Immune-inflammatory, coagulation, adhesion, and imaging biomarkers combined in machine learning models improve the prediction of death 1 year after ischemic stroke

Relationship between thrombus vWF and NETs with clinical severity and peripheral blood immunocytes' indicators in patients with acute ischemic stroke

OBJECTIVE

To investigate the association between von Willebrand factor (vWF) and neutrophil extracellular traps (NETs) in thrombus with clinical severity and peripheral blood immunocytes' indicators in patients with early-stage acute ischemic stroke (AIS).

METHODS

A retrospective study was conducted using the clinical data of 66 patients with AIS who underwent endovascular mechanical thrombectomy and had their thrombus samples collected. The concentrations of vWF and NETs in the thrombus samples were quantitatively assessed. Peripheral blood samples taken in the early stages of the disease were analyzed for total white blood cell counts (WBC), ratios of neutrophils (NEU%), lymphocytes (LYM%), eosinophils (EOS%), and monocytes (MONO%). The severity of clinical symptoms in these patients was evaluated using the modified Rankin Scale (mRS), Essen Stroke Risk Score (ESRS), Barthel Index (BI), and National Institute of Health Stroke Scale (NIHSS).

RESULTS

Higher vWF levels in thrombus were associated with lower NIHSS scores, while higher NETs levels were associated with higher initial NIHSS scores. In the early stages of AIS, WBC count and vWF levels were negatively correlated, as well as NEU%. LYM% was positively correlated with vWF level; however, it was negatively correlated with NETs. EOS% was positively correlated with vWF levels.

CONCLUSION

In the early stages of AIS, a higher peripheral WBC count and NEU%, combined with decreased EOS% and LYM%, were significantly correlated with a lower vWF level in the thrombus, potentially indicating more severe symptoms. Consequently, the timely administration of vWF-targeted medications is recommended for such patients. Reduced LYM% is indicative of elevated NETs levels and correlated with more severe clinical symptoms. Therefore, the prompt initiation of NETs-targeted medication is warranted for these patients.

Transcriptomic Analysis of Extracellular Vesicles in the Search for Novel Plasma and Thrombus Biomarkers of Ischemic Stroke Etiologies

Accurate etiologic diagnosis provides an appropriate secondary prevention and better prognosis in ischemic stroke (IS) patients; still, 45% of IS are cryptogenic, urging us to enhance diagnostic precision. We have studied the transcriptomic content of plasma extracellular vesicles (EVs) (n = 21) to identify potential biomarkers of IS etiologies. The proteins encoded by the selected genes were measured in the sera of IS patients (n = 114) and in hypertensive patients with (n = 78) and without atrial fibrillation (AF) (n = 20). IGFBP-2, the most promising candidate, was studied using immunohistochemistry in the IS thrombi (n = 23) and atrium of AF patients (n = 13). In vitro, the IGFBP-2 blockade was analyzed using thromboelastometry and endothelial cell cultures. We identified 745 differentially expressed genes among EVs of cardioembolic, atherothrombotic, and ESUS groups. From these, IGFBP-2 (cutoff > 247.6 ng/mL) emerged as a potential circulating biomarker of embolic IS [OR = 8.70 (1.84-41.13) p = 0.003], which was increased in patients with AF vs. controls (p < 0.001) and was augmented in cardioembolic vs. atherothrombotic thrombi (p < 0.01). Ex vivo, the blockage of IGFBP-2 reduced clot firmness (p < 0.01) and lysis time (p < 0.001) and in vitro, diminished endothelial permeability (p < 0.05) and transmigration (p = 0.06). IGFBP-2 could be a biomarker of embolic IS and a new therapeutic target involved in clot formation and endothelial dysfunction.

Fluid biomarkers of the neurovascular unit in cerebrovascular disease and vascular cognitive disorders: A systematic review and meta-analysis

Background

The disruption of the neurovascular unit (NVU), which maintains the integrity of the blood brain barrier (BBB), has been identified as a critical mechanism in the development of cerebrovascular and neurodegenerative disorders. However, the understanding of the pathophysiological mechanisms linking NVU dysfunction to the disorders is incomplete, and reliable blood biomarkers to measure NVU dysfunction are yet to be established. This systematic review and meta-analysis aimed to identify biomarkers associated with BBB dysfunction in large vessel disease, small vessel disease (SVD) and vascular cognitive disorders (VCD).

Methods

A literature search was conducted in PubMed, EMBASE, Scopus and PsychINFO to identify blood biomarkers related to dysfunction of the NVU in disorders with vascular pathologies published until 20 November 2023. Studies that assayed one or more specific markers in human serum or plasma were included. Quality of studies was assessed using the Newcastle-Ottawa Quality Assessment Scale. Effects were pooled and methodological heterogeneity examined using the random effects model.

Results

A total of 112 studies were included in this review. Where study numbers allowed, biomarkers were analysed using random effect meta-analysis for VCD (1 biomarker; 5 studies) and cerebrovascular disorders, including stroke and SVD (9 biomarkers; 29 studies) while all remaining biomarkers (n = 17 biomarkers; 78 studies) were examined through qualitative analysis. Results of the meta-analysis revealed that cerebrospinal fluid/serum albumin quotient (Q-Alb) reliably differentiates VCD patients from healthy controls (MD = 2.77; 95 % CI = 1.97-3.57; p < 0.0001) while commonly measured biomarkers of endothelial dysfunction (VEGF, VCAM-1, ICAM-1, vWF and E-selectin) and neuronal injury (NfL) were significantly elevated in vascular pathologies. A qualitative assessment of non-meta-analysed biomarkers revealed NSE, NfL, vWF, ICAM-1, VCAM-1, lipocalin-2, MMP-2 and MMP-9 levels to be upregulated in VCD, although these findings were not consistently replicated.

Conclusions

This review identifies several promising biomarkers of NVU dysfunction which require further validation. A panel of biomarkers representing multiple pathophysiological pathways may offer greater discriminative power in distinguishing possible disease mechanisms of VCD.

Development and Validation of a Dynamic Nomogram for Predicting 3-Month Mortality in Acute Ischemic Stroke Patients with Atrial Fibrillation

Background

Acute ischemic stroke (AIS) in patients with atrial fibrillation (AF) carries a substantial risk of mortality, emphasizing the need for effective risk assessment and timely interventions. This study aimed to develop and validate a practical dynamic nomogram for predicting 3-month mortality in AIS patients with AF.

Methods

AIS patients with AF were enrolled and randomly divided into training and validation cohorts. The nomogram was developed based on independent risk factors identified by multivariate logistic regression analysis. The prediction performance of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC-ROC), calibration plots, decision curve analysis (DCA), and Kaplan-Meier survival analysis.

Results

A total of 412 patients with AIS and AF entered final analysis, 288 patients in the training cohort and 124 patients in the validation cohort. The nomogram was developed using age, baseline National Institutes of Health Stroke Scale score, early introduction of novel oral anticoagulants, and pneumonia as independent risk factors. The nomogram exhibited good discrimination both in the training cohort (AUC, 0.851; 95% CI, 0.802-0.899) and the validation cohort (AUC, 0.811; 95% CI, 0.706-0.916). The calibration plots, DCA and Kaplan-Meier survival analysis demonstrated that the nomogram was well calibrated and clinically useful, effectively distinguishing the 3-month survival status of patients with AIS and AF, respectively. The dynamic nomogram can be obtained at the website: https://yanxiaodi.shinyapps.io/3-monthmortality/.

Conclusion

The dynamic nomogram represents the first predictive model for 3-month mortality and may contribute to managing the mortality risk of patients with AIS and AF.

Paraoxonase 1 status is a major Janus-faced component of mild and moderate acute ischemic stroke and consequent disabilities

AIMS

This study aims to examine the associations between paraoxonase 1 (PON)1 status and acute ischemic stroke (AIS) and consequent disabilities.

METHODS

This study recruited 122 patients with AIS and 40 healthy controls and assessed the Q192R gene variants, arylesterase (AREase) and chloromethyl phenylacetate (CMPAase) activities, and high-density lipoprotein cholesterol (HDLc) in baseline conditions. AREase and CMPAase were measured 3 months later. The National Institutes of Health Stroke Scale (NIHSS) and the modified Rankin score (mRS) were assessed at baseline and 3 and 6 months later.

RESULTS

Reduced CMPAase and increased AREase activities are significantly associated with AIS and mRS and NIHSS scores (baseline and 3 and 6 months later). The best predictor of AIS/disabilities was a decrease in the z-unit-based composite zCMPAase-zAREase score. Serum high density lipoprotein cholsterol (HDLc) was significantly correlated with CMPAase, but not AREase, activity and a lowered zCMPAase + zHDLc score was the second best predictor of AIS/disabilities. Regression analysis showed that 34.7% of the variance in baseline NIHSS was explained by zCMPAase-zAREase and zCMPAase + zHDLc composites, HDLc, and hypertension. Neural network analysis showed that stroke was differentiated from controls with an area under the ROC curve of 0.975 using both new composite scores, PON1 status, hypertension, dyslipidemia, previous stroke as body mass index. The PON1 Q192R genotype has many significant direct and mediated effects on AIS/disabilities, however, its overall effect was not significant.

DISCUSSION

PON1 status and the CMPAase-HDLc complex play key roles in AIS and its disabilities at baseline and 3 and 6 months later.

Baseline severity and soluble vascular cell adhesion molecule 1 (sVCAM-1) as biomarker predictors of short-term mortality in acute ischemic stroke

The aim was to investigate the association between plasma levels of cellular adhesion molecules (CAMs) and risk factors, subtypes, severity and short-term mortality of acute ischemic stroke (IS), and to identify a panel of biomarkers to predict short-term mortality after IS. The prospective study evaluated 132 IS patients within 24 h of their hospital admission. The baseline IS severity was assessed using the National Institutes Health Stroke Scale (NIHSS) and categorized as mild (NIHSS < 5), moderate (NIHSS 5-14) and severe (NIHSS ≥ 15). After three-month follow-up, the disability was assessed using the modified Rankin Scale (mRS); moreover, the patients were classified as survivors and non-survivors. Baseline inflammatory and anti-inflammatory cytokines and soluble CAMs were evaluated. Twenty-nine (21.9%) IS patients were non-survivors and showed higher NIHSS and soluble vascular cellular adhesion molecule 1 (sVCAM-1) than the survivors. The sVCAM-1 levels positively correlated with age, homocysteine, severity, and disability. The model #3 combining sVCAM-1 and NIHSS showed better results to predict short-term mortality with an area under the curve receiving operating characteristics (AUC/ROC) of 0.8841 [95% confidence interval (CI): 0.795-0.941] than the models with sVCAM-1 and NIHSS alone, with positive predictive value of 68.0%, negative predictive value of 91.3%, and accuracy of 86.5%. In conclusion, the combined model with baseline severity of IS and sVCAM-1 levels can early predict the prognosis of IS patients who may benefit with therapeutic measures of personalized therapy that taken into account these biomarkers. Moreover, this result suggests that VCAM-1 might be a potential target for the therapeutic strategies in IS.

Stroke mortality prediction using machine learning: systematic review

BACKGROUND AND AIMS

Accurate prognostication of stroke may help in appropriate therapy and rehabilitation planning. In the past few years, several machine learning (ML) algorithms were applied for prediction of stroke outcomes. We aimed to examine the performance of machine learning-based models for the prediction of mortality after stroke, as well as to identify the most prominent factors for mortality.

MATERIALS AND METHODS

We searched MEDLINE/PubMed and Web of Science databases for original publications on machine learning applications in stroke mortality prediction, published between January 1, 2011, and October 27, 2022. Risk of bias and applicability were evaluated using the tailored QUADAS-2 tool.

RESULTS

Of the 1015 studies retrieved, 28 studies were included. Twenty-Five studies were retrospective. The ML models demonstrated a favorable range of AUC for mortality prediction (0.67-0.98). In most of the articles, the models were applied for short-term post stroke mortality. The number of explanatory features used in the models to predict mortality ranged from 5 to 200, with substantial overlap in the variables included. Age, high BMI and high NIHSS score were identified as important predictors for mortality. Almost all studies had a high risk of bias in at least one category and concerns regarding applicability.

CONCLUSION

Using machine learning, data available at the time of admission may aid in stroke mortality prediction. Notwithstanding, current research is based on few preliminary works with high risk of bias and high heterogeneity. Thus, future prospective, multicenter studies with standardized reports are crucial to firmly establish the usefulness of the algorithms in stroke prognostication.

In Mild and Moderate Acute Ischemic Stroke, Increased Lipid Peroxidation and Lowered Antioxidant Defenses Are Strongly Associated with Disabilities and Final Stroke Core Volume

In acute ischemic stroke (AIS), there are no data on whether oxidative stress biomarkers have effects above and beyond known risk factors and measurements of stroke volume. This study was conducted in 122 mild-moderate AIS patients and 40 controls and assessed the modified ranking scale (mRS) at baseline, and 3 and 6 months later. We measured lipid hydroperoxides (LOOH), malondialdehyde (MDA), advanced oxidation protein products, paraoxonase 1 (PON1) activities and PON1 Q192R genotypes, high density lipoprotein cholesterol (HDL), sulfhydryl (-SH) groups), and diffusion-weighted imaging (DWI) stroke volume and fluid-attenuated inversion recovery (FLAIR) signal intensity. We found that (a) AIS is characterized by lower chloromethyl acetate CMPAase PON1 activity, HDL and -SH groups and increased LOOH and neurotoxicity (a composite of LOOH, inflammatory markers and glycated hemoglobin); (b) oxidative and antioxidant biomarkers strongly and independently predict mRS scores 3 and 6 months later, DWI stroke volume and FLAIR signal intensity; and (c) the PON1 Q192R variant has multiple effects on stroke outcomes that are mediated by its effects on antioxidant defenses and lipid peroxidation. Lipid peroxidation and lowered -SH and PON1-HDL activity are drug targets to prevent AIS and consequent neurodegenerative processes and increased oxidative reperfusion mediators due to ischemia-reperfusion injury.

Protein Biomarkers in Blood Reflect the Interrelationships Between Stroke Outcome, Inflammation, Coagulation, Adhesion, Senescence and Cancer

The most important predictors for outcomes after ischemic stroke, that is, for health deterioration and death, are chronological age and stroke severity; gender, genetics and lifestyle/environmental factors also play a role. Of all these, only the latter can be influenced after the event. Recurrent stroke may be prevented by antiaggregant/anticoagulant therapy, angioplasty of high-grade stenoses, and treatment of cardiovascular risk factors. Blood cell composition and protein biomarkers such as C-reactive protein or interleukins in serum are frequently considered as biomarkers of outcome. Here we aim to provide an up-to-date protein biomarker signature that allows a maximum of mechanistic understanding, to predict health deterioration following stroke. We thus surveyed protein biomarkers that were reported to be predictive for outcome after ischemic stroke, specifically considering biomarkers that predict long-term outcome (≥ 3 months) and that are measured over the first days following the event. We classified the protein biomarkers as immune‑inflammatory, coagulation-related, and adhesion-related biomarkers. Some of these biomarkers are closely related to cellular senescence and, in particular, to the inflammatory processes that can be triggered by senescent cells. Moreover, the processes that underlie inflammation, hypercoagulation and cellular senescence connect stroke to cancer, and biomarkers of cancer-associated thromboembolism, as well as of sarcopenia, overlap strongly with the biomarkers discussed here. Finally, we demonstrate that most of the outcome-predicting protein biomarkers form a close-meshed functional interaction network, suggesting that the outcome after stroke is partially determined by an interplay of molecular processes relating to inflammation, coagulation, cell adhesion and cellular senescence.

False dogmas in mood disorders research: Towards a nomothetic network approach

The current understanding of major depressive disorder (MDD) and bipolar disorder (BD) is plagued by a cacophony of controversies as evidenced by competing schools to understand MDD/BD. The DSM/ICD taxonomies have cemented their status as the gold standard for diagnosing MDD/BD. The aim of this review is to discuss the false dogmas that reign in current MDD/BD research with respect to the new, data-driven, machine learning method to model psychiatric illness, namely nomothetic network psychiatry (NNP). This review discusses many false dogmas including: MDD/BD are mind-brain disorders that are best conceptualized using a bio-psycho-social model or mind-brain interactions; mood disorders due to medical disease are attributable to psychosocial stress or chemical imbalances; DSM/ICD are the gold standards to make the MDD/BD diagnosis; severity of illness should be measured using rating scales; clinical remission should be defined using threshold values on rating scale scores; existing diagnostic BD boundaries are too restrictive; and mood disorder spectra are the rule. In contrast, our NNP models show that MDD/BD are not mind-brain or psycho-social but systemic medical disorders; the DSM/ICD taxonomies are counterproductive; a shared core, namely the reoccurrence of illness (ROI), underpins the intertwined recurrence of depressive and manic episodes and suicidal behaviors; mood disorders should be ROI-defined; ROI mediates the effects of nitro-oxidative stress pathways and early lifetime trauma on the phenome of mood disorders; severity of illness and treatment response should be delineated using the NNP-derived causome, pathway, ROI and integrated phenome scores; and MDD and BD are the same illness.

The Assessment of Endovascular Therapies in Ischemic Stroke: Management, Problems and Future Approaches

Ischemic stroke accounts for over 80% of all strokes and is one of the leading causes of mortality and permanent disability worldwide. Intravenous administration of recombinant tissue plasminogen activator (rt-PA) is an approved treatment strategy for acute ischemic stroke of large arteries within 4.5 h of onset, and mechanical thrombectomy can be used for large arteries occlusion up to 24 h after onset. Improving diagnostic work up for acute treatment, reducing onset-to-needle time and urgent radiological access angiographic CT images (angioCT) and Magnetic Resonance Imaging (MRI) are real problems for many healthcare systems, which limits the number of patients with good prognosis in real world compared to the results of randomized controlled trials. The applied endovascular procedures demonstrated high efficacy, but some cellular mechanisms, following reperfusion, are still unknown. Changes in the morphology and function of mitochondria associated with reperfusion and ischemia-reperfusion neuronal death are still understudied research fields. Moreover, future research is needed to elucidate the relationship between continuously refined imaging techniques and the variable structure or physical properties of the clot along with vascular permeability and the pleiotropism of ischemic reperfusion lesions in the penumbra, in order to define targeted preventive procedures promoting long-term health benefits.

Clinical Significance and Dynamic Change of Coagulation Parameters in Ischemic Stroke Patients Treated with Intravenous Thrombolysis

Objective

Investigations on coagulation parameters including fibrinogen (Fbg), fibrinogen degradation products (FDP), and D-dimer in ischemic stroke patients treated with intravenous thrombolysis are insufficient. We aimed to investigate the association between in-hospital clinical outcomes and the coagulation parameters at different time points in ischemic stroke patients treated with intravenous tissues plasminogen activator (IV tPA).

Methods

We retrospectively enrolled patients who received IV tPA therapy within 4.5 h from symptoms onset. Demographics, clinical characteristics, imaging measures, and the discharge mRS score were collected. Multivariable logistic regression analyses were performed to test whether coagulation parameters were independent predictors for the in-hospital clinical outcomes. We also employed machine learning models to investigate whether coagulation parameters were able to improve the prediction of favorable functional outcomes.

Results

One hundred and fifty-two patients treated with IV tPA were included. Among the coagulation parameters, low D-dimers at 48 h proved to be an independent predictor of favorable functional outcome (adjusted odd ratio 0.24, 95% confidential intervals 0.06-0.92, P  =  0.04). The AUC of D-dimer at 48 h to predict favorable functional outcome was 0.73 (0.60-0.87) and the optimal cut-off value was 0.92 (sensitivity 0.69, specificity 0.78). Machine learning models with D-dimer at 48 h had superior performance in predicting favorable functional outcomes and among the input variables in the machine learning models, D-dimer at 48 h showed the highest weight in predicting mRS 0-1 at discharge (38.44%).

Conclusion

Increased levels of D-dimer at 48 h was associated with lower proportion of favorable functional outcomes in acute ischemic stroke patients with intravenous thrombolysis.

New Drug Targets to Prevent Death Due to Stroke: A Review Based on Results of Protein-Protein Interaction Network, Enrichment, and Annotation Analyses

This study used established biomarkers of death from ischemic stroke (IS) versus stroke survival to perform network, enrichment, and annotation analyses. Protein-protein interaction (PPI) network analysis revealed that the backbone of the highly connective network of IS death consisted of IL6, ALB, TNF, SERPINE1, VWF, VCAM1, TGFB1, and SELE. Cluster analysis revealed immune and hemostasis subnetworks, which were strongly interconnected through the major switches ALB and VWF. Enrichment analysis revealed that the PPI immune subnetwork of death due to IS was highly associated with TLR2/4, TNF, JAK-STAT, NOD, IL10, IL13, IL4, and TGF-β1/SMAD pathways. The top biological and molecular functions and pathways enriched in the hemostasis network of death due to IS were platelet degranulation and activation, the intrinsic pathway of fibrin clot formation, the urokinase-type plasminogen activator pathway, post-translational protein phosphorylation, integrin cell-surface interactions, and the proteoglycan-integrin extracellular matrix complex (ECM). Regulation Explorer analysis of transcriptional factors shows: (a) that NFKB1, RELA and SP1 were the major regulating actors of the PPI network; and (b) hsa-mir-26-5p and hsa-16-5p were the major regulating microRNA actors. In conclusion, prevention of death due to IS should consider that current IS treatments may be improved by targeting VWF, the proteoglycan-integrin-ECM complex, TGF-β1/SMAD, NF-κB/RELA and SP1.

Genetic Variation of Inflammatory Genes to Ischemic Stroke Risk in a Chinese Han Population

Background

Inflammation proteins play an important role in stroke occurrence. IL1A, IL1B, PTGS2, MMP2, and MMP9 were the mediators involved in the immune response, and the association of these genetic variations with ischemic stroke (IS) risk was still unclear.

Methods

To investigate the susceptibility of genetic variations of IL1A, IL1B, PTGS2, MMP2, and MMP9 to IS risk, we performed a case–control study involving 299 patients and 300 controls in a Chinese population. Thirteen genetic variations of investigated genes of all participants were genotyped using an improved multiplex ligase detection–reaction technique.

Results

No SNP in all genes showed an association with overall IS. However, in subgroup analysis, PTGS2 rs689466 (dominant model: CT vs TT – OR_adjusted= 2.51, 95% CI: 1.22–5.16, p = 0.012; co-dominant model: CT/CC vs TT – OR_adjusted= 2.53, 95% CI: 1.26–5.07, p = 0.009; additive model – OR_adjusted= 2.26, 95% CI: 1.19–4.28, p = 0.013) and rs5275 (dominant model: GG vs AA – OR_adjusted= 0.31, 95% CI: 0.12–0.80, p = 0.016; co-dominant model: GA/GG vs AA – OR_adjusted= 0.45, 95% CI: 0.21–0.95, p = 0.036; additive model – OR_adjusted= 0.60, 95% CI: 0.39–0.92, p = 0.020) were associated with IS type of small-vessel occlusion.

Conclusion

Our study suggested that PTGS2 rs689466 C and rs5275 A were potentially associated with IS subtype of small-vessel occlusion. Our result should be confirmed with further large sample sized studies.