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The differential proteomic response to ischemic stroke in appalachian subjects treated with mechanical thrombectomy

Journal of Neuroinflammation volume 21, Article number: 205 (2024) Cite this article

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 mechanical thrombectomy (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 were identified 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 for seven 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-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 community-based 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 great interest regarding access to care, health disparities, and health outcomes. Appalachia has a high proportion of rural, Caucasian, socioeconomically and medically underprivileged individuals [1]. As reported by the Appalachian Regional Commission in 2017, the stroke mortality rate in Appalachia is higher than in the rest of the United States [2]. This report also includes data that individuals from Appalachia are more likely to have stroke-related comorbidities including diabetes, obesity, and increased tobacco usage.

There are approximately 800,000 strokes in the US each year, of which 87% are ischemic [3]. Within the ischemic stroke subset, approximately 30–40% are emergent large vessel occlusions (ELVOs), which often represent the most debilitating cerebrovascular blockages. [4] When subjects 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 clinical outcomes 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 and registry, 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, and the 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. Our group has used these methods to link arterial blood gasses, electrolyte chemistry, transcriptomics, proteomics, short-chain fatty acids, and neuroinflammatory cells to clinical outcomes in stroke patients undergoing thrombectomy [6,7,8,9,10,11,12,13]. 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 that are differentially expressed between Appalachian and non-Appalachian stroke subjects undergoing MT, and that are uniquely associated with stroke outcomes based on 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 critically important and underserved population.

Methods

Patient categorization

A total of 81 subjects were included in this study, with 58 (71.6%) subjects from Appalachian areas and 23 (28.4%) subjects from non-Appalachian areas. 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 be viewed 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 human biospecimens 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. Clinical data 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, and both 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 then aliquoted into BD Microtainer tubes with K2E (K2EDTA; Becton, Dickinson and Company), spun down at 2000 revolutions for 15 min, and plasma was extracted and 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 to Olink Proteomics (Olink Proteomics, Boston, MA) for analysis of plasma protein.

Clinical data acquisition

The National Institutes of Health Stroke Scale (NIHSS) is a standardized assessment tool used to evaluate the severity of stroke by assessing various neurological functions (motor strength, eye movements, sensation, speech, etc.). [15] This score is documented for each subject at time of presentation as well as at time of discharge. The modified Rankin Scale (mRS) is another standardized tool which assesses functional disability or dependence in patients who have experienced a neurologic injury, such as stroke. [16] It is a 6-point scale that ranges from 0 (no symptoms) to 6 (severe disability or death). For analytic purposes, this metric was recategorized as 0–2, 3–4, and 5 + 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, and executive function. Some subjects were administered an abbreviated version of the MoCA, so those scores were re-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 and Target 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 when running statistics across sample sets. As of 2023, Olink had been included in over 1200 publications (https://www.olink.com/). For statistical analyses, between-group comparisons 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, to test for differences in proteomic expression based on location, a series of independent samples t-tests were conducted comparing protein expression between the Appalachian and non-Appalachian samples. For the main study hypotheses, a series of general linear models were used 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 the main effect of a protein, a dummy variable representing Appalachian status, and the interaction between these two effects. In these models, the interaction was the primary effect of interest, testing whether the relationship between the protein expression and the outcome significantly differed for the Appalachian and non-Appalachian patients. All models where the interaction was significant were further examined to determine the nature of the relationships involved. Due to its positively skewed distribution, discharge NIHSS scores were log-transformed to better meet model assumptions. Regression diagnostics were used to evaluate model assumptions. A generalized linear model was used to determine if the relationship between protein expression and post-stroke disability (as measured by the Modified Rankin Scale) scores differed by Appalachian status. Specifically, since the mRS is an ordinal variable, ordinal logistic regression was used, and relationships were quantified with odds ratios. Age, sex, BMI, admission NIHSS score, infarct volume, and time from last known normal were entered as covariates in all models 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 was made to set α at 0.05 for all analyses, despite the risk of Type 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 data compared 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 clinical characteristics.

Multiple imputation was used to impute missing data by chained equations (i.e., multiple imputation by chained equations (MICE)). [19] Predictive mean matching was used for continuous missing data and logistic regression was used to impute the ordered categorical mRS values. Imputation models included demographics (age, sex, Appalachian status), clinical measures (A1c, thyroid stimulating hormone, glucose, HDL, LDL, total cholesterol 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 measured proteins (182) was greater than the number of observations (82), a principal components analysis was first performed so the regression models would have a unique solution. The first three components, accounting for 38% of the variability in protein expression, were used in place of individual proteins for imputation models. Additionally, the imputation models were congenial with the 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

Table 1 presents the demographic and baseline clinical characteristics of the cohort of subjects treated with MT and 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 1 Demographic and clinical characteristics of Appalachian and non-Appalachian participants
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Table 2 presents the unadjusted outcomes for the Appalachian and non-Appalachian patients. Appalachian patients had significantly higher median discharge NIHSS scores (p = 0.028), indicating worse neurologic status, and were more likely to fall in the more severe stroke categories (p = 0.046). Additionally, Appalachian patients had 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).

Table 2 Unadjusted outcomes of Appalachian and non-Appalachian participants
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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 and non-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 Appalachian subjects.

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 that higher expression of these proteins were associated with 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.

Table 3 Regression analysis Predicting NIHSS scores from Inflammatory proteins
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Fig. 1
figure 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 scale

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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 determined by 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.

Fig. 2
figure 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

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Proteins associated with mRS based on Appalachian or non-Appalachian locale

Three proteins had a significantly different relationship with 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 associated with post-stroke mRS scores. Within those subjects, higher IL13 and CCL25 expression was associated with an increased likelihood of having a poorer outcome, whereas higher expression of PTPRS was associated with an increased likelihood of having a more favorable outcome. PTPRS and IL13 were associated with post-stroke 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.

Table 4 Odds ratios for the relationship between protein expression and modified Rankin scales by Appalachian Status
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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 molecular mechanisms underlying stroke recovery in these populations, offering insights into personalized therapeutic strategies 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 patients. 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 exhibited delayed presentation for treatment compared to their non-Appalachian counterparts, emphasizing the need for targeted interventions to improve stroke awareness and access 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 underscore the urgent need for tailored rehabilitation programs and support services to address the unique challenges faced by Appalachian stroke survivors.

Proteomic analysis revealed differential expression of specific plasma proteins between Appalachian and non-Appalachian 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]. Both CXCL9 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, and higher levels were associated with poor outcomes. While THBS4 has been shown to have benefit in rodent models of stroke, its expression increases in cardiovascular pathologies, such as atherogenesis [27, 28], heart hypertrophy [29] and ischemic injury to the heart [30].

Two proteins, LIFR and ANG, significantly differed with 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. This is surprising for LIFR since its ligand, LIF, has neuroprotective effects after experimental stroke resulting 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 to be 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 with poor recovery. For the Appalachian group, higher levels of PTPRS were associated with poor stroke functional recovery and interestingly, higher levels of IL-13 was associated with improved recovery. In a previous study, increased expression of CCL25 was associated with improved 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 patients outside of this area.

Out of the 182 proteins, 11 were discovered to be expressed significantly different based on the locality of the patient. This disparity in expression could be due to 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 in a 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 be elucidated in future studies.

Based on the literature, these proteins shared functions in three areas: angiogenesis, autoimmunity and T cell regulation. There were 10 proteins which have been reported to be involved in the process of angiogenesis [37,38,39,40,41,42,43,44,45,46], with nine associated with autoimmunity [47,48,49,50,51,52,53,54,55,56] and eight with roles in T cell development and activation [47, 57,58,59,60,61,62,63]. 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 associated with edema and infarct volume. [13] The Th2 lymphocytes mediate autoimmune inflammation [64, 65] and produce 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 them to 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 not missing at random, an unfortunately untestable assumption. Notably, despite performing hundreds of statistical tests, 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 for statistical interactions. In comparison to main effects, interactions are notoriously underpowered in typical applied 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 these findings in an independent sample. Another limitation is that 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 more tolerable 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 non-memory aspects of a broader cognitive test and not overwhelming the more limited patients, all while keeping a clinical lingua franca as an outcome. Furthermore, the generalizability of our results may be limited to the study population and requires validation in larger cohorts from diverse geographical regions. Despite these limitations, our study provides valuable insights into the proteomic associations with post-stroke recovery in Appalachian and 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 and 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 to develop personalized therapeutic interventions tailored to the unique molecular profiles of Appalachian and non-Appalachian stroke patients are warranted to optimize post-stroke care and improve long-term outcomes in underserved populations.

Data availability

Data are available upon request to the Corresponding Author, Keith R. Pennypacker.

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Funding

The current study was supported by the National Institute of Neurologic Disorders and Stroke (NINDS) through grant number 1R01NS127974-01A1 and the National Institute of Environmental Health Sciences (NIEHS) through grant number P30-ES026529.

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Authors and Affiliations

  1. Department of Biostatistics, University of Kentucky, Lexington, KY, USA

    Christopher J. McLouth & Will Cranford

  2. Department of Neurology, University of Kentucky, Lexington, KY, USA

    Christopher J. McLouth, Jordan P. Harp, Lila Sheikhi, Ann M. Stowe, Justin F. Fraser & Keith R. Pennypacker

  3. Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA

    Benton Maglinger

  4. Department of Neurosurgery, University of Kentucky, Lexington, KY, USA

    Jacqueline A. Frank, Shivani Pahwa, Lila Sheikhi, David Dornbos III, Amanda L. Trout & Justin F. Fraser

  5. Department of Radiology, University of Kentucky, Lexington, KY, USA

    Shivani Pahwa, Lila Sheikhi, David Dornbos III & Justin F. Fraser

  6. Department of Neuroscience, University of Kentucky, Lexington, KY, USA

    Shivani Pahwa, Lila Sheikhi, David Dornbos III, Ann M. Stowe & Keith R. Pennypacker

  7. Center for Advanced Translational Stroke Science, Department of Neurology and Neuroscience Building BBSRB Office B463, University of Kentucky, Lexington, KY, 40536, USA

    Jacqueline A. Frank, Jordan P. Harp, Amanda L. Trout, Ann M. Stowe, Justin F. Fraser & Keith R. Pennypacker

  8. College of Medicine, University of Kentucky, Lexington, KY, USA

    Hunter S. Hazelwood

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  1. Christopher J. McLouth
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Contributions

Authors CM, BM, JFF, KRP contributed to the development and planning for 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.

Corresponding author

Correspondence to Keith R. Pennypacker.

Ethics 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 mechanical thrombectomy.

Competing interests

Authors KRP, AMS, and JFF are co-founders/equity holders in Cerelux, LLC. No others to report for any other coauthor.

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McLouth, C.J., Maglinger, B., Frank, J.A. et al. The differential proteomic response to ischemic stroke in appalachian subjects treated with mechanical thrombectomy. J Neuroinflammation 21, 205 (2024). https://doi.org/10.1186/s12974-024-03201-9

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Journal of Neuroinflammation

ISSN: 1742-2094