Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke

Lower CALLY index levels indicate higher poor functional outcome risk in acute ischemic stroke patients treated with endovascular thrombectomy

Background

The imbalance in the nutrition-immunity-inflammation status is linked to the prognosis of various diseases. This study sought to evaluate the correlation between the C-reactive protein-albumin-lymphocyte (CALLY) index and the outcomes of acute ischemic stroke (AIS) managed with endovascular thrombectomy (EVT).

Methods

This study retrospectively enrolled 473 AIS patients who underwent EVT from a multicenter investigation. Poor functional outcome was defined as a modified Rankin scale score exceeding 2 points at 90 days after EVT. The cutoff value for the CALLY index was determined using the receiver operating characteristic curve. Multivariable logistic regression models were utilized to explore the association between the CALLY index and poor functional outcome and restricted cubic splines was used to illustrate the relationship between the CALLY index and the risk of poor functional outcome after EVT.

Results

Poor functional outcomes occurred in 214 (45.2%) patients at 90 days after EVT. The cutoff for the CALLY index was 10^ (-0.635). Multivariate logistic regression revealed that the CALLY index was significantly associated with poor functional outcome (odds ratio [OR]: 0.80, 95% confidence interval [CI]: 0.70-0.91, p < 0.001; high versus low OR: 0.64, 95% CI: 0.41-1.00, p = 0.048). The restricted cubic spline analysis indicated an inverse association between the CALLY index and the risk of poor functional outcome (P for nonlinearity = 0.373).

Conclusion

Our study identified that a lower CALLY index is an independent predictor of poor functional outcome after EVT. The CALLY index could emerge as a practical, cost-effective, and promising predictive biomarker for adverse outcomes in AIS patients undergoing EVT treatment.

Association Between Naples Prognostic Score and All-Cause and Cardiovascular Mortality in Stroke Patients

OBJECTIVES

Stroke is a primary public health challenge worldwide, with its prognosis profoundly related to inflammation and nutritional status. Naples prognostic score (NPS) is an integrated indicator of combined inflammation and nutrition. The study aimed to elucidate the link between NPS and all-cause and cardiovascular (CVD) deaths in stroke patients.

METHODS

NHANES (2001 to 2018) data set was used. Multivariate corrected Cox models, Kaplan-Meier, and restricted cubic spline analysis were adopted to explore the link between NPS, all-cause, and CVD mortality in stroke patients. Subgroup analyses based on age, sex, BMI, education, alcohol consumption, smoking, hypertension, and diabetes were performed to further explore associations.

RESULTS

Totally, 1247 stroke patients were enrolled. High NPS levels were notably linked with a heightened risk of all-cause mortality (HR: 1.320, 95% CI: 1.180-1.470, P<0.001) and CVD death (HR: 1.390, 95% CI: 1.120-1.730, P=0.003) in stroke patients after adjusting for relevant factors. Compared with group 0 (NPS=0), group 2 (NPS=3-4) had a high hazard ratio for all-cause mortality (HR: 2.920, 95% CI: 1.820-4.670, P<0.001) and CVD mortality (HR: 2.610, 95% CI: 1.140-5.970, P=0.023). The RCS suggested a linear link between NPS and both all-cause (P for nonlinear=0.2202) and CVD deaths (P for nonlinear=0.9841) in stroke patients. These links were mostly consistent in subgroups. There was no pronounced interaction between status and NPS scores for each subgroup (all P<0.05).

CONCLUSIONS

High NPS scores are connected with an augmented risk of all-cause mortality and CVD death in stroke patients. NPS is possibly connected with prognosis in stroke patients.

Profiling X chromosome genes expression relevant to sex dimorphism in stroke: insights from transcriptomics landscape analysis

Introduction

Although age is the most important non-modifiable risk factor for cerebral stroke, it is also apparent that females commonly exhibit longer lifespan and better outcome after stroke compared to the age-matched males. A critical event after stroke is the peripheral infiltration of immune cells across damaged blood-brain barrier, which induces inflammatory and immune responses within the brain parenchyma and consequently worsen brain injury. These events are also dependent on age and display a sex different pattern. Theoretically, X chromosome-encoded differential expression genes (DEGs) may explain differences between the sexes. However, the expression and regulation of these DEGs after stroke have not been studied in detail.

Methods

We conducted three datasets of human blood cells, mice brain, mice microglia and T cells that were previously published, and analyzed the contribution of gender, age and stroke insult on the X chromosome-encoded DEGs.

Results

The main findings were (i) compared to age, the stroke/hypoxia was a more potent factor in eliciting the DEGs. Particularly, older stroke patients exhibited more changes compared to young stroke group. (ii) After a stroke, the DEGs was diversely influenced by sex, age and cell types being studied. Particularly, either aging or gender led to more striking changes in brain-infiltrating T cells than in the resident immune cells.

Discussion

These findings highlight the complex interplay between sex, age, and immune responses in mediating stroke incidence and outcome. Investigation of the identified X chromosome-encoded genes in brain-infiltrating T cells deserves high priority, as they may play more important roles in explaining gender-related differences in stroke and brain injury.

Molecular heterogeneity in human stroke - What can we learn from the peripheral blood transcriptome?

Stroke is a multifaceted disease with genetic and environmental components like diet and lifestyle. The central nervous and immune systems display complex interactions, with the peripheral immune response participating in brain injury and repair mechanisms following stroke. The bidirectional communication between the injured brain and peripheral blood presents an opportunity to investigate the molecular changes in the latter. There is substantial heterogeneity in stroke pathogenesis, pathophysiology, comorbidities, and response to treatment and outcome. This is captured and underscored by heterogeneity in the peripheral blood transcriptome. The current review highlights the role of the human peripheral blood transcriptome architecture for molecular phenotyping of different stroke etiologies and comorbidities, and for identifying underlying molecular correlates with clinically important variables and outcomes. Specific transcriptome features can potentially provide targets for clinical translation and for prioritizing genes and pathways for evaluation in experimental models. We also propose an approach to study the patient-specific transcriptional architecture and uncover the combinatorial heterogeneity in altered pathways in stroke patients that can also guide the search for treatment and prevention targets. Deciphering the molecular heterogeneity of stroke in a tissue that can be easily accessed and monitored, such as peripheral blood, may improve clinical trial success.

Investigation of the relationship between lead exposure in heavy metals mixtures and the prevalence of stroke: a cross-sectional study

BACKGROUND

The adverse effects of environmental toxic metal exposure on human health are well-documented. However, the specific influence of heavy metal exposure on stroke prevalence remains underexplored.

METHODS

This study utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning from 2011 to 2018 to investigate the association between blood metal concentrations and the incidence of stroke. Four analytical approaches-logistic regression, Restricted Cubic Splines (RCS), Weighted Quantile Sum regression (WQS), and Bayesian Kernel Machine Regression (BKMR)-were employed to assess the relationship, with a mediation analysis conducted to explore the role of inflammatory markers in Pb exposure-induced stroke.

RESULTS

Among the 9,399 participants in this project, 421 (4.4%) were diagnosed with stroke. After adjusting for covariates, a multivariable logistic regression model identified a positive association between the logarithmic concentration of Pb and the incidence of stroke. Besides, the analysis conducted using both WQS and BKMR methodologies found a consistent positive association between the composite exposure to heavy metals and the frequency of stroke cases, with Pb emerging as the predominant factor in this relationship. An evident saturation phenomenon was noted in the correlation between lead exposure and the risk of stroke. Additionally, the interplay between Pb exposure and stroke manifestation was found to be partially mediated by inflammatory markers, which were responsible for 6.9% of the observed effect (95%CI:0.01, 0.24, P = 0.03).

CONCLUSION

These findings indicate a notable contribution of Pb exposure to stroke risk, highlighting inflammation as a significant intermediary mechanism in the Pb exposure-stroke association.

Profiling immune cell-related gene features and immunoregulatory ceRNA in ischemic stroke

Molecules in immune cells plays a vital role in the pathogenesis of ischemic stroke (IS). The aim of this study is to profile the landscape of molecules on the basis of immune cells in IS peripheral blood and construct an immunoregulatory competing endogenous RNA (ceRNA) network. We collected and combined multiple public transcriptome datasets from the peripheral blood of IS patients and healthy controls. CIBERSORT deconvolution revealed that the proportions of CD8 and CD4 naive T cells, monocytes, and neutrophils changed significantly in the IS group. Intersecting the immune cell-related genes identified by weighted gene co-expression network analysis (WGCNA) and differential expression analysis, 38 overlapping candidate biomarkers were selected. Three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forest were applied, and 11 distinct immune cell-related genes were identified. We obtained the mRNA-miRNA and miRNA-lncRNA interactions from StarBase v3.0, and constructed a ceRNA network based on the differentially expressed mRNAs, miRNAs, and lncRNAs. The aberrant expression of HECW2-centered ceRNAs in the peripheral blood of in-house patients was validated using quantitative PCR. We also revealed that the expression of HECW2 was positively correlated with lncRNAs LINC02593 through miRNAs miR-130a-3p, miR-130b-3p and miR-148b-3p in cells. These results show that there are distinct immune features between IS patients and healthy controls. The ceRNA network may help elucidate the mechanism of immune cell-related genes in IS and may serve as a therapeutic target.

Salidroside facilitates neuroprotective effects in ischemic stroke by promoting axonal sprouting through promoting autophagy

BACKGROUND

Ischemic stroke is a common cerebrovascular disease characterized by high incidence, disability, mortality, and recurrence. The limitations of current pharmacological treatments, which have primarily single neuroprotective action and a narrow therapeutic time window, lead to unsatisfactory therapeutic efficacy. Activation of autophagy can facilitate neural regeneration.

OBJECTIVE

To clarify whether salidroside can promote axonal sprouting through autophagy resulting in protecting neurons.

METHODS

In vivo, a Middle Cerebral Artery Occlusion/reperfusion (MCAO/IR) model was used, and in vitro, an Oxygen-Glucose Deprivation/Reoxygenation (OGD/R)-induced primary neuronal cell model was employed to evaluate the neuroprotective effects of salidroside. BDA neurotracer, immunofluorescence, and Western blot (WB) were utilized to determine its impact on axonal sprouting and the levels of related proteins (MAP2, GAP43, and PSD-95). Proteomics, transmission electron microscopy (TEM), and WB were applied to identify the effects on autophagy-related proteins (beclin1, LC3, p62, and LAMP2), autophagosomes and lysosomes. The mechanism of salidroside in promoting axonal sprouting through inducing autophagy was further confirmed by blocking with the autophagy inhibitor 3-MA.

RESULTS

Salidroside reduced neurologic deficits and infarct volume induced by MCAO/IR in vivo and protected OGD/R induced primary neuronal cells in vitro. Both in vivo and in vitro, it increased the number and length of axons and upregulated the expression of key axonal proteins (MAP2, GAP43, and PSD-95) and mediated autophagy-related proteins. Mechanistic studies showed that the promoting effects of salidroside on autophagy and axonal sprouting disappeared after the blockade by 3-MA.

CONCLUSION

This study reports for the first time that the neuroprotective effect of salidroside in ischemic stroke can be executed through mediating autophagy-related protein (beclin1, LC3, p62, and LAMP2), resulting in induced axonal sprouting or mature protein (MAP2, GAP43, and PSD-95).

Impact of perinatal factors on T cells and transcriptomic changes in preterm infant brain injury

BACKGROUND

T cells have been implicated in various neurological conditions, yet their role in neonatal brain injuries remains unclear. This study aimed to investigate the impact of perinatal factors on frequencies of T cell subsets in preterm infants and to explore the differences in blood genome expression profiles between preterm infants with and without brain injury.

MATERIALS AND METHODS

Three cohorts of preterm infants were used. Blood samples were collected soon after birth for the first cohort and late timepoint for the second and third cohorts. In the first cohort (88 infants), flow cytometry measured the proportions of αβT and γδT cell subsets in peripheral blood, analyzing associations with gestational age, birth weight, sex, delivery type, and maternal conditions. The second cohort focused on the relationship between T cell subsets and brain injury. In the third cohort, transcriptome sequencing identified differentially expressed genes and pathways in infants with brain injury, highlighting immune-related changes.

RESULTS

Infants born at 29-30 weeks or with a birth weight of 1000-1500 g had significantly higher proportions of Vδ2+ T cells compared to those born at 30-32 weeks or with a birth weight > 1500 g, while no significant difference was found between infants born at < 29 weeks or with a birth weight < 1000 g. A negative correlation was observed between gestational age and Vδ2+ T cell frequency. No significant associations were found between Vδ2+ T cell proportions and perinatal factors other than gestational age or brain injury. Blood transcriptome analysis revealed 173 differentially expressed genes, characterized by downregulated interferon signaling and upregulated antimicrobial and neutrophil pathways in infants with brain injury.

CONCLUSIONS

Gestational age and birth weight influence Vδ2+ T cell proportions in preterm infants, likely reflecting immune maturation. While no direct link to brain injury was found, altered immune pathways suggest potential biomarkers for prognosis, warranting further research into their roles and therapeutic implications in neonatal brain injuries.

Integrating machine learning and single-cell transcriptomic analysis to identify potential biomarkers and analyze immune features of ischemic stroke

This study employs machine learning and single-cell transcriptome sequencing (scRNA-seq) analysis to unearth novel biomarkers and delineate the immune characteristics of ischemic stroke (IS), thereby contributing fresh insights into IS treatment strategies.Our research leverages gene expression data sourced from the GEO database. We undertake weighted gene co-expression network analysis (WGCNA) to filter pertinent genes and subsequently employ machine learning algorithms for the identification of feature genes. Concurrently, we rigorously execute quality control measures, dimensionality reduction techniques, and cell annotation on the scRNA-seq data to pinpoint differentially expressed genes (DEGs). The identification of core genes, denoted as Hub genes, among the feature genes and DEGs, is achieved through meticulous overlapping analysis. We illuminate the immune characteristics of these Hub genes using a suite of analytical tools, encompassing CIBERSORT, MCPcounter, and pseudotemporal analysis, all based on immune cell annotations and single-cell transcriptome data.Subsequently, we harness the CMap database to prognosticate potential therapeutic drugs and scrutinize their associations with the identified Hub genes. Our findings unveil robust linkages between three pivotal Hub genes-namely, RNF13, VASP, and CD163-and specific immune cell types such as T cells and neutrophils. These Hub genes predominantly manifest in macrophages and microglial cells within the scRNA-seq immune cell population, exhibiting variances across different stages of cellular differentiation. In conclusion, this study unearths highly pertinent biomarkers for IS diagnosis and elucidates IS-induced immune infiltration characteristics, thus providing a firm foundation for a comprehensive exploration of potential immune mechanisms and the identification of novel therapeutic targets for IS.

Exploring common biomarkers of ischemic stroke and obstructive sleep apnea through bioinformatics analysis

BACKGROUND

Clinical observations have shown that many patients with ischemic stroke (IS) have a history of obstructive sleep apnea (OSA) both before and after the stroke's onset, suggesting potential underlying connections and shared comorbid mechanisms between the two conditions. The aim of this study is to identify the genetic characteristics of OSA patients who develop IS and to establish a reliable disease diagnostic model to assess the risk of IS in OSA patients.

METHODS

We selected IS and OSA datasets from the Gene Expression Omnibus (GEO) database as training sets. Core genes were identified using the Limma package, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning algorithms. Gene Set Variation Analysis (GSVA) was conducted for pathway enrichment analysis, while single-sample gene set enrichment analysis (ssGSEA) was employed for immune infiltration analysis. Finally, a diagnostic model was developed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, with its diagnostic efficacy validated using receiver operating characteristic (ROC) curves across two independent validation sets.

RESULTS

The results revealed that differential analysis and machine learning identified two common genes, TM9SF2 and CCL8, shared between IS and OSA. Additionally, seven signaling pathways were found to be commonly upregulated in both conditions. Immune infiltration analysis demonstrated a significant decrease in monocyte levels, with TM9SF2 showing a negative correlation and CCL8 showing a positive correlation with monocytes. The diagnostic model we developed exhibited excellent predictive value in the validation set.

CONCLUSIONS

In summary, two immune-related core genes, TM9SF2 and CCL8, were identified as common to both IS and OSA. The diagnostic model developed based on these genes may be used to predict the risk of IS in OSA patients.

Multicenter Validation of lncRNA and Target mRNA Diagnostic and Prognostic Biomarkers of Acute Ischemic Stroke From Peripheral Blood Leukocytes

BACKGROUND

Long noncoding RNA (lncRNA) and mRNA profiles in leukocytes have shown potential as biomarkers for acute ischemic stroke (AIS). This study aimed to identify altered lncRNA and target mRNA profiles in peripheral blood leukocytes as biomarkers and to assess the diagnostic value and association with AIS prognosis.

METHODS AND RESULTS

Differentially expressed lncRNAs (DElncRNAs) and differentially expressed target mRNAs (DEmRNAs) were screened by RNA sequencing in the discovery set, which consisted of 10 patients with AIS and 20 controls. Validation sets consisted of a multicenter (311 AIS versus 303 controls) and a nested case-control study (351 AIS versus 352 controls). The discriminative value of DElncRNAs and DEmRNAs added to the traditional risk factors was estimated with the area under the curve. NAMPT-AS, FARP1-AS1, FTH1, and NAMPT were identified in the multicenter case-control study (P<0.05). LncRNA NAMPT-AS was associated with cis-target mRNA NAMPT and trans-target mRNA FTH1 in all validation sets (P<0.001). Similarly, AIS cases exhibited upregulated lncRNA FARP-AS1 and FTH1 expression (P<0.001) in the nested case-control study (P<0.001). Furthermore, lncRNA FARP1-AS1 expression was upregulated in AIS patients at discharge with an unfavorable outcome (P<0.001). Positive correlations were found between NAMPT expression level and NIHSS scores of AIS patients (P<0.05). Adding 2 lncRNAs and 2 target mRNAs to the traditional risk factor model improved area under the curve by 22.8% and 5.2% in the multicenter and the nested case-control studies, respectively.

CONCLUSIONS

lncRNA NAMPT-AS and FARP1-AS1 have potential as diagnostic biomarkers for AIS and exhibit good performance when combined with target mRNA NAMPT and FTH1.

Inflammatory biomarkers may be associated with poor outcomes after mechanical thrombectomy

BACKGROUND

Mechanical thrombectomy (MT) has become the mainstay of treatment for acute ischemic stroke (AIS) recently. This case-control study aimed to identify the pivotal role of inflammation in the prognosis of AIS patients after MT.

METHODS

Altogether, 70 AIS patients who underwent MT were retrospectively recruited for this study. Receiver operating characteristic analysis was performed to demonstrate the sensitivity and specificity of the inflammatory variables for predicting prognosis. A meta-analysis was performed to pool the published results together. Stata software was used for analysis.

RESULTS

There was no differences in pre-MT inflammatory biomarkers between patients who survived and those who died, as well as patients with modified Rankin Scale (mRS) 0-2 and mRS ≥ 3. In contrast, post-MT C-reactive protein (CRP) levels might be a potential parameter to predict death after thrombectomy [area under the curve (AUC), 95%confidence interval (CI), 0.737, 0.587-0.887; p = 0.005; optimal cutoff value = 4.565]. Moreover, post-MT monocyte count might be an appropriate parameter to predict poor long-term prognosis after thrombectomy (AUC, 95%CI, 0.704, 0.575-0.833; p = 0.017; optimal cutoff value = 0.345). A meta-analysis revealed that the pre-MT inflammatory indices, including white blood cell count (weighted mean difference, 95%CI, 1.32, 1.01-1.63), neutrophil count (1.23, 0.95-1.51), monocyte count (0.05, 0.02-0.09), neuthrophil-to-lymphocyte ratio (2.42, 1.98-2.87) and platelet-to-lymphocyte ratio (24.65, 7.99-41.32), were higher in patients with 3-month mRS ≥ 3, and the lymphocyte count (-0.31,-0.43 to -0.18) was lower in this cohort.

CONCLUSIONS

Inflammatory indices were significantly associated with the prognosis of patients undergoing MT, especially post-MT CRP and monocyte count, which can predict long-term outcomes.

Decoding Molecular Mechanisms Underlying Outcomes After Ischemic Stroke Thrombectomy by RNA Sequencing of Retrieved Clots

BACKGROUND

Transcriptomic profiling has emerged as a powerful tool for exploring the molecular landscape of ischemic stroke clots and providing insights into the pathophysiological mechanisms underlying stroke progression and recovery. In this study, we aimed to investigate the relationship between stroke clot transcriptomes and stroke thrombectomy outcome, as measured by early neurological improvement (ENI) 30 (i.e., a 30% reduction in NIHSS at 24 h post-thrombectomy).

HYPOTHESIS

We hypothesized that there exist distinct clot gene expression patterns between good and poor neurological outcomes.

METHODS

Transcriptomic analysis of 32 stroke clots retrieved by mechanical thrombectomy was conducted. Transcriptome data of these clots were analyzed to identify differentially expressed genes (DEGs), defined as those with a log(fold-change) ≥ 1.5 and q < 0.05 between samples with good and poor early neurological outcomes. Gene ontology and bioinformatics analyses were performed on genes with p < 0.01 to identify enriched biological processes and Ingenuity Pathway Analysis canonical pathways. Moreover, AUC analysis assessed the predictive power of DEGs for 90-day function outcome (mRS ≤ 2) and cellular composition of clot was predicted using CIBERSORT. We also assessed whether differential enrichment of immune cell types could indicate patient survival.

RESULTS

A total of 41 DEGs were identified. Bioinformatics showed that enriched biological processes and pathways emphasized the chronic immune response and matrix metalloproteinase inhibition. Moreover, 25 of the DEGs were found to be significant predictors of 90-day mRS. These genes were indicative of monocytes enrichment and neutrophil depletion in patients with poorer outcomes.

CONCLUSION

Our study revealed a distinct gene expression pattern and dysregulated biological pathways associated with ENI. This expression pattern was also predictive of long-term outcome, suggesting a biological link between those ENIs and 90-day mRS.

Systemic-inflammatory indices and clinical outcomes in patients with anterior circulation acute ischemic stroke undergoing successful endovascular thrombectomy

Background

There is a lack of comprehensive profile assessment on complete blood count (CBC)-derived systemic-inflammatory indices, and their correlations with clinical outcome in patients with anterior circulation acute ischemic stroke (AIS) who achieved successful recanalization by endovascular thrombectomy (EVT).

Methods

Patients with anterior circulation AIS caused by large vessel occlusion (AIS-LVO) were retrospectively screened from December 2018 to December 2022. Systemic-inflammatory indices including ratios of neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte (MLR), platelet-to-lymphocyte (PLR), and platelet-to-neutrophil (PNR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and aggregate inflammation systemic index (AISI) on admission and the first day post-EVT were calculated. Their correlations with symptomatic intracranial hemorrhage (sICH) and unfavorable 90-day functional outcome (modified Rankin Scale score of 3-6) were analyzed.

Results

A total of 482 patients [65 (IQR, 56-72) years; 33 % female] were enrolled, of which 231 (47.9 %) had unfavorable 90-day outcome and 50 (10.4 %) developed sICH. Day 1 neutrophil and monocyte counts, NLR, MLR, PLR, SII, SIRI, and AISI were increased, while lymphocyte and PNR were decreased compared to their admission levels. In multivariate analyses, neutrophil count, NLR, SII, and AISI on day 1 were independently associated with 90-day functional outcome. Moreover, day 1 neutrophil count, NLR, MLR, PLR, PNR, SII, and SIRI were independently linked to the occurrence of sICH. No admission variables were identified as independent risk factors for patient outcomes.

Conclusion

CBC-derived systemic-inflammatory indices measured on the first day after successful EVT are predictive of 90-day functional outcome and the sICH occurrence in patients with anterior circulation AIS-LVO.

Association between advanced lung cancer inflammation index and all-cause and cardiovascular mortality among stroke patients: NHANES, 1999-2018

Background

Stroke was a major global public health challenge, and its prognosis was remarkably associated with inflammation levels and nutritional status. The advanced lung cancer inflammation index (ALI) was a comprehensive indicator that combined inflammation and nutritional status. Currently, the relationship between ALI and the prognosis of stroke patients was not yet known. The purpose of the current study was to estimate their relationship.

Methods

Cohort data from the National Health and Nutrition Examination Survey (NHANES) 1999-2018 were collected. The association between ALI and all-cause and cardiovascular disease (CVD) mortality in stroke patients was estimated using a multivariable adjusted Cox model. Their non-linear relationship was analyzed by restricted cubic spline analysis. Sensitivity analysis was constructed through stratified analysis and interaction analysis.

Results

1,440 stroke patients were included in this study. An elevated ALI was significantly related to a reduced risk of all-cause mortality in stroke patients but not related to CVD mortality. A reverse J-shaped non-linear association between ALI and all-cause mortality in stroke patients, with an inflection point at 83.76 (the lowest of the mortality risk). On the left side of the inflection point, for each 10 U increase in ALI, there was a 16% reduction in the risk of all-cause mortality. However, on the right side, the risk increased by 6%. There was no remarkable interaction between stratified variables and ALI.

Conclusion

This was the first study on the relationship between ALI and all-cause and CVD mortality in stroke patients. Elevated ALI was closely associated with a reduced risk of all-cause mortality. A reverse J-shaped non-linear relationship existed between the two, with an inflection point at 83.76. These findings implied that controlling the ALI of stroke patients within an appropriate range was crucial for their prognosis (such as weight management, albumin supplementation, anti-inflammatory treatment). The dynamic variation in ALI was also advantageous for clinicians in establishing personalized ALI criteria to maximize the long-term survival of stroke patients.

LCP1 knockdown in monocyte-derived macrophages: mitigating ischemic brain injury and shaping immune cell signaling and metabolism

Rationale: Ischemic stroke poses a significant health burden with limited treatment options. Lymphocyte Cytosolic Protein 1 (LCP1) facilitates cell migration and immune responses by aiding in actin polymerization, cytoskeletal rearrangements, and phagocytosis. We have demonstrated that the long non-coding RNA (lncRNA) Maclpil silencing in monocyte-derived macrophages (MoDMs) led to LCP1 inhibition, reducing ischemic brain damage. However, the role of LCP1 of MoDMs in ischemic stroke remains unknown. Methods and Results: We investigated the impact of LCP1 on ischemic brain injury and immune cell signaling and metabolism. We found that knockdown of LCP1 in MoDMs demonstrated robust protection against ischemic infarction and improved neurological behaviors in mice. Utilizing the high-dimensional CyTOF technique, we demonstrated that knocking down LCP1 in MoDMs led to a reduction in neuroinflammation and attenuation of lymphopenia, which is linked to immunodepression. It also showed altered immune cell signaling by modulating the phosphorylation levels of key kinases and transcription factors, including p-PLCg2, p-ERK1/2, p-EGFR, p-AKT, and p4E-BP1 as well as transcription factors like p-STAT1, p-STAT3, and p-STAT4. Further bioinformatic analysis indicated that Akt and EGFR are particularly involved in fatty acid metabolism and glycolysis. Indeed, single-cell sequencing analysis confirmed that enrichment of fatty acid and glycolysis metabolism in Lcp1high monocytes/macrophages. Furthermore, Lcp1high cells exhibited enhanced oxidative phosphorylation, chemotaxis, migration, and ATP biosynthesis pathways. In vitro experiments confirmed the role of LCP1 in regulating mitochondrial function and fatty acid uptake. Conclusions: These findings contribute to a deeper understanding of LCP1 in the context of ischemic stroke and provide valuable insights into potential therapeutic strategies targeting LCP1 and metabolic pathways, aiming to attenuating neuroinflammation and lymphopenia.

A case-control comparison of acute-phase peripheral blood gene expression in participants diagnosed with minor ischaemic stroke or stroke mimics

BACKGROUND

Past studies suggest that there are changes in peripheral blood cell gene expression in response to ischaemic stroke; however, the specific changes which occur during the acute phase are poorly characterised. The current study aimed to identify peripheral blood cell genes specifically associated with the early response to ischaemic stroke using whole blood samples collected from participants diagnosed with ischaemic stroke (n = 29) or stroke mimics (n = 27) following emergency presentation to hospital. Long non-coding RNA (lncRNA), mRNA and micro-RNA (miRNA) abundance was measured by RNA-seq, and the consensusDE package was used to identify genes which were differentially expressed between groups. A sensitivity analysis excluding two participants with metastatic disease was also conducted.

RESULTS

The mean time from symptom onset to blood collection was 2.6 h. Most strokes were mild (median NIH stroke scale score 2.0). Ten mRNAs (all down-regulated in samples provided by patients experiencing ischaemic stroke) and 30 miRNAs (14 over-expressed and 16 under-expressed in participants with ischaemic stroke) were significantly different between groups in the whole cohort and sensitivity analyses. No significant over-representation of gene ontology categories by the differentially expressed genes was observed. Random forest analysis suggested a panel of differentially expressed genes (ADGRG7 and miRNAs 96, 532, 6766, 6798 and 6804) as potential ischaemic stroke biomarkers, although modelling analyses demonstrated that these genes had poor diagnostic performance.

CONCLUSIONS

This study provides evidence suggesting that the early response to minor ischaemic stroke is predominantly reflected by changes in the expression of miRNAs in peripheral blood cells. Further work in independent cohorts particularly in patients with more severe stroke is needed to validate these findings and investigate their clinical relevance.

The relationship between systemic inflammation index, systemic immune-inflammatory index, and inflammatory prognostic index and 90-day outcomes in acute ischemic stroke patients treated with intravenous thrombolysis

BACKGROUND AND PURPOSE

To explore the association of systemic inflammatory index (SIRI), systemic immune-inflammatory index (SII) and inflammatory prognosis index (IPI) with 90d outcomes in patients with acute ischemic stroke (AIS) after intravenous thrombolysis.

METHODS

The patients who underwent intravenous thrombolysis were enrolled in the present study from September 2019 to December 2022. According to the relevant blood indexes obtained in 24 h after admission, the corresponding values of SIRI, SII and IPI were calculated. The correlation among SIRI, SII, IPI, and admission NIHSS scores was examined by Spearman correlation analysis. ROC curve analysis was conducted to determine the optimal cut-off value of SIRI, SII, IPI, and their corresponding sensitivity and specificity to evaluate their predictive value on admission for poor prognosis. To investigate whether high SIRI, SII, and IPI were independent predictors of poor outcomes within 90 days, variables with P-value < 0.05 during univariate analysis were included in multivariate analysis.

RESULTS

Compared with the good outcome group, the poor outcome group had higher SIRI, IPI, and SII. Spearman correlation analysis showed that the SIRI, IPI, and SII levels significantly correlated with the admission NIHSS score (r = 0.338, 0.356, 0.427, respectively; Ps < 0.001). Univariate analysis and Multivariate logistic regression analysis revealed high SIRI, SII, and IPI values as independent risk factors for poor 90-day prognosis (OR = 1.09, 1.003 and 7.109, respectively).

CONCLUSIONS

High SIRI, IPI, and SII values are correlated with poor 90d outcomes in AIS patients undergoing intravenous thrombolysis.

A Nomogram for Predicting the Recurrence of Acute Non-Cardioembolic Ischemic Stroke: A Retrospective Hospital-Based Cohort Analysis

Non-cardioembolic ischemic stroke (IS) is the predominant subtype of IS. This study aimed to construct a nomogram for recurrence risks in patients with non-cardioembolic IS in order to maximize clinical benefits. From April 2015 to December 2019, data from consecutive patients who were diagnosed with non-cardioembolic IS were collected from Lanzhou University Second Hospital. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection. Multivariable Cox regression analyses were used to identify the independent risk factors. A nomogram model was constructed using the "rms" package in R software via multifactor Cox regression. The accuracy of the model was evaluated using the receiver operating characteristic (ROC), calibration curve, and decision curve analyses (DCA). A total of 729 non-cardioembolic IS patients were enrolled, including 498 (68.3%) male patients and 231 (31.7%) female patients. Among them, there were 137 patients (18.8%) with recurrence. The patients were randomly divided into training and testing sets. The Kaplan-Meier survival analysis of the training and testing sets consistently revealed that the recurrence rates in the high-risk group were significantly higher than those in the low-risk group (p < 0.01). Moreover, the receiver operating characteristic curve analysis of the risk score demonstrated that the area under the curve was 0.778 and 0.760 in the training and testing sets, respectively. The nomogram comprised independent risk factors, including age, diabetes, platelet-lymphocyte ratio, leukoencephalopathy, neutrophil, monocytes, total protein, platelet, albumin, indirect bilirubin, and high-density lipoprotein. The C-index of the nomogram was 0.752 (95% CI: 0.705~0.799) in the training set and 0.749 (95% CI: 0.663~0.835) in the testing set. The nomogram model can be used as an effective tool for carrying out individualized recurrence predictions for non-cardioembolic IS.