Listen to the Clinical Chemistry Podcast
Pedrum Mohammadi-Shemirani, et al., A Mendelian Randomization-Based Approach to Identify Early and Sensitive Diagnostic Biomarkers of Disease
Clin Chem 2019;65:427-36.
Drs. Pedrum Mohammadi-Shemirani and Guillaume Paré of McMaster University in Hamilton, Ontario, Canada.
This is a podcast from Clinical Chemistry, sponsored by the Department of Laboratory Medicine at Boston Children’s Hospital. I am Bob Barrett.
Identifying markers of chronic kidney disease that occur early in the disease process and are specific to loss of kidney function may allow timely more accurate identification of patients who will eventually develop the disease.
In the March 2019 issue of Clinical Chemistry, a paper investigated potential blood markers of early chronic kidney disease which are caused by loss of kidney function using an innovative reverse Mendelian randomization approach. Mendelian randomization is a genetic epidemiological approach that is made substantial inroads into our understanding of the causes and consequences of disease.
Two of the authors of that paper are Pedrum Mohammadi-Shemirani and Guillaume Paré. Dr. Paré is Associate Professor and University Scholar in the Department of Pathology and Molecular Medicine at McMaster University in Hamilton, Ontario, in Canada, where he also serves as Director of Genetic and Molecular Epidemiology at the Laboratory Population Health Research Institute.
Pedrum Mohammadi-Shemirani is a doctoral student of Professor Paré and specializes in bioinformatics research with a focus on applying genomics and molecular data to better understand disease and improve patient care and outcomes. They are both our guests in this podcast, and Dr. Paré, we’ll start with you.
Before we delve into reverse Mendelian randomization, what exactly is the rationale behind the technique of Mendelian randomization? How is it used in epidemiology?
So, Mendelian randomization is a statistical genetic technique to infer causal relationship between risk factors and outcome of interest. And it relies on the second law of Mendel, which is the random allocation of alleles at meiosis, which essentially states that alleles are randomly allocated at meiosis and this randomization is very useful, and it’s
useful for two reasons.
First of all is that it is immune to some of the confounding
that we can find in epidemiological studies such as, let’s say,
the environment or other exposures that have nothing to do
with the disease of interest. And the second reason why
this is very useful is because it’s not immune to reverse
causation. So, genetic variants can lead to a disease. But
generally speaking, a disease will not lead to a genetic
And essentially, why this is so useful and getting so popular
is that we can then test hypothesis on whether, for
example, LDL cholesterol is causally related to
cardiovascular disease. And in this case, both Mendelian
randomization and clinical trials are telling us that, “yes.”
And this is very important because this is not always the
case for the risk factor outcome association that we see. A
good way to conceptualize Mendelian randomization is to
look at the parallel between Mendelian randomization and a
randomized clinical trial.
So, in a randomized clinical trial, an investigator will
randomize participants to either an active drug or placebo.
And then, after a few years of follow up, we will see the
effect of this drug, for example, on blood cholesterol, and
we will see the effect of this drug on cardiovascular
outcomes, and we will decide whether this drug is
efficacious or not at decreasing cardiovascular disease.
Mendelian randomization has many parallels except that, in
this case, it’s not an investigator that’s randomizing
individuals due to drug or not, it’s the second law of Mendel
that is randomizing individuals to have alleles that either
increases a little bit cholesterol or decreases cholesterol by a
tiny bit. And then, after a follow up, we see if people with
the cholesterol-raising allele developed more cardiovascular
Some of the key differences here is that in a drug trial,
usually the effect on the risk factor is quite pronounced.
When we think in terms of genetic effects, these genetic
effects are usually tiny. But on the other hand, we have a
lifelong follow-up of a genetic exposure. So, people are
born with an allele that gives them slightly higher
cholesterol, whereas in a randomized clinical trial, the follow
up is limited by the length of follow-up of that trial.
And how did the idea of reverse Mendelian randomization
come about? Why would that be important to the context of
identifying diagnostic biomarkers?
So, for the first part, Mendelian randomization is
traditionally applied in the context of identifying a biomarker
that causes a disease. So, these biomarkers would be, for
example, promising targets for pharmaceutical
interventions, and this has been shown in the context of
For example, PCSK9 inhibitors, statins, and more recently,
ACLY, have all been validated with Mendelian randomization
to lower LDL cholesterol and decrease the risk of coronary
artery disease, which has also been shown in corresponding
randomized control trials.
And so, it’s kind of a natural extension to apply these
principles of Mendelian randomization in the opposite
direction to instead investigate, “Does a disease cause any
biomarkers to be elevated or decreased?” And this question
is better suited to addressing whether a biomarker would be
a promising diagnostic candidate rather than a
So, to answer the second part of that question, “Why is
causality important?”, causality is important because a
causal diagnostic biomarker would, in theory, always be a
consequence of the disease, and therefore be more sensitive
as well as being expressed earlier in the pathogenesis of the
disease. For instance, in the context of CKD, an
observational study may find several serum biomarkers
elevated in CKD cases relative to controls. However, some
of these biomarkers may be due to an earlier cause of CKD
like diabetes or BMI rather than CKD itself.
Another useful aspect of this reverse Mendelian
randomization is investigating whether these biomarkers
that maybe are identified would be caused by the disease or
we can investigate to see whether they are indeed caused
by diabetes, or obesity, or any other common set of risk
factors for the disease of interest.
I would say that, essentially, the promise of reverse
Mendelian randomization is to identify biomarkers that are
both more sensitive and more specific to disease. Sensitive
because they can detect early disease though these genetic
associations, but specific because we can also test for
potential confounders or related risk factors and to make
sure that they do not impact the biomarker of interest.
Okay. Now, in your paper in Clinical Chemistry, you applied
that technique to kidney function and chronic kidney
disease. Why did you choose that area of disease and
So, this is an excellent question and I think you know our
paper can be seen as a proof of concept here on applying
Mendelian randomization to biomarker discovery. We
thought that CKD was a particularly good example for two
The first reason, which is really the most important one is
that there is a clinical need for earlier markers of CKD.
Unfortunately, we do know that carotenemia is relatively
insensitive to early changes in kidney function. And
likewise, albuminuria can be considered as a later marker of
On the other hand, we do have interventions that are
efficacious at decreasing progression of kidney disease. And
therefore, there has been unmet medical need to have
earlier markers of chronic kidney disease so we can identify
individuals with very early, very subtle decrease in kidney
function, and we could have interventions to stymie the loss
of kidney function in these individuals.
The second reason is that, we also add a lot of genetic data
that gives us the primary material to be able to apply this
method. In fact, there are many very large international
consortia that have looked at the genetics of eGFR and CKD,
and we really need to have these data to be able to apply
reverse Mendelian randomization.
So, in effect, there was a little bit of a perfect storm
between something that we thought is clinically useful and
for which we thought we had everything necessary to
conduct the analysis.
Your results suggest that protein trefoil factor 3 may be a
valuable diagnostic marker for early chronic kidney disease.
What is known about that protein and its potential role in
chronic kidney disease?
Yeah. So, trefoil factor 3 is actually a part of a broader
family of trefoil factor proteins, which also include trefoil
factor 1 and trefoil factor 2. And their biological role is
largely unclear, currently.
There is some literature that has investigated the role in
terms of colorectal cancers and other diseases, but there
have also been several smaller observational studies that we
have cited in our paper that identified increased trefoil factor
3 to be predictive of decreased eGFR in both urine and
serum samples. And the prevailing thought is that trefoil
factor 3 is a protein that’s involved in cellular repair and
cellular restitution, which also makes sense in the context of
our data since we found that increased trefoil factor 3 is the
cause of chronic kidney disease and decrease to GFR, which suggests that the kidney perhaps may be attempting to
repair damage that is being done over the course of this
Well, finally, let’s look ahead. There must be other diseases
where you can apply reverse Mendelian randomization.
Where do we go from here? Do you have some prime
Yes, absolutely. I think we have shown in this report that
reverse Mendelian randomization can be used to identify
markers of disease. And I think there are many more
diseases where we could have improved blood biomarkers.
One of them that we’re particularly interested in is early
cognitive decline, which is a terrible disease and I think that
adding early markers would be useful. And I think we can
think about the whole host of latency disease where we
could intervene in early stages that we could see benefit.
And I think cancer comes to mind as well.
And I think, clearly, with data accumulating rapidly, both
genetic data that is necessary to apply these techniques but
also with proteomics data and metabolomics, we hope to be
able to apply them to not only more disease but more
comprehensive set of biomarkers to try to find like the very
best biomarkers for each of these diseases.
That was Dr. Guillaume Paré in the Department of Pathology
and Molecular Medicine at McMaster University, Hamilton,
Ontario in Canada. He was joined by his co-author, Pedrum
Mohammadi-Shemirani, a doctoral student of Professor
Paré. They have both been our guests in this podcast on
potential early markers of chronic kidney disease uncovered
by reverse Mendelian randomization. Their paper appears in
the March 2019 issue of Clinical Chemistry. I’m Bob Barrett.
Thanks for listening!