McLouth et al. Journal of Neuroinflammation
https://doi.org/10.1186/s12974-024-03201-9(2024) 21:205

Journal of Neuroinflammation

Open Access

RESEARCH

The differential proteomic response
to ischemic stroke in appalachian subjects
treated with mechanical thrombectomy
Christopher J. McLouth1,2, Benton Maglinger3, Jacqueline A. Frank4,7, Hunter S. Hazelwood8, Jordan P. Harp2,7,Will Cranford1, Shivani Pahwa4,5,6, Lila Sheikhi2,4,5,6, David Dornbos III4,5,6, Amanda L. Trout4,7, Ann M. Stowe2,6,7,
Justin F. Fraser2,4,5,7 and Keith R. Pennypacker2,6,7*
Abstract
Introduction The Appalachia region of North America is known to have significant health disparities, specifically,worse risk factors and outcomes for stroke. Appalachians are more likely to have comorbidities related to stroke,such as diabetes, obesity, and tobacco use, and are often less likely to have stroke interventions, such as mechanicalthrombectomy (MT), for emergent large vessel occlusion (ELVO). As our Comprehensive Stroke Center directly
serves stroke subjects from both Appalachian and non-Appalachian areas, inflammatory proteomic biomarkers wereidentified associated with stroke outcomes specific to subjects residing in Appalachia.
Methods There were 81 subjects that met inclusion criteria for this study. These subjects underwent MT for ELVO,
and carotid arterial blood samples acquired at time of intervention were sent for proteomic analysis. Samples were
processed in accordance with the Blood And Clot Thrombectomy Registry And Collaboration (BACTRAC; clinicaltrials.
gov; NCT 03153683). Statistical analyses were utilized to examine whether relationships between protein expression
and outcomes differed by Appalachian status for functional (NIH Stroke Scale; NIHSS and Modified Rankin Score; mRS),
and cognitive outcomes (Montreal Cognitive Assessment; MoCA).
Results No significant differences were found in demographic data or co-morbidities when comparing Appalachian
to non-Appalachian subjects. However, time from stroke onset to treatment (last known normal) was significantly
longer and edema volume significantly higher in patients from Appalachia. Further, when comparing Appalachian
to non-Appalachian subjects, there were significant unadjusted differences in the NIHSS functional outcome. A
comprehensive analysis of 184 proteins from Olink proteomic (92 Cardiometabolic and 92 Inflammation panels)
showed that the association between protein expression outcomes significantly differed by Appalachian status forseven proteins for the NIHSS, two proteins for the MoCA, and three for the mRS.
Conclusion Our study utilizes an ELVO tissue bank and registry to investigate the intracranial/intravascular proteomic
environment occurring at the time of thrombectomy. We found that patients presenting from Appalachian areas
have different levels of proteomic expression at the time of MT when compared to patients presenting from non-*Correspondence:
Keith R. Pennypacker
keith.pennypacker@uky.edu
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
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McLouth et al. Journal of Neuroinflammation(2024) 21:205

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Appalachian areas. These proteins differentially relate to stroke outcome and could be used as prognostic biomarkers,
or as targets for novel therapies. The identification of a disparate proteomic response in Appalachian patients provides
initial insight to the biological basis for health disparity. Nevertheless, further investigations through communitybased studies are imperative to elucidate the underlying causes of this differential response.

Introduction
The Appalachian region of North America is a subpopulation of the country that has become an area of greatinterest regarding access to care, health disparities, andhealth outcomes. Appalachia has a high proportionof rural, Caucasian, socioeconomically and medicallyunderprivileged individuals [1]. As reported by the Appalachian Regional Commission in 2017, the stroke mortality rate in Appalachia is higher than in the rest of theUnited States [2]. This report also includes data that individuals from Appalachia are more likely to have strokerelated comorbidities including diabetes, obesity, andincreased tobacco usage.
There are approximately 800,000 strokes in the US eachyear, of which 87% are ischemic [3]. Within the ischemic
stroke subset, approximately 30–40% are emergent large
vessel occlusions (ELVOs), which often represent themost debilitating cerebrovascular blockages. [4] Whensubjects with ELVOs meet criteria, they are considered
for endovascular recanalization by mechanical thrombectomy (MT). Rural-urban disparities exist regarding
the use and clinical outcomes of MT in which clinicaloutcomes are poorer among rural patients when compared to their urban counterparts [5]. Our institution is
optimally positioned for research in this field as it provides MT to ELVO subjects in which over 70% are from
Appalachian counties. Moreover, our institution has
developed a human stroke biospecimen tissue bank andregistry, the Blood And Clot Thrombectomy Registry
And Collaboration (BACTRAC; clinicaltrials.gov; NCT
03153683). For each consented thrombectomy subject
who meets inclusion criteria, the BACTRAC registry isolates an intracranial (distal to thrombus) arterial blood
sample, a systemic (carotid) arterial blood sample, andthe thrombus for research purposes. provides an unique
opportunity to commence an understanding on the biological basis for this health disparity in stroke outcome
within this patient cohort from Appalachia.
Although handling human data is exceedingly complex,
new statistical tools are available to address the multiple
co-variables associated with human conditions. Ourgroup has used these methods to link arterial blood gasses, electrolyte chemistry, transcriptomics, proteomics,
short-chain fatty acids, and neuroinflammatory cells toclinical outcomes in stroke patients undergoing thrombectomy [6–13]. Our proteomic studies focus on inflammatory and cardiometabolic proteins, which are early
responders to ischemic injury. The objective of this study

is to utilize BACTRAC to identify plasma proteins thatare differentially expressed between Appalachian and
non-Appalachian stroke subjects undergoing MT, andthat are uniquely associated with stroke outcomes basedon location. Proteins and signaling functions identified
here will serve as specific potential biomarkers for clinical outcomes and potential targets for novel/existing
therapeutics to help improve stroke care for a criticallyimportant and underserved population.

Methods
Patient categorization

A total of 81 subjects were included in this study,
with 58 (71.6%) subjects from Appalachian areasand 23 (28.4%) subjects from non-Appalachianareas. Stroke subjects were categorized into “Appalachian” or “non-Appalachian” based on the most current data from the Appalachian Regional Commission(ARC). The list of which counties ARC determines
to be included in the Appalachian designation can beviewed on their public website (https://www.arc.gov/
appalachian-counties-served-by-arc/).
Biospecimen sampling

The current study was approved by the University of
Kentucky Institutional Review Board (IRB) and utilizes
the prospective BACTRAC tissue registry (clinicaltrials.gov; NCT 03153683), which is comprised of humanbiospecimens acquired during MT for ELVO stroke subjects. Inclusion criteria included all ELVO subjects who
were candidates for MT and aged 18 or older. Exclusion
criteria included pregnancy, incarceration, and individuals less than 18 years of age, as well as subjects unable
to consent within the IRB-outlined 72-hour window.
Subjects included in this current study were enrolled
between June 21, 2017, and March 1, 2021. Clinicaldata were collected on each subject from an electronic
health record and entered into a REDCap database. Data
included demographics, comorbidities, relevant labs,
radiographic outcome, thrombectomy outcome, andboth functional and cognitive outcome metrics.
Methods for tissue processing for proteomic analysis have been previously published [14]. Briefly, before
recanalization, systemic arterial blood samples were collected from the carotid artery. These samples were thenaliquoted into BD Microtainer tubes with K2E (K2EDTA;
Becton, Dickinson and Company), spun down at 2000
revolutions for 15 min, and plasma was extracted andMcLouth et al. Journal of Neuroinflammation(2024) 21:205

immediately frozen on dry ice in a Wheaton CryoELITE
cryogenic vial (DWK Life Sciences; Millville, New Jersey). The samples were then stored at -80° C until sent toOlink Proteomics (Olink Proteomics, Boston, MA) foranalysis of plasma protein.
Clinical data acquisition

The National Institutes of Health Stroke Scale (NIHSS)
is a standardized assessment tool used to evaluate theseverity of stroke by assessing various neurological functions (motor strength, eye movements, sensation, speech,etc.). [15] This score is documented for each subject attime of presentation as well as at time of discharge. The
modified Rankin Scale (mRS) is another standardized
tool which assesses functional disability or dependence inpatients who have experienced a neurologic injury, suchas stroke. [16] It is a 6-point scale that ranges from 0 (nosymptoms) to 6 (severe disability or death). For analytic
purposes, this metric was recategorized as 0–2, 3–4, and5 + to represent slight or no disability, moderate disability,
and severe disability or death, respectively. The Montreal
Cognitive Assessment (MoCA) is a widely used screening
tool to assess cognitive function in adults. [17] The test
measures different cognitive domains, such as attention,
memory, language, orientation, visuospatial skills, andexecutive function. Some subjects were administered anabbreviated version of the MoCA, so those scores werere-scaled to the maximum score of 30.
Proteomic and statistical analysis

Isolated plasma samples were sent to Olink Proteomics
for analysis of using their Target 92 Cardiometabolic andTarget 92 Inflammation Panels. Olink returns proteomic
expression values in a Normalized Protein eXpression(NPX) value, which is in log2 scale, where a one-unit
increase corresponds to a doubling of protein concentration, to reduce intra- and inter-assay variability whenrunning statistics across sample sets. As of 2023, Olink
had been included in over 1200 publications (https://www.olink.com/). For statistical analyses, between-groupcomparisons on demographic, baseline, and clinical characteristics were performed using chi-square or Fisher’s
exact tests for quantitative variables and t-tests or Wilcoxon rank-sum tests for continuous variables. Next, totest for differences in proteomic expression based on
location, a series of independent samples t-tests wereconducted comparing protein expression between theAppalachian and non-Appalachian samples. For the main
study hypotheses, a series of general linear models wereused to determine if the relationship between protein
quantity and stroke severity (as measured by the NIHSS)
or cognitive impairment (as measured by the MoCA) differed by Appalachian status. These models included themain effect of a protein, a dummy variable representing

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Appalachian status, and the interaction between these
two effects. In these models, the interaction was the primary effect of interest, testing whether the relationshipbetween the protein expression and the outcome significantly differed for the Appalachian and non-Appalachianpatients. All models where the interaction was significant
were further examined to determine the nature of therelationships involved. Due to its positively skewed distribution, discharge NIHSS scores were log-transformed tobetter meet model assumptions. Regression diagnostics
were used to evaluate model assumptions. A generalized
linear model was used to determine if the relationshipbetween protein expression and post-stroke disability (asmeasured by the Modified Rankin Scale) scores differedby Appalachian status. Specifically, since the mRS is an
ordinal variable, ordinal logistic regression was used, andrelationships were quantified with odds ratios. Age, sex,
BMI, admission NIHSS score, infarct volume, and timefrom last known normal were entered as covariates in allmodels to reduce the potential for confounding. Results
were combined from the multiple imputation data sets(see below) using Rubin’s rules. [18] Due to the highly
exploratory nature of this research, the decision wasmade to set α at 0.05 for all analyses, despite the risk ofType I errors.
Missing data and multiple imputation models

Follow-up data were not available for all participants.
Missing data for the outcomes ranged from a low of 4.9%
for infarct/edema volumes to a high of 53.1% for discharge MoCA. Of the 81 patients, 50 (61.7%) were missing some data on primary study outcomes. Individuals
from Appalachia had a similar amount of missing datacompared to those from non-Appalachia areas (67.2%
and 47.8%, respectively. p = 0.105). Individuals with any
missing data were older (mean 69.1 vs. 61.1, p = 0.016),
had higher admission NIHSS scores (mean 19.3 vs. 11.4,
p < 0.001), and had a longer median time from last known
normal (721.5 vs. 489.0, p = 0.028). There were no other
differences based on variables measured in Table 1.
Missing data was minimal for demographic and clinicalcharacteristics.
Multiple imputation was used to impute missing
data by chained equations (i.e., multiple imputation bychained equations (MICE)). [19] Predictive mean matching was used for continuous missing data and logistic
regression was used to impute the ordered categoricalmRS values. Imputation models included demographics(age, sex, Appalachian status), clinical measures (A1c,thyroid stimulating hormone, glucose, HDL, LDL, totalcholesterol and triglycerides), comorbidities/medical history (BMI, hypertension, hyperlipidemia, type 2 diabetes,
previous stroke, and atrial fibrillation), proteomic expression, and dependent variables. Because the number of

McLouth et al. Journal of Neuroinflammation(2024) 21:205

Table 1 Demographic and clinical characteristics of Appalachianand non-Appalachian participants

Age in years, mean(SD)
Sex, n (%) Female
BMI, mean (SD)
Comorbidities,n (%)
Hypertension
Diabetes Mellitus II
Hyperlipidemia
Previous Stroke
Afib
Smoking Status
Never
Current
Previously (> 6months)Unknown
Admission NIHSS,
mean (SD)
Admission NIHSS
Categories
Minor Stroke (1–4)
Moderate Stroke(5–15)
Moderate/Severe(16–20)
Severe Stroke(≥ 21)TICI Score
2 A = < 50%
Perfusion
2B = > 50%
Perfusion
3 = Full Perfusion
Last Known Normal to Thrombectomy in minutes,
median (IQR)
CTA Collateral
Score, n (%)
0
1
2
3
NA
Infarct Volume
mm3, median[IQR]4
Edema Volume
mm3, median[IQR]4Death

Non-AppalachianAppalachiann = 2362.6 (17.2)

n = 5867.4 (13.4)

0.188

11 (48%)29.2 (5.2)

32 (55%)27.7 (5.8)

0.625
0.275

18 (78%)10 (43%)8 (35%)
2 (9%)
11 (48%)44 (76%)15 (26%)
12 (21%)9 (16%)
21 (36%)> 0.999
0.181
0.253
0.72
0.45
0.278

12 (52%)6 (26%)
0 (0%)

25 (43%)16 (28%)8 (14%)

5 (22%)
15.4 (7.8)

9 (16%)
16.6 (6.7)

3 (5%)
18 (31%)4 (17%)

23 (40%)5 (22%)

14 (24%)1 (4%)

4 (6%)

8 (35%)

27 (47%)14 (61%)408 (253–878)

27 (47%)720 (403–949.5)

Table 2 Unadjusted outcomes of Appalachian and nonAppalachian participants

p
value

0.486
0.074

0 (0%)
14 (61%)Page 4 of 10

0.505

Non-Appalachiann = 23
Discharge NIHSS,
2 [1–9]median [IQR]
Discharge NIHSS Categories, n (%)
Minor Stroke (1–4)
13 (56.5%)
Moderate Stroke
5 (21.7%)(5–15)
Moderate/Severe
3 (13.0%)(16–20)
Severe Stroke (≥ 21) 2 (8.7%)
mRS, n (%)
0–2
11 (47.8%)
3–4
7 (30.4%)5+5 (21.7%)
Discharge MoCA,
21.5 (6.5)
mean (SD)

Appalachiann = 58
11 [3–18]

p
value
0.028
0.046

18 (31.0%)
23 (39.7%)
6 (10.3%)
11 (19.0%)
0.052
14 (24.1%)
19 (32.8%)
25 (43.1%)
18.4 (5.8)

0.319

measured proteins (182) was greater than the number ofobservations (82), a principal components analysis was
first performed so the regression models would have aunique solution. The first three components, accounting
for 38% of the variability in protein expression, were usedin place of individual proteins for imputation models.
Additionally, the imputation models were congenial withthe analysis model and specifically included the interaction between Appalachian status and the proteomic principal components. A total of 100 data sets were multiply
imputed.

Results
Demographic and unadjusted outcome data in
Appalachian vs. non-Appalachian groups
0.042

0.710
5 (22%)
13 (56%)2 (9%)
1 (4%)
2 (9%)
12,145[3,815 − 40,300]

11 (19%)30 (51%)5 (9%)
5 (9%)
7 (12%)
29,030[11,080–126,900]

14,560[1,501 − 64,370]

32,270[11,560 − 137,900]

0.050

4 (17.4%)

20 (34.5%)

0.179

0.051

Table 1 presents the demographic and baseline clinicalcharacteristics of the cohort of subjects treated with MTand enrolled in BACTRAC. There were n = 58 (71.6%)
Appalachian and n = 23 (28.4%) non-Appalachian subjects. The groups were similar with regard to demographic, comorbidity, and admission stroke severity.
However, the Appalachian patients had a significantly
longer time between the onset of symptoms and presentation for treatment (median difference = 312 min,p = 0.042). Edema volume was significantly increased in
the Appalachian relative to the non-Appalachian group,
while infarct volume was nearly significantly increased at
p = 0.051.
Table 2 presents the unadjusted outcomes for the
Appalachian and non-Appalachian patients. Appalachianpatients had significantly higher median discharge NIHSS
scores (p = 0.028), indicating worse neurologic status, andwere more likely to fall in the more severe stroke categories (p = 0.046). Additionally, Appalachian patients had

McLouth et al. Journal of Neuroinflammation(2024) 21:205

lower MoCA scores at discharge, but the difference was
not significant (p = 0.319). Appalachian patients also displayed worse but non-statistically significant functional
status based on discharge mRS scores (p = 0.052).
Differential protein expression between Appalachian and
non-Appalachian subjects

The expression of leukocyte immunoglobulin-like receptor B5 (LILRB5) and Neurotrophin-3 (NT-3) was significantly different between those from Appalachia andnon-Appalachia at the α = 0.01 level. Both LILRB5 (difference (SE) = 0.53 (0.16), p = 0.002) and NT-3 (difference(SE) = 0.37 (0.11), p = 0.002) had higher expression levels
in non-Appalachian subjects compared to Appalachiansubjects.
Proteins associated with discharge NIHSS based on
Appalachian or non-Appalachian locale

Table 3 displays the regression coefficients associating discharge NIHSS scores with protein expression by
Appalachian status, and the test of whether these regression coefficients were significant (i.e., the interaction).
The relationship between seven proteins and discharge
NIHSS scores were significantly different for the Appalachian and non-Appalachian subjects. These proteins
include chemokine ligand 9 (CXCL9), chemokine ligand
10 (CXCL10), thrombospondin-4 (THBS4), interleukin 15 receptor subunit alpha (IL15RA), interleukin 13(IL13), interleukin 6 (IL6), and serine protease 2 (PRSS2).
In the non-Appalachian population, there was a positive relationship between CXCL9, CXCL10, IL15RA,
IL13, and IL6 and discharge NIHSS scores, implying thathigher expression of these proteins were associated with
Table 3 Regression analysis Predicting NIHSS scores from
Inflammatory proteins

0.325

Interaction
p-value
0.021

0.006*
0.946

0.022
0.027

0.544
0.300

0.027
0.028

0.268

0.028

0.581

0.031

Protein

Non-Appalachianb (SE)
p-value

Appalachianb (SE)
p-value

CXCL9

0.37 (0.18)

0.041*THBS4
CXCL10-0.31 (0.28)
0.36 (0.15)

0.267
0.015*

IL15RA
IL13

1.06 (0.38)
0.35 (0.15)

0.005*
0.022*

PRSS2

0.28 (0.15)

0.061

IL6

0.43 (0.18)

0.018*-0.08(0.08)
0.43 (0.16)-0.01(0.08)
0.12 (0.20)-0.20(0.19)-0.14(0.13)
0.03 (0.06)

Note. Values represent unstandardized regression coefficients (standard error)
and the associated p-value for the relationship between the protein and logtransformed NIHSS Scores. The interaction tests if the relationship between theprotein and NIHSS scores differs by Appalachian status
All interactions are significant. The significance of an individual regression line
is denoted with *Page 5 of 10

a higher stroke severity. Within the Appalachian subjects,
THBS4 was positively associated with discharge NIHSS
scores; higher THBS4 expression within these subjects
was associated with worse stroke severity. For PRSS2,
the individual regression coefficients were not significant
by Appalachian status, but the difference between these
regression coefficients was significant. Graphs of these
relationships by Appalachian status can be seen in Fig. 1.
Proteins associated with MoCA at discharge based on
appalachian or non-appalachian locale

The relationships between two proteins and MoCA
scores differed across the two populations as determinedby interaction p-values. These proteins include leukemia
inhibitory factor receptor (LIFR) and angiogenin (ANG).
Figure 2 illustrates the significant linear relationships
described below. Only in non-Appalachian subjects, both
LIFR and ANG were negatively associated with MoCA
scores, implying higher protein expression were associated lower levels of post-stroke cognitive impairment.
Proteins associated with mRS based on Appalachian or
non-Appalachian locale

Three proteins had a significantly different relationshipwith mRS scores based on Appalachian status (Table 4).
These include IL13, protein tyrosine phosphatase sigma(PTPRS), and chemokine ligand 25 (CCL25). In subjects from the non-Appalachia regions, IL13, PTPRS,
and CCL25 expression were significantly associatedwith post-stroke mRS scores. Within those subjects,
higher IL13 and CCL25 expression was associated withan increased likelihood of having a poorer outcome,whereas higher expression of PTPRS was associatedwith an increased likelihood of having a more favorable
outcome. PTPRS and IL13 were associated with poststroke mRS scores in the Appalachian population, where
higher PTPRS expression was associated with an increase
likelihood of having a poorer outcome and higher IL13
expression was associated with an increased likelihood of
having a more favorable outcome.

Discussion
The present study investigated the association of proteomic expression and post-stroke cognitive and functional recovery that differ between subjects from
Appalachian and non-Appalachian regions. Our findings shed light on potential differences in the molecularmechanisms underlying stroke recovery in these populations, offering insights into personalized therapeuticstrategies and highlights the importance of addressing
health disparities in stroke care.
Consistent with previous reports, our study confirms the existence of demographic and clinical disparities between Appalachian and non-Appalachian stroke

McLouth et al. Journal of Neuroinflammation(2024) 21:205

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Fig. 1 Proteins with differential correlation with NIHSS based on Appalachia or non-Appalachia residency. X-axis units are Normalized Protein eXpression,a relative quantification on the log2 scalepatients. However, the difference in stroke risk factors was not as pronounced as previous research suggests [20], likely influenced by the non-random sample
involved in this study. Appalachian subjects exhibiteddelayed presentation for treatment compared to their
non-Appalachian counterparts, emphasizing the need fortargeted interventions to improve stroke awareness andaccess to care in rural communities. Additionally, Appalachian subjects presented with more severe strokes, as
indicated by higher discharge NIHSS scores and on average worse functional and cognitive outcomes compared
to non-Appalachian subjects. These findings underscorethe urgent need for tailored rehabilitation programs andsupport services to address the unique challenges facedby Appalachian stroke survivors.
Proteomic analysis revealed differential expression of
specific plasma proteins between Appalachian and nonAppalachian stroke patients, providing valuable insights
into the molecular pathways involved in stroke recovery.
These findings underscore the importance of considering
regional disparities in biomarker discovery, and further
investigation into the functional roles of these proteins
may uncover novel therapeutic targets for enhancing
post-stroke recovery outcomes.
A total of seven proteins were significantly different
between Appalachian and non-Appalachian subjects

based on NIHSS on discharge. The proteins IL15RA,
CXCL9, CXCL10, IL-13 and IL-6 were found to be correlated with NIHSS only in the non-Appalachian cohort,suggesting that these inflammatory proteins are associated with increased stroke severity in this specific group.
Expression of IL-6 has been reported extensively to be
associated with poor outcomes after stroke [21, 22]. BothCXCL9 and 10 are elevated after stroke [23] and similar
to our findings, CXCL9 has been reported to be correlated with stroke severity. [24] IL-15 has been reported
to exacerbate injury in ischemic stroke [25] and blocking
IL-15 reduces injury [26] implicating the activation of
IL15-RA is deleterious during stroke. THBS4 was correlated with NIHSS only in Appalachian patients, andhigher levels were associated with poor outcomes. While
THBS4 has been shown to have benefit in rodent models of stroke, its expression increases in cardiovascularpathologies, such as atherogenesis [27, 28], heart hypertrophy [29] and ischemic injury to the heart [30].
Two proteins, LIFR and ANG, significantly differedwith MoCA scores at discharge between the two cohorts.
Both proteins were negatively related to MoCA scores in
non-Appalachian subjects indicating that their increased
expression is correlated with impaired cognition. Thisis surprising for LIFR since its ligand, LIF, has neuroprotective effects after experimental stroke resulting

McLouth et al. Journal of Neuroinflammation(2024) 21:205

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Fig. 2 Proteins with differential correlation with MoCA based on Appalachia or non-Appalachia residency. X-axis units are Normalized Protein eXpression,a relative quantification on the log2 scale

Table 4 Odds ratios for the relationship between protein
expression and modified Rankin scales by Appalachian Status
Protein
IL13
PTPRS
CCL25

Non-Appalachia OR
3.03 (1.51–6.80)
0.20 (0.04–0.95)
3.56 (1.01–14.51)

Appalachia OR
0.44 (0.18–0.99)
1.86 (1.08–3.35)
0.52 (0.24–1.02)

Interaction p value
0.003
0.031
0.039

Note. Values represent odds ratios (OR) and 95% confidence intervals and areinterpreted as a 1 standard deviation change in the log2 protein concentration,using the following standard deviations:
IL13 = 0.71
PTPRS = 0.27
CCL25 = 0.63

in improved function [31, 32]. Moreover, ANG has also
been reported to be a beneficial factor with increased
neurogenesis after stroke, [33] and as a biomarker for
improvement during stroke rehabilitation [34]. One
potential explanation of this issue is that in our current
study, blood samples are collected early during the stroke.
This increased expression of these proteins is ill-timed tobe effective in reducing functional deficits.
Three proteins had a significantly different association with mRS due to subject location. For non-Appalachian subjects, IL-13 and CCL25 were positively related
to mRS, suggesting high expression is associated withpoor recovery. For the Appalachian group, higher levelsof PTPRS were associated with poor stroke functional

recovery and interestingly, higher levels of IL-13 wasassociated with improved recovery. In a previous study,
increased expression of CCL25 was associated withimproved clinical outcomes. [35] IL-13 has been implicated to promote long-term recovery after ischemic
stroke. [36] PTPRS has not been studied in the context
of stroke to our knowledge. Incongruities exist between
our findings and the literature which may be explained by
the fact that our study is focused on one type of stroke,
ELVO, and our patient population has a differential systemic proteomic response to ELVO relative to patientsoutside of this area.
Out of the 182 proteins, 11 were discovered to be
expressed significantly different based on the localityof the patient. This disparity in expression could be dueto the fact that either the basal expression of these proteins differs or the proteomic response to stroke differs.
To explore this further, future research should delve into
examining the basal expression levels of these proteins ina suitable control sample but who did not experience a
stroke. We intend to conduct this analysis as part of an
ongoing investigation. In the absence of basal differences,
this response could be influenced by genetic, environmental, and/or socioeconomic factors and needs to beelucidated in future studies.

McLouth et al. Journal of Neuroinflammation(2024) 21:205

Based on the literature, these proteins shared functions in three areas: angiogenesis, autoimmunity and T
cell regulation. There were 10 proteins which have beenreported to be involved in the process of angiogenesis[37–46], with nine associated with autoimmunity [47–56]
and eight with roles in T cell development and activation[47, 57–63]. This finding is congruous with our previous
report depicting T helper 2 (Th2) response occurs during
the initial phase in response to ELVO and is associatedwith edema and infarct volume. [13] The Th2 lymphocytes mediate autoimmune inflammation [64, 65] andproduce cytokines that can lead to edema [66]. The Th2
response is proangiogenic and has been shown to induce
the expression of proangiogenic factors, like VEGF [67].
While only IL-13 is considered a Th2-linked cytokine,
these other proteins share similar functions linking themto T cell activity, autoimmunity and angiogenesis.
Limitations of our study include the relatively modest sample size and the use of multiple imputation techniques to address missing data, which may introduce bias
or imprecision in our findings, particularly if data are notmissing at random, an unfortunately untestable assumption. Notably, despite performing hundreds of statisticaltests, we made no correction for multiple comparisons.
This was done intentionally. Not only was this an exploratory study, but we were mainly interested in testing forstatistical interactions. In comparison to main effects,
interactions are notoriously underpowered in typicalapplied research. As a result, the decision was made to
run the risk of committing Type I errors (i.e., false positives) rather than prematurely discard significant findings. In the future, it is important to replicate thesefindings in an independent sample. Another limitation isthat only the MoCA test is used for cognitive assessment.
It is the only clinical tool validated for frequent monitoring with the short time from stroke until first assessment.
From it, the mini-MoCA can be derived, which is moretolerable for those unable to engage in more in-depth
testing, but the limitation for aphasic patients is amplified using that short form. We found the use of the full
MoCA to be a balance between maintaining the nonmemory aspects of a broader cognitive test and not overwhelming the more limited patients, all while keeping aclinical lingua franca as an outcome. Furthermore, thegeneralizability of our results may be limited to the study
population and requires validation in larger cohorts fromdiverse geographical regions. Despite these limitations,
our study provides valuable insights into the proteomic
associations with post-stroke recovery in Appalachianand non-Appalachian populations, laying the foundation
for future research aimed at reducing health disparities
and improving stroke care for all patients.
The observed differences in proteomic associations with stroke recovery between Appalachian andPage 8 of 10

non-Appalachian subjects underscore the complex interplay shaping post-stroke outcomes. Future studies should
explore the functional significance of identified proteins
and their interactions within specific biological pathways
to elucidate the underlying mechanisms driving regional
disparities in stroke recovery. Additionally, efforts todevelop personalized therapeutic interventions tailoredto the unique molecular profiles of Appalachian and nonAppalachian stroke patients are warranted to optimize
post-stroke care and improve long-term outcomes inunderserved populations.
Author contributions
Authors CM, BM, JFF, KRP contributed to the development and planningfor this study. Authors CM, BM, JAF, HH, JH, WC, SP, LS, DD, AT, AS, JFF, KRP
contributed to obtaining or analyzing subject data. Additionally, authors CM,
BM, JAF, HH, JH, WC, SP, LS, DD, AT, AS, JFF, KRP helped craft and edit the final
manuscript. All authors read and agreed to submission of the final manuscript.
Funding
The current study was supported by the National Institute of Neurologic
Disorders and Stroke (NINDS) through grant number 1R01NS127974-01A1 andthe National Institute of Environmental Health Sciences (NIEHS) through grant
number P30-ES026529.
Data availability
Data are available upon request to the Corresponding Author, Keith R.
Pennypacker.

Declarations
Ethics approval and consent to participate
The current study involves human subjects and was approved by the
University of Kentucky Institutional Review Board (IRB) ID 48831. All
included subjects were consented within the IRB-outlined 72-hour window.
This is single-cohort, longitudinal, observational study of large vessel
occlusion stroke patients treated under standard of care with mechanicalthrombectomy.
Competing interests
Authors KRP, AMS, and JFF are co-founders/equity holders in Cerelux, LLC. No
others to report for any other coauthor.
Author details
1
Department of Biostatistics, University of Kentucky, Lexington, KY, USA
2
Department of Neurology, University of Kentucky, Lexington, KY, USA
3
Department of Neurology, Beth Israel Deaconess Medical Center, Boston,
MA, USA
4
Department of Neurosurgery, University of Kentucky, Lexington, KY, USA
5
Department of Radiology, University of Kentucky, Lexington, KY, USA
6
Department of Neuroscience, University of Kentucky, Lexington, KY, USA
7
Center for Advanced Translational Stroke Science, Department of
Neurology and Neuroscience Building BBSRB Office B463, University of
Kentucky, Lexington, KY 40536, USA
8
College of Medicine, University of Kentucky, Lexington, KY, USA
Received: 30 May 2024 / Accepted: 9 August 2024

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