Association of inflammatory markers with functional outcome after endovascular thrombectomy in anterior circulation stroke: a retrospective study

 When will your incompetent? mentors and senior researchers tell you that 'associations' and inflammatory markers TELL YOU NOTHING ON HOW TO GET RECOVERED! 

I'll assess your comeuppance/screaming when you are the 1 in 4 per WHO that has a stroke will be soul satisfying. 

Association of inflammatory markers with functional outcome after endovascular thrombectomy in anterior circulation stroke: a retrospective study

• 

  Yun Mo 1

• X

  Xue Guan 2

• 

  Yuxuan He 1

• L

  Lijuan Wu 1

• 

  Meng Zuo 1*

• 

  Zhenhua Zhou 1*

• 1. Department of Neurology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China

• 2. Department of Neurology, The First People's Hospital of Wanzhou District, Chongqing, China

Abstract

Background: 

Endovascular thrombectomy (EVT) is an effective treatment for acute ischemic stroke (AIS) caused by large vessel occlusion in the anterior circulation. However, functional outcomes vary. Systemic inflammatory response may affect prognosis. This study aims to determine the prognostic association of inflammatory indicators for favorable outcomes in patients undergoing EVT.

Method: 

In this retrospective study, 378 patients with anterior circulation large vessel occlusion stroke (LVOS) who underwent EVT were included. Patients were stratified into favorable outcome group (mRS ≤ 2) and unfavorable outcome group (mRS > 2). Clinical baseline data and laboratory blood tests were completed on admission. Imaging data were collected between 2 and 5 days after admission. Univariate and multivariate analyses were performed to evaluate the factors associated with favorable outcome.

Result: 

Compared to the favorable outcome group, the unfavorable outcome group had a higher incidence of symptomatic intracranial hemorrhage (sICH) (22%) and stroke-associated pneumonia (SAP) (59%), lower successful recanalization (mTICI 2b-3: 83.7%), and poorer collateral circulation status (58.8%). Furthermore, the unfavorable outcome group exhibited significantly higher preoperative white blood cell count, neutrophil count, monocyte count, and neutrophil-to-lymphocyte ratio (NLR) (all p < 0.05), while the lymphocyte-to-monocyte ratio (LMR) and platelet distribution width (PDW) were significantly lower (all p < 0.05). The AUC for LMR was 0.578 (95% CI 0.517–0.639), with an optimal cut-off value of 2.69 (sensitivity 64.7%, specificity 48.8%). The AUC for PDW was 0.572 (95% CI 0.511–0.636), with an optimal cut-off value of 15.2% (sensitivity 78.9%, specificity 43.1%).

Conclusion: 

Inflammatory indicators, particularly LMR and PDW, were associated with functional outcome after EVT in patients with anterior circulation large vessel occlusion and may provide supplementary information for risk stratification.

Relationship Between Lymphocyte-Associated Inflammatory Markers and Post-Stroke Cognitive Impairment

 ABSOLUTELY USELESS!  Nothing here helps survivors recover. You're all fired for not knowing that the only goal in stroke is 100% recovery

Relationship Between Lymphocyte-Associated Inflammatory Markers and Post-Stroke Cognitive Impairment

Authors Hu QY, Liu J, Cui CH, Guo MF, Shi YT, Zhang XM, Jia BF, Li XY, Sun SJ

Received 10 June 2025

Accepted for publication 6 September 2025

Published 25 September 2025 Volume 2025:18 Pages 13347—13358

DOI https://doi.org/10.2147/JIR.S545953

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Ning Quan

Qian-Ying Hu,^1,* Juan Liu,^1,* Cai-Hong Cui,¹ Mei-Fang Guo,² Yu-Tong Shi,² Xiao-Man Zhang,² Bing-Fei Jia,² Xin-Yu Li,² Su-Juan Sun<sup>3

1</sup>Department of Rehabilitation Medicine, Affiliated Hospital of Hebei University, Baoding, Hebei, 071000, People’s Republic of China; ²Department of Basic Medical Sciences, Hebei University, Baoding, Hebei, 071000, People’s Republic of China; ³Department of Nursing, Hebei General Hospital, Shijiazhuang, Hebei, 050000, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Su-Juan Sun, Department of Nursing, Hebei General Hospital, No. 348 Heping West Road, Xinhua District, Shijiazhuang, Hebei, 050000, People’s Republic of China, Tel +86 13933093071, Email sujuansunssjm@126.com Cai-Hong Cui, Department of Rehabilitation Medicine, Affiliated Hospital of Hebei University, No. 212 of Yuhua East Road, Lianchi District, Baoding, Hebei, 071000, People’s Republic of China, Tel +86 13463236473, Email caihongcuicchk@126.com

Objective: To determine whether differences in lymphocyte-related inflammatory markers in the ultra-early phase of stroke (within 24 hours of onset) are associated with post-stroke cognitive impairment in the early recovery phase (within 30 days of stroke onset), and to further assess the predictive value of these markers.
Methods: The study population consisted of patients who underwent rehabilitation treatment at the Rehabilitation Department of Hebei University Affiliated Hospital between December 2024 and June 2025, within 30 days of stroke onset, ie, during the early recovery phase of stroke. Patients were grouped based on whether they developed cognitive impairment. A retrospective analysis was conducted of patients’ blood markers and neurological deficit scores within 24 hours of stroke onset to examine the relationship between ultra-early blood markers and neurological deficits and post-stroke cognitive impairment.
Results: There were no significant differences in baseline data between the two groups. However, the proportion of hemorrhagic stroke patients was significantly higher in the PSCI group than in the non-PSCI group (39.7% vs 18.8%, P=0.026< 0.05). NLR and NIHSS scores showed significant differences between the two groups. Multivariate analysis indicated that NIHSS (OR=1.297, 95% CI: 1.167– 1.442, p< 0.001) was independently associated with PSCI, while NLR (OR=1.107, 95% CI: 0.995– 1.231, p=0.063) showed a borderline association with PSCI. MLR showed differences between the two groups in univariate analysis (P=0.018) but was excluded in multivariate analysis. ULR did not show significant differences.
Conclusion: NIHSS is a strong predictive factor (P < 0.05), with a cut of value of 12 calculated by the ROC curve. NLR is at the threshold for an independent risk factor. Subsequent ROC curves indicate that NLR has low diagnostic sensitivity but high specificity, making it more suitable for screening rather than diagnostic use. MLR and ULR did not demonstrate high predictive value; further studies should be conducted to expand the sample size, perform subgroup analyses, and increase follow-up.

Keywords: post-stroke cognitive impairment, NIHSS, NLR, MLR, ULR

Background

The 2020 China Stroke Report shows that the prevalence of stroke in China is 1,114.8 per 100,000 people, the annual incidence rate is 246.8 per 100,000 people, and the mortality rate is 149.49 per 100,000 people. China has become the country with the highest lifetime risk of stroke and the heaviest disease burden globally.¹ Post-stroke cognitive impairment (PSCI) refers to a clinical syndrome characterized by cognitive impairment that occurs after a stroke event and persists for up to six months. According to literature reports, approximately one-third of stroke patients experience.² The diagnosis of PSCI is typically based on clinical assessment, neuropsychological assessment, and neuroimaging. The Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) are the most widely used cognitive tests in PSCI research.³ However, both are subject to copyright restrictions, which pose challenges for research. Meanwhile, the new cognitive test Mini-Cog, which is simple to administer and time-efficient, can aid in detecting the early stages of cognitive impairment. In a post-hoc analysis of a population-based study of older adults in the United States, the Mini-Cog scores classified as “possibly impaired” or “possibly normal” using the algorithm were comparable to those of the MMSE with a cutoff point of 25 in terms of sensitivity (76% vs 79%) and specificity (89% vs 88%) for dementia.-Cog, which was scored as “possibly impaired” or “possibly normal” by the algorithm, showed similar sensitivity (76% vs 79%) and specificity (89% vs 88%) for dementia compared to the MMSE with a cutoff point of 25. These results were comparable to those obtained using traditional neuropsychological assessments (sensitivity 75%, specificity 90%).⁴ A meta-analysis indicated that for cognitive impairment (including dementia and mild cognitive impairment) in primary care settings, Mini-Cog showed 73% sensitivity and 84% specificity. In secondary care settings, Mini-Cog showed 73% sensitivity and 76% specificity⁵ All performed well.

In addition to clinical examinations, neuropsychological assessments, and imaging studies, various blood biomarkers have become a focal point of research. By measuring indicators associated with brain injury in the early stages of stroke, it is possible to predict the occurrence of PSCI, which aids in the early identification of PSCI and facilitates further rehabilitation interventions. Inflammatory factors are closely associated with cognitive impairment. Clinically, lymphocyte-related inflammatory markers, such as the neutrophil-lymphocyte ratio (NLR), have been shown to have predictive value in a prospective cohort study of cognitive impairment following ischemic stroke. Peripheral NLR levels are significantly elevated in PSCI patients.³

Another inflammatory marker closely related to lymphocytes—the monocyte-to-lymphocyte ratio (MLR)—is more commonly used in the prediction of cardiovascular disease.⁶ The serum uric acid to lymphocyte ratio (ULR) has been validated as a new lymphocyte-related inflammatory marker with superior predictive value for hemorrhagic stroke compared to uric acid or lymphocyte levels alone in a large prospective cohort study conducted in China.⁷ This study was conducted to further clarify whether differences in inflammatory markers, especially lymphocyte-related inflammatory markers, in the ultra-early stage of stroke (within 24 hours of onset), including hemorrhagic stroke, are associated with post-stroke cognitive impairment in the early recovery stage (within 30 days of stroke onset).⁸

Research Methods

This study is a retrospective study targeting patients who underwent rehabilitation therapy at the Rehabilitation Department of Hebei University Affiliated Hospital between December 2024 and June 2025, specifically those within 30 days post-stroke, ie, in the early recovery phase of stroke. The study population was divided into an observation group (PSCI group) and a control group (non-PSCI group) based on the occurrence of cognitive impairment. A retrospective analysis was conducted of blood markers and neurological deficit scores within 24 hours of stroke onset, to investigate the relationship between ultra-early blood markers and neurological deficits and the occurrence of cognitive impairment after stroke.

Refer to previous literature,³ the inclusion criteria are as follows: (1) age ≥ 18 years; (2) confirmed by cranial computed tomography (CT) or magnetic resonance imaging (MRI);² (3) Patients who have experienced a stroke within the past 30 days, are in the early stages of recovery, have stable conditions, are conscious, can speak fluently, and are able to cooperate with rehabilitation assessments and treatments; (4) Patients who visited the Department of Neurology, Department of Neurosurgery, Department of Critical Care Medicine, or other relevant departments at Hebei University Affiliated Hospital within 24 hours of experiencing a stroke, with complete clinical data and traceable medical records from within 24 hours of the onset of symptoms.

Refer to previous literature,³ the exclusion criteria are as follows: (1) Patients who had been diagnosed with cognitive impairment disorders prior to the stroke, including Alzheimer’s disease, frontotemporal dementia, Parkinson’s disease dementia, Lewy body dementia, mixed dementia, and other types of cognitive impairment or dementia caused by various reasons; (2) Patients unable to cooperate with cognitive function assessments, including those with severe aphasia, hearing impairments, dysarthria, impaired consciousness, visual impairments, or those unable to write normally due to limb dysfunction caused by stroke; (3) Patients with unstable conditions who are unable to cooperate with assessments and rehabilitation therapy, including but not limited to those with severe illnesses such as cardiovascular diseases (eg, severe heart failure, severe arrhythmias), renal diseases (eg, renal failure), pulmonary diseases (eg, severe pneumonia, respiratory failure), etc.; (4) Patients who had conditions prior to the stroke that could trigger inflammatory and immune responses (eg, acute infections, tumors, blood disorders, autoimmune diseases, recent major surgeries, or trauma); (5) Patients who had been taking medications that could interfere with the inflammatory and immune systems prior to the stroke (eg, antibiotics, corticosteroids, immunosuppressants, targeted therapies, etc).; (6) Patients with a history of recurrent strokes.

Based on the Mini-Cog score, patients were divided into a post-stroke cognitive impairment (PSCI) group and a group without post-stroke cognitive impairment. This study complies with the World Medical Association’s Declaration of Helsinki and has been approved by the Ethics Committee of Hebei University Affiliated Hospital (the Ethics Committee of Affiliated Hospital of Hebei University:HDFYLL - KY - 2024 - 120).

Data Collection

Baseline Characteristics

Age, sex, body mass index (BMI), educational attainment (elementary, middle school, university),smoking and alcohol use,were collected.

Clinical Variables

This included past medical history (including coronary heart disease, diabetes, and hypertension, the definition criteria are a previous clear diagnosis of a related disease and/or long-term use of therapeutic drugs prior to onset, stroke type (hemorrhagic or ischemic).

Observation Indicators

Extract and analyze medical records from the acute phase within 24 hours of onset, extracting data on neutrophils, lymphocytes, monocytes, and uric acid, and calculate NLR, MLR, and ULR. Uric acid levels were analyzed using the Fusion biochemical analyzer from Johnson & Johnson, and blood cell counts were measured using the XN-L™ Series blood analyzer from Sysmex Corporation. Stroke severity was assessed using the National Institute of Health Stroke Scale (NIHSS) to evaluate neurological deficit scores.⁹

Cognitive Function Assessment and Grouping

The official Chinese version of the Mini-Cog official website was used to assess the cognitive function of the patients, which was truncated into a control group (no cognitive impairment group) and an observation group (cognitive impairment group) with 4 points.

The sample size calculation formula is:  where the expected prevalence rate (P) is 1/3 (33%) as described in the background section above, the confidence interval is 95%, and the allowable error is 0.1. The minimum sample size calculated is 85 cases, which will be assessed by therapists who are trained and proficient in using the Mini-Cog. A total of 127 cases were ultimately included in this study, 69 in the control group and 58 in the observation group.

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Prognostic assessment of acute ischemic stroke by systemic immune-inflammatory index: a comprehensive meta-analysis of multidimensional outcomes

'Assessments' and 'prognostication' DO NOTHING unless you map EXACT RECOVERY PROTOCOLS TO THEM! This was absolutely useless for getting survivors recovered! Stroke research is to get survivors recovered; you'll want recovery when you are the 1 in 4 per WHO that has a stroke! Just maybe you want to do the proper research now!

WITH NO LEADERSHIP IN STROKE NOTHNING EVER GETS DONE PROPERLY!

 Prognostic assessment of acute ischemic stroke by systemic immune-inflammatory index: a comprehensive meta-analysis of multidimensional outcomes

Yanhong Jiang¹Yifan Cui¹Xiaojie Hu²Jiaying Lian³Xueying Qin⁴Xingchen Wang^1,2*Xuran Ma^1*

• ¹The Second Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
• ²Department of Neurology, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
• ³Department of Neurology, Quzhou Traditional Chinese Medicine Hospital, Quzhou, China
• ⁴The First Clinical Medical College, Beijing University of Traditional Chinese Medicine, Beijing, China

Introduction: Our study aimed to quantify the predictive ability of the Systemic Immune-inflammatory Index (SII) for predicting the prognosis and multidimensional complications in acute ischemic stroke (AIS) patients. The primary outcome was poor prognosis, and secondary outcomes included mortality, severity, hemorrhagic transformation/symptomatic intracerebral hemorrhage, stroke-associated pneumonia/poststroke pneumonia, early neurological deterioration, post-stroke depression, progression or recurrence, and other adverse outcomes.

Methods: We searched 15 databases from their establishment to 13 October 2024 and selected cohort or case-control analyses that analyzed the association of continuous or categorized SII as exposures with the above adverse outcomes of AIS populations.

Results: The results showed that 78 studies with 40,682 participants were included in meta-analyses. Continuous SII values were significantly higher in poor prognosis groups than in controls (SMD = 248.13, 95% CI: 198.77 to 297.50; p = 0.000). Poor prognosis incidences rose with higher continuous SII values (OR = 1.004, 95% CI: 1.002 to 1.005; p = 0.000). More patients in High SII groups had poor prognosis (RR = 1.95, 95% CI: 1.66 to 2.28; p = 0.000). The risk of poor prognosis was higher in the high SII groups, though this was not statistically significant (OR = 1.007, 95% CI: 0.998 to 1.015; p = 0.120).

Discussion: In conclusion, our study found that continuous SII and high SII were associated with poor prognosis of AIS and various complications. Given the accessibility and low cost of SII, integrating it into prognostic scores merits further research for better clinical choices.

Systematic review registration: PROSPERO (CRD42024586414), https://www.crd.york.ac.uk/PROSPERO/view/CRD42024586414.

Machine learning consensus clustering for inflammatory subtype analysis in stroke and its impact on mortality risk: a study based on NHANES (1999–2018)

 Don't tell us what the mortality risk is; REDUCE IT! You blithering idiots can't think at all, can you?

Machine learning consensus clustering for inflammatory subtype analysis in stroke and its impact on mortality risk: a study based on NHANES (1999–2018)

Zhang Chunjuan^*Wang YulongZhou XichengMa Xiaodong^*

• Haiyan People’s Hospital, Jiaxing, Zhejiang, China

Background: Our study aims to utilize unsupervised machine learning methods to perform inflammation clustering on stroke patients via novel CBC-derived inflammatory indicators (NLR, PLR, NPAR, SII, SIRI, and AISI), evaluate the mortality risk among these different clusters and construct prognostic models to provide reference for clinical management.

Methods: A cross-sectional analysis was conducted using data from stroke participants in the U.S. NHANES 1999–2018. Weighted multivariate logistic regression was used to construct different models; consensus clustering methods were employed to subtype stroke patients based on inflammatory marker levels; LASSO regression analysis was used to construct an inflammatory risk score model to analyze the survival risks of different inflammatory subtypes; WQS regression, Cox regression, as well as XGBoost, random forest, and SVMRFE machine learning methods were used to screen hub markers which affected stroke prognosis; finally, a prognostic nomogram model based on hub inflammatory markers was constructed and evaluated using calibration and DCA curves.

Results: A total of 918 stroke patients with a median follow-up of 79 months and 369 deaths. Weighted multivariate logistic regression analysis revealed that high SIRI and NPAR levels were significantly positively correlated with increased all-cause mortality risk in stroke patients (p < 0.001), independent of potential confounders; Consensus clustering divided patients into two inflammatory subgroups via SIRI and NPAR, with subgroup 2 having significantly higher markers and mortality risks than subgroup 1 (p < 0.001); LASSO regression analysis showed subgroup 2 had higher risk scores and shorter overall survival than subgroup 1 [HR, 1.99 (1.61–2.45), p < 0.001]; WQS regression, Cox regression, and machine learning methods identified NPAR and SIRI as hub prognostic inflammatory markers; The nomogram prognostic model with NPAR and SIRI demonstrated the best net benefit for predicting 1, 3, 5 and 10-year overall survival in stroke patients.

Conclusion: This study shows NPAR and SIRI were key prognostic inflammatory markers and positively correlated with mortality risk (p < 0.001) for stroke patients. Patients would been divided into 2 inflammatory subtypes via them, with subtype 2 having higher values and mortality risks (p < 0.001). It suggests that enhanced monitoring and management for patients with high SIRI and NPAR levels to improve survival outcomes.

1 Introduction

Stroke, also termed cerebrovascular accident (CVA), is characterized by rapidly developing neurological deficits due to sudden cerebral blood flow disruption, leading to long-term disability or mortality. As a leading global cause of disability and death (1), stroke affects approximately 15 million individuals annually, with a substantial proportion experiencing recurrent events or persistent functional impairments. The inflammatory cascade plays a dual role in post-stroke pathophysiology, mediating both secondary neuronal injury and repair processes (2). While inflammation has emerged as a promising therapeutic target, the clinical benefits of systemic anti-inflammatory interventions remain controversial. Consequently, stratifying acute ischemic stroke (AIS) patients based on inflammatory heterogeneity may enhance pathophysiological understanding and enable tailored therapeutic modulation of neuroinflammation, thereby optimizing cerebral protection and functional recovery.

Complete blood count (CBC)-derived inflammatory indices—including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), neutrophil percentage-to-albumin ratio (NPAR), systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), and aggregate index of systemic inflammation (AISI)—provide integrative prognostic insights by quantifying interactions among platelets, neutrophils, and lymphocytes. These indices have demonstrated diagnostic and prognostic utility across multiple diseases. In AIS, compelling evidence highlights their clinical relevance: NLR independently predicts 90-day functional outcomes (3), while NLR, PLR, lymphocyte-to-monocyte ratio (LMR), and SIRI correlate with in-hospital mortality, length of stay (4), and 3-month disability rates (5). Notably, these CBC-based biomarkers reflect real-time inflammatory dynamics through routine hematological testing, offering practical advantages over conventional cytokine assays. Their integration into AIS subtyping frameworks may thus facilitate precision medicine by identifying high-risk phenotypes amenable to targeted immunomodulation.

Unsupervised machine learning (ML), a statistical approach that identifies latent patterns by analyzing the underlying structures of unlabeled data, has demonstrated unique value in medical research for classification and risk stratification (6). As a pivotal branch of unsupervised ML, consensus clustering enables precise phenotypic classification by iteratively validating multidimensional feature heterogeneity across patient populations, independent of outcome variables. This methodology has been successfully applied to elucidate disease mechanisms and optimize therapeutic strategies across various conditions (7–10). Notably, its potential is emerging in stroke research. For instance, Yang et al. (11) classified AIS patients into three molecular subtypes based on peripheral blood monocyte transcriptomic profiles, revealing that the high-inflammatory-response subtype exhibited a significantly elevated risk of hemorrhagic transformation, providing critical insights for timing immunomodulatory therapies. Similarly, Cui et al. (12) integrated clinical and neuroimaging data using unsupervised ML to identify a subgroup showing superior therapeutic responses to a “statin combined with repetitive transcranial magnetic stimulation” regimen. However, existing studies have yet to systematically explore the role of CBC (complete blood count)-derived inflammatory indices (e.g., SIRI, NPAR) in AIS subtyping. These indices not only dynamically reflect key inflammatory pathways, such as neutrophil–platelet interactions, but also offer practical advantages for point-of-care testing.

This study applies consensus clustering to resolve inflammatory heterogeneity in AIS, aiming to achieve dual objectives: (1) identifying pathophysiologically meaningful stroke inflammatory subtypes based on CBC-derived inflammatory indices, and (2) constructing interpretable ML prognostic models to quantify mortality risk disparities across subtypes. Ultimately, this work seeks to provide evidence-based guidance for anti-inflammatory therapy optimization and monitoring frequency stratification (e.g., dynamic inflammation monitoring in high-risk subtypes), thereby facilitating the clinical translation of precision medicine in AIS management.

More at link.

Relationship of cerebral microbleeds to inflammatory marker levels

I got nothing out of this article. What problems does this cause? What solutions there are to prevent them? What interventions are needed to resolve the problems caused?
http://nnjournal.net/article/view/1904

Qiao-Li Lu¹, Chen Li¹, Ying Song¹, Liang Wang², Zhi-Rong Jia³

¹Department of Neurology, Tianjin 5th Center Hospital, Tianjin 300450, China.

²Department of Neurosurgery, Tianjin 5th Center Hospital, Tianjin 300450, China.

³Department of Neurology, Peking University First Hospital, Beijing 100034, China.

Correspondence Address: Dr. Chen Li, Department of Neurology, Tianjin 5th Center Hospital, 41 Zhejiang Road, Tanggu, Tianjin 300450, China. 
E-mail: lichenokk@163.com

Dr. Chen Li works in Department of Neurology, Tianjin 5th Center Hospital. She graduated from Tianjin Medical University and got her Master of Medicine in July 2011, and she is skilled in the diagnosis and treatment of cerebrovascular diseases, neuroimmune diseases and anxiety-depression diseases. She enjoys reading and writing as well as looking after her daughter.
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Date of Submission           24-02-2017

Date of Acceptance           10-05-2017

Date of Web Publication    08-08-2017

DOI:10.20517/2347-8659.2017.05

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License (http://creativecommons.org/licenses/by-nc-sa/3.0/), which allows others to remix, tweak and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

How to cite this article:

Lu QL, Li C, Song Y, Wang L, Jia ZR. Relation of cerebral microbleeds to inflammatory marker levels. Neuroimmunol Neuroinflammation 2017;4:145-51.

Abstract

Aim: The purpose of this study is to investigate the incidence, distribution and risk factors of cerebral microbleeds (CMBs) and the relation between CMBs and inflammation in ischemic cerebrovascular disease.  
Methods: Two hundred and one patients without acute infarction or transient ischemic attack were enrolled. The presence and number of CMB were assessed on susceptibility-weighted imaging. The traditional risk factors of CMB were recorded. Levels of high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and matrix metalloproteinase-9 (MMP-9) were tested. Logistic regression analyses were used for multiple-factor analysis of risk factors of CMB.  
Results: Of the 201 patients, 49 (24.38%) had CMB. Multivariate logistic regression analyses showed that the age, the prevalence of hypertension, silent lacunar infarction, white matter lesion, Montreal Cognitive Assessment Score, the using rate of antithrombotic drugs and levels of hs-CRP, IL-6, MMP-9 were the risk factors for CMB. After adjustments for traditional risk factors, inflammatory marker levels remained to be associated with CMBs. The adjusted odd ratios of hs-CRP, IL-6 and MMP-9 were 1.745 (1.342-2.270), 1.223 (1.018-1.533) and 1.284 (1.082-1.423), respectively. Furthermore, inflammatory marker levels were the risk factor for deep or infratentorial CMBs and lobar CMBs.  
Conclusion: The age, prevalence of hypertension, silent lacunar infarction, white matter lesion, MoCA Score, the using rate of antithrombotic drugs and serum hs-CRP, IL-6, and MMP-9 levels were the independent risk factors for CMBs.