Biostatistics Unit, Faculty of Medicine, Universitat
Autonoma de Barcelona, and a healthcare startup called
Curelator Inc.
This study 6 , published in Cephalalgia, the journal of the
International Headache Society, examined risk profiles
of more than 300 individual patients. A key aspect of
this research was that it examined a previously analyzed
database from a landmark study called the PAMINA
study 7 where individuals recorded their daily exposure (or
lack thereof ) to a list of commonly believed “risk factors”
(e.g. commonly called “triggers” but also includes non-
causal risk factors such as symptoms that might precede
attacks or be part of an attack, e.g. neck pain) associated
with migraine: weather, dietary, emotional, physical,
etc. The original PAMINA study looked at the aggregate
population, which yielded the most common trigger
associations in that population, namely the “average
trigger profile” of the average migraineur. In contrast,
the new Cephalalgia study reanalyzed the PAMINA
database but did so in each of the individual patients.
This individualized approach revealed two unexpected
findings.
First, virtually all of the patients in the study where
a trigger profile was generated showed unique profiles.
How many shared an average profile of four potential
triggers -- the most common being menstruation, neck
pain, tiredness, and bright lights? Not even one patient.
Second, the data 8 revealed that trigger factors in some
people were protective factors in others, and vice-versa.
To be clear: trigger factors are associated with increasing
the risk of migraine while protective factors are associated
with decreasing risk of migraine. Why is this an alarm-
ing result? Because it is one thing to say: there are factors,
possibly including therapeutics, that work in some but
don’t work in others. It is entirely another thing to say:
this works in some but possibly causes harm in others.
The appearance of “protectors” in individual patients
is a significant observation. In chronic diseases such as
migraine, protectors - factors associated with decreased
risk of an attack - have been observed but never
measured before. Why not? One explanation is that the
aggregation of patient data is subject to a phenomena
called, “Simpson’s paradox” 9 , which concerns the loss of
individual signal after population data aggregation occurs,
especially in disease populations with high individual
diversity. As an example of Simpson’s paradox, if 10
individuals are each sensitive to 10 different protectors,
and furthermore, those prot ectors are triggers in other
individuals, then the protector signal will likely be lost
after data aggregation.
If we proceed in this
manner, the journey
started last century
to discover individual
variation may
finally acquire
a sense of urgency.
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