Background: TBI biomarkers display population-level time-varying
kinetics [1] which may be a rich source of pathobiological information
[2]. At an individual level, deviations from stereotypical trajectories
may represent different pathological processes or secondary insults.
A method for discovering such phenotypes may be useful in in-
dividualising treatments in real-time.
Methods: Serial blood (12hourly) and CSF (6hourly) samples were
obtained from seventeen adult patients with severe TBI (Stockholm
ethics committee approval #2009/1112-31). S100B and neuron-specific
enolase (NSE) concentrations were measured along with blood:CSF
albumin quotient Qa as a measure of blood-brain-barrier (BBB) integrity.
S100B and NSE concentrations were log-transformed: Equivalent to the
assumption of baseline exponential decay. We used trajectory modeling
combining a quadratic mixed effects model with latent group analysis to
search for characteristic trajectories in the measured parameter.
Results: For serum S100B, we discovered two phenotypes with fast
and slow kinetics. The fast group corresponded with patients with
more severe extracranial injury. For serum NSE, again two phenotypes
were discovered; a time-decaying group and another with a peak
around day 4. CSF analysis yielded two latent groups for both S100B
and NSE: a time-decaying group and another displaying prolonged
elevation over several days. Qa data clustered into three groups: two
with fast, slow decay and another with prolonged elevation. The group
with prolonged BBB permeability had corresponding poorer outcomes.
Conclusions: Small numbers prevent statistical comparison, but
trajectory modeling identified a number of phenotypes with plausible
pathobiological significance. In particular the technique revealed a
group of patients with secondary serum NSE release and another with
sustained BBB permeability. Such groups seem to relate to injury
profile and outcome suggesting biological relevance. To our knowledge
this is the first use of an unsupervised clustering technique in kinetic
phenotype discovery.
References:
[1] Ercole A, Thelin EP, Holst A, Bellander BM, Nelson DW.
Kinetic modelling of serum S100b after traumatic brain injury. BMC
Neurol. 2016;16:93.
[2] Thelin EP, Zeiler FA, Ercole A, Mondello S, Büki A, Bellander
BM, Helmy A, Menon DK, Nelson DW. Serial Sampling of Serum
Protein Biomarkers for Monitoring Human Traumatic Brain Injury
Dynamics: A Systematic Review. Front Neurol. 2017;8:300