An inflammation-derived and clinical-based model for ischemic stroke recovery
medrxiv(2024)
摘要
Background Neuroinflammatory responses reflecting disease progression are believed to be closely associated with the severity of prognosis in post-stroke.
Purpose This study developed a combined predicted model of inflammation-derived biomarkers and clinical-based indicators using machine learning algorithms for differentiation of the functional outcome in patients with subacute ischemic stroke.
Methods Clinical blood samples and patient data from individuals with subacute ischemic stroke were collected at admission. Based on activities of daily living assessments followed by a 3-month recovery, patients were categorized into two groups: those with little effective recovery (LE) and those with obvious effective recovery (OE). Serum samples underwent proteomic testing for initial candidates. Subsequently, multidimensional validation of candidates in models of ischemia-reperfusion at protein and mRNA levels was performed. T -test, Receiver Operating Characteristic (ROC), and LASSO analysis in an additional cohort were performed to confirm the clinical variables and candidate biomarkers in the discriminatory sensitivity and specificity between the LE and OE groups. Finally, models were developed based on candidates in the training dataset and predicted stroke recovery outcomes in another new dataset using ten standard two-categorical variable algorithms in machine learning.
Results We identified higher tissue inhibitor metalloproteinase-1 (TIMP1) and LGALS3 levels were positively correlated with the severity of prognosis after ischemic stroke rehabilitation. TIMP1 (AUC=0.904, 0.873) and LGALS3 (AUC=0.995, 0.794) were confirmed to address superior sensitivity and specificity in distinguishing ischemic stroke from healthy control and LE group from OE group. The TIMP1 and Lgals3 expression exhibited an evident increase in microglia following ischemia-reperfusion. In addition, inflammation-derived biomarkers (TIMP1, LGALS3) coupled with clinical-based indicators (HGB, LDL-c, UA) were built in a combined model with random forest to differentiate OE from LE in 3-month follow-up with high accuracy (AUC = 0.8).
Conclusion Our findings provided evidence supporting the critical prognostic potential and risk prediction of inflammation-derived biomarkers after ischemic stroke rehabilitation in complementary to current clinical-based parameters.
### Competing Interest Statement
The authors have declared no competing interest.
### Clinical Trial
The study was approved by the local ethical committee of The First Affiliated Hospital of Shenzhen University (No.20211025002-FS01) and carried out by the Declaration of Helsinki. Medical history, physical examination, and biochemical data were obtained at the enrollment.
### Funding Statement
This study was supported by funds from the Natural Science Funding of China (No.82272598 to Jiao Luo, No.81901470 to Jiao Luo), the Science, Technology and Innovation Commission of Shenzhen (JCYJ20210324135804012 to Jiao Luo?JCYJ20220530150407015 to Yulong Wang), Natural Science Foundation of Guangdong Province, China (No. 2020A1515011203), the Postdoctoral Science Foundation of China (No. 2019M663100).
### Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
The study was approved by the local ethical committee of The First Affiliated Hospital of Shenzhen University (No.20211025002-FS01) and carried out by the Declaration of Helsinki. Medical history, physical examination, and biochemical data were obtained at the enrollment.
I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes
I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
Yes
There is no conflict of interest for all authors who share data and materials in this article.
* ### Abbreviations
IS
: ischemic stroke
LE
: little effective recovery
OE
: obvious effective recovery
HC
: healthy control
ADL
: activities of daily living
TIMP1
: Tissue inhibitor of metalloproteinase 1
LGALS3
: Galectin-3
MMPs
: Matrix metalloproteinases
HGB
: hemoglobin
LDL-c
: low-density lipoprotein cholesterol
UA
: uric acid
ROC
: receiver operating characteristic
AUC
: area under curve
NVU
: neurovascular unit
ROS
: reactive oxygen species
NO
: nitric oxide
TNF-α
: tumor necrosis factor α
MCAO
: middle cerebral artery occlusion
LC-MS/MS
: liquid chromatography-tandem mass spectrometry analysis
UHPLC
: ultra high-performance liquid chromatography
PASEF
: parallel accumulation serial fragmentation
FPR
: false-positive rate
DEPs
: differentially expressed proteins
PCA
: principal component analysis
DEGs
: differentially expressed genes
GO
: gene ontology
TGFB1
: Transforming Growth Factor beta 1
PTPRC
: Protein Tyrosine Phosphatase Receptor Type C
VIM
: Vimentin
MYH9
: Myosin Heavy Chain 9
CSF1R
: Colony stimulating factor 1 receptor
GSN
: Gelsolin
TTC
: triphenyl tetrazolium chloride
OGD/R
: oxygen-glucose deprivation/ reoxygenation
PVN
: paraventricular nucleus of hypothalamus
ECM
: extracellular matrix
BBB
: blood-brain barrier
PASEF
: parallel cumulative serial fragmentation
DEPs
: differentially expressed proteins
DEGs
: differentially expressed genes
GO
: Gene Ontology
ALB
: Albmin
svm
: Support Vector Machine
rf
: Random Forest
nnet
: Artificial Neural and Network
nb
: naive bayesian classifier
kknn
: K-Nearest Neighbor
glmnet
: Logistic Regression
C5.0
SEM
: standard error of the mean.
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