedical research world, and for good reason. Biomarkers are an indispensable part of the researcher’s and clinician’s toolkit, and offer a powerful approach to improving disease prevention, diagnosis, and management. It can also be a confusing term, and in this Research Decoder I’ll be giving you a quick 101 on biomarkers.

Biomarkers — a portmanteau of “biological” and “markers” — are signatures found in the body that can be objectively measured and can be an indicator of your health or reveal the presence or progress of a disease. For example, blood pressure can serve as a biomarker for your cardiovascular health, while blood glucose can be a biomarker for diabetes. Biomarkers come in different shapes and sizes: molecular biomarkers are measured in biological samples (e.g. blood, urine, tissue, cerebrospinal fluid, etc.), recording biomarkers are features of your vital signs (e.g. blood pressure, temperature, etc.) and imaging biomarkers are characteristics that can be detected in a biological image (e.g. X-ray, magnetic resonance imaging, etc.).

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Research Decoder: Comorbidities

hA comorbidity is a chronic medical condition that occurs alongside an existing one. For example, someone diagnosed with both multiple sclerosis and hypertension would be said to have two comorbid conditions. Comorbidities are of paramount concern to clinicians and other care providers, since they can often interact with MS in unpredictable ways, in turn complicating the course or treatment of MS, impacting quality of life and influencing disease progression. A proper understanding of the types and frequencies of comorbidities among people living with MS is a crucial step in figuring out the optimal treatment and management plan that suits each individual. Comprehensive and accurate information on the population patterns of comorbidities is also indispensable for designing proper research studies in order to account for any complicating factors that could end up skewing the results.

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Research Decoder: Why Grey Matter Matters

hMultiple sclerosis has classically been considered a disease of the white matter. What does this mean, exactly?

The brain and spinal cord (which together make up the central nervous system, or CNS) can be roughly categorized into two kinds of nervous tissue: white matter and grey matter. White matter is made up of bundles of nerve fibres that connect disparate parts of the brain together and allow these regions to communicate via electrical signals. These nerve fibers are coated in myelin, an insulating fatty layer that appears white when examined by the naked eye; hence the name.

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Research Decoder: The Blood-Brain Barrier

hWhile reading about multiple sclerosis, you may have come across the blood-brain barrier and wondered what it’s all about. Many people think of the blood brain barrier (BBB) as some sort of membrane or casing that surrounds the brain and keeps anything from moving in and out. In fact, the BBB is a feature of the walls making up the blood vessels that supply the central nervous system (CNS), including the brain. The BBB is selectively permeable; that is, it lets certain substances pass across it between the blood and the CNS (and vice versa) while blocking the movement of others. This way, the BBB wields precise control over anything that enters or leaves the brain, in turn helping the brain to maintain a constant environment.

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Research Decoder: Remyelination versus Neuroprotection

hTwo words that I often see used interchangeably are remyelination and neuroprotection. Both processes are part of the body’s innate repair and damage prevention system, and they are critical tools for restoring function and integrity to damaged nerve cells and their protective coating in people with multiple sclerosis. However, remyelination and neuroprotection are distinct processes that lend themselves to different therapeutic approaches, and so knowing the difference between these terms is important in order to best capitalize on the diversity of molecular and cellular targets at our disposal for fighting MS.

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Research Decoder: Negative Results

hIn scientific research, negative results can get a bad rap. What exactly do I mean when I say “negative results”? At its core, scientific research is the process of developing a research question, formulating a hypothesis (an educated prediction) about the outcome of that question, then testing the hypothesis through a system of measurement and observation.

For example, a scientist may want to know whether drinking alcohol in the evening leads to louder snoring during sleep that night. In other words, she is proposing a possible relationship between two variables: the independent variable (what the researcher changes, i.e. alcohol consumption in the evening) and the dependent variable (what the researcher will measure, i.e. volume of snoring during the following night).

In reality, what the scientist is really testing is the null hypothesis, which states that there is no relationship between the two variables; or, in this case, alcohol does not lead to louder snoring. Let’s suppose that after performing a well-designed study with proper statistical analysis in a group of subjects, the scientist finds that subjects who drank alcohol snored at a statistically higher volume than those who didn’t drink alcohol. She can then reject the null hypothesis and conclude that drinking alcohol in the evening leads to louder snoring that night. If there is no significant difference between the two groups, then the observed snoring is likely unrelated to drinking alcohol. The scientist would regard this as a negative result.

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Research Decoder: Risk Factors

hAlthough there is no single cause for multiple sclerosis, one priority in research is to identify different risk factors associated with MS. A risk factor is anything that can affect your chances of getting a disease like MS. Some risk factors, like your age, sex, or family history, cannot be changed, and are referred to as non-modifiable risk factors. Others, such as lifestyle and environmental influences, are avoidable and are termed modifiable risk factors, although in some cases changing these factors is easier said than done.

Having one or more risk factors for a disease does not necessarily guarantee that you will develop that disease, but it can increase your chances. On the other hand, some people can have all of the risk factors for a disease and remain disease-free. Take smoking, for example. Although smoking is the number one risk factor for lung cancer, some people can smoke their entire lives without getting lung cancer. Information about risk factors comes from the study of large groups of people, and various risk factors can interact in complex ways; for that reason, making definitive conclusions about how risk factors can lead to disease in each individual person can be tricky. At the end of the day, however, the best way to minimize your chances of developing MS is to manage the risk factors that you can control.

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Research Decoder: Clinical Trials

h
There’s a lot of buzz in the world of research and in the media about clinical trials. Indeed, just a few weeks ago there was a great deal of excitement surrounding our announcement of the MESCAMS trial investigating the safety and efficacy of mesenchymal stem cell therapy in people living with MS. Yet for all the attention they receive, clinical trials are quite often a misunderstood topic. Since clinical trials represent the final link between the laboratory discovery of a therapy, and having that therapy be made readily available to the public, it’s important to clear up any confusion about the subject and ensure the process is transparent and understandable.

In this week’s Research Decoder, I’ll be debunking some of the most common misconceptions about clinical trials. There are already many fantastic resources available online explaining what clinical trials are, why they’re important, and what some of the jargon means (not least of all our very own Introduction to Clinical Trials), so I won’t be retreading charted territory. Instead, I’ll focus on three statements I hear regularly from people who ask me about the ins and outs of clinical trials.

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