Currently, blood tests fail to consider individual differences. However, Brody and his team at Washington University aim to alter that.
If you’ve ever had a doctor order a blood
test for you, chances are that they ran a complete blood count, or CBC. One of
the most common blood tests in the world, CBC tests are run billions of times
each year to diagnose conditions and monitor patients’ health.
But
despite the test’s ubiquity, the way clinicians interpret and use it in the
clinic is often less precise than ideal. Currently, blood test readings are
based on one-size-fits-all reference intervals that don’t account for
individual differences.
Brody H.
Foy and his team, at the University of Washington, studies ways to use
computational tools to improve clinical blood testing. To develop better ways
to capture individual patient definitions of “normal” lab values, Foy and his
colleagues in the Higgins Lab at Harvard Medical School examined 20 years of
blood count tests from tens of thousands of patients from both the East and
West coasts.
In their newly published research, they
used machine learning to identify healthy blood count ranges for individual
patients and predict their risk of future disease.
Clinical
tests and complete blood counts
Many people
commonly think of clinical tests as purely diagnostic. For example, a COVID-19
or a pregnancy test comes back as either positive or negative, telling you
whether you have a particular condition. However, most tests don’t work this
way. Instead, they measure a biological trait that your body continuously
regulates up and down to stay within certain bounds.
Your
complete blood count is also a continuum. The CBC test creates a detailed
profile of your blood cells – such as how many red blood cells, platelets and
white blood cells are in your blood. These markers are used every day in nearly
all areas of medicine.
For
example, hemoglobin is an iron-containing protein that allows your red blood
cells to carry oxygen. If your hemoglobin levels are low, it might mean you are
iron deficient.
Platelets
are cells that help form blood clots and stop bleeding. If your platelet count
is low, it may mean you have some internal bleeding and your body is using
platelets to help form blood clots to plug the wound.
White
blood cells are part of your immune system. If your white cell count is high,
it might mean you have an infection and your body is producing more of these
cells to fight it off.
Normal
ranges and reference intervals
But this
all raises the question: What actually counts as too high or too low on a blood
test?
Traditionally,
clinicians determine what are called reference intervals by measuring a blood
test in a range of healthy people. They usually take the middle 95% of these
healthy values and call that “normal,” with anything above or below being too
low or high. These normal ranges are used nearly everywhere in medicine.
But reference
intervals face a big challenge: What’s normal for you may not be normal for
someone else.
Nearly
all blood count markers are heritable, meaning your genetics and environment
determine much of what the healthy value for each marker would be for you.
At the
population level, for example, a normal platelet count is approximately between
150 and 400 billion cells per liter of blood. But your body may want to
maintain a platelet count of 200 – a value called your set point. This means
your normal range might only be 150 to 250.
Differences
between a patient’s true normal range and the population-based reference
interval can create problems for doctors. They may be less likely to diagnose a
disease if your set point is far from a cutoff. Conversely, they may run
unnecessary tests if your set point is too close to a cutoff.
Defining
what’s normal for you
Luckily,
many patients get blood counts each year as part of routine checkups. Using
machine learning models, Foy and his team were able to estimate blood count set
points for over 50,000 patients based on their history of visits to the clinic.
This allowed them to study how the body regulates these set points and to test
whether they can build better ways of personalizing lab test readings.
Over
multiple decades, they found that individual normal ranges were about three
times smaller than at the population level. For example, while the “normal”
range for the white blood cell count is around 4.0 to 11.0 billion cells per
liter of blood, they found that most people’s individual ranges were much
narrower, more like 4.5 to 7, or 7.5 to 10.
When Foy
and his team used these set points to interpret new test results, these results
helped improve diagnosis of diseases such as iron deficiency, chronic kidney
disease and hypothyroidism. They could note when someone’s result was outside
their smaller personal range, potentially indicating an issue, even if the
result was within the normal range for the population overall.
The set points themselves were strong
indicators for future risk of developing a disease. For example, patients with
high white blood cell set points were more likely to develop Type 2 diabetes in
the future. These patients were also nearly twice as likely to die of any cause
compared with similar patients with low white cell counts. Other blood count
markers were also strong predictors of future disease and mortality risk.
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