A High Neutrophil-to-Lymphocyte Ratio Predicts Higher Risk of Poststroke Cognitive Impairment: Development and Validation of a Clinical Prediction Model

Myeloid cells in vascular dementia and Alzheimer's disease: Possible therapeutic targets?

Growing evidence supports the suggestion that the peripheral immune system plays a role in different pathologies associated with cognitive impairment, such as vascular dementia (VD) or Alzheimer's disease (AD). The aim of this review is to summarize, within the peripheral immune system, the implications of different types of myeloid cells in AD and VD, with a special focus on post-stroke cognitive impairment and dementia (PSCID). We will review the contributions of the myeloid lineage, from peripheral cells (neutrophils, platelets, monocytes and monocyte-derived macrophages) to central nervous system (CNS)-associated cells (perivascular macrophages and microglia). Finally, we will evaluate different potential strategies for pharmacological modulation of pathological processes mediated by myeloid cell subsets, with an emphasis on neutrophils, their interaction with platelets and the process of immunothrombosis that triggers neutrophil-dependent capillary stall and hypoperfusion, as possible effector mechanisms that may pave the way to novel therapeutic avenues to stop dementia, the epidemic of our time. LINKED ARTICLES: This article is part of a themed issue From Alzheimer's Disease to Vascular Dementia: Different Roads Leading to Cognitive Decline. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v181.6/issuetoc.

Prediction of poststroke cognitive impairment based on the systemic inflammatory response index

BACKGROUND

Poststroke cognitive impairment (PSCI) is a prevalent complication among stroke survivors. Although the systemic inflammatory response index (SIRI) has been shown to be a reliable predictor of a variety of inflammatory diseases, the association between the SIRI and PSCI is still unclear. Therefore, the purpose of this study was to investigate the relationship between SIRI and PSCI, and to design a nomogram to predict the risk of PSCI in acute ischemic stroke (AIS) patients.

METHODS

A total of 1342 patients with AIS were included in the study. Using the Mini-Mental State Examination scale, patients were separated into PSCI and non-PSCI groups within 2 weeks of stroke. Clinical data and SIRI values were compared between the groups. We developed the optimal nomogram for predicting PSCI using multivariate logistic regression. Finally, the nomogram was validated using the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA).

RESULTS

In total, 690 (51.4%) patients were diagnosed with PSCI. After adjusting for potential confounders, the SIRI (OR = 1.226, OR: 1.095-1.373, p < .001) was shown to be an independent risk factor for PSCI in the logistic regression analysis. The nomogram based on patient gender, age, admission National Institutes of Health Stroke Scale scores, education, diabetes mellitus, and SIRI had good discriminative ability with an area under the curve (AUC) of 0.716. The calibration curve and Hosmer-Lemeshow test revealed excellent predictive accuracy for the nomogram. Finally, the DCA showed the good clinical utility of the model.

CONCLUSION

Increased SIRI on admission is correlated with PSCI, and the nomogram built with SIRI as one of the predictors can help identify PSCI early.

Relationship between preoperative neutrophil-to-lymphocyte ratio and postoperative delirium: The PNDABLE and the PNDRFAP cohort studies

OBJECTIVES

In this study, the relationship between preoperative neutrophil-to-lymphocyte ratio (NLR) and Alzheimer-related biomarkers in cerebrospinal fluid (CSF) was investigated to determine whether high NLR is a potential risk factor for postoperative delirium (POD) and to evaluate its predictive efficacy.

METHODS

We selected 1000 patients from the perioperative neurocognitive disorder risk factor and prognosis (PNDRFAP) database and 999 patients from the perioperative neurocognitive disorder and biomarker lifestyle (PNDABLE) database. Patients in the PNDABLE database have been measured for Alzheimer-related biomarkers in CSF (Aβ40 , Aβ42 , P-tau, and tau protein). Mini-mental state examination was used to assess the preoperative mental status of patients. POD was diagnosed using the confusion assessment method and assessed for severity using the memorial delirium assessment scale. Logistic regression analysis was utilized to explore the association of preoperative NLR with POD. What's more, we also performed sensitivity analysis by adding corrected confounders, and the results were almost unchanged. Spearman's rank correlation was used to determine the associations between NLR and Alzheimer-related biomarkers. Mediation analyses with 10,000 bootstrapped iterations were used to explore the mediation effects. Finally, we use decision curves and the nomogram model to evaluate the efficacy of preoperative NLR in predicting POD; we also performed external validation using data from Qilu Hospital.

RESULT

Logistic regression results showed that an elevated preoperative NLR was a risk factor for the development of POD in patients (PNDRFAP: OR = 1.067, 95% CI 1.020-1.116; PNDABLE: OR = 1.182, 95% CI 1.048-1.335, p < .05). Spearman's rank correlation analysis showed a positive but weak correlation between NLR and P-tau/T-tau (R = .065). The mediating effect results indicate that NLR likely mediates the occurrence of POD through elevated tau protein levels (proportion: 47.47%). The results of the box plots showed statistically significant NLR and CSF biomarkers between the POD and non-POD (NPOD) groups (p < .05), with higher NLR, P-tau, and T-tau in the POD group than in the NPOD group. In contrast, the NPOD group had higher Aβ42 levels compared to the POD group. In addition, we used R package to plot the decision curve and nomogram both suggesting a good predictive effect of preoperative NLR on the occurrence of POD.

CONCLUSION

Elevated preoperative NLR levels may be a risk factor for POD and likely mediate the development of POD through elevated P-tau/T-tau levels.

Association between the neutrophil-to-lymphocyte ratio and cognitive impairment: a meta-analysis of observational studies

Background

Systemic inflammation is one of the underlying mechanisms of cognitive impairment. The neutrophil-to-lymphocyte ratio (NLR) has emerged as a systemic inflammation indicator. This meta-analysis aimed to evaluate the association between high NLR and cognitive impairment (CI) risk.

Method

A comprehensive systematic search was conducted to identify eligible studies published until May 30, 2023. The reference group comprised patients with the lowest NLR level, whereas the exposure group comprised those with the highest NLR level. The main outcome was to examine the relationship between NLR and CI risk. The secondary outcome included the association between patient characteristics or comorbidities and CI risk.

Results

This meta-analysis included 11 studies published between 2018 and 2023, involving 10,357 patients. Patients with CI had a higher NLR than those without (mean difference=0.35, 95% confidence interval [CI]: 0.26-0.44, p < 00001, I2 = 86%). Consistently, pooled results revealed an association between high NLR and CI risk (odds ratio [OR]=2.53, 95% CI:1.67-3.82, p<0.0001, I2 = 84%). Furthermore, aging (mean difference =4.31 years, 95% CI:2.83-5.8, p < 0.00001, I2 = 92%), diabetes (OR=1.59, 95% CI:1.35-1.88, p < 0.00001, I2 = 66%), and hypertension (OR=1.36, 95% CI:1.19-1.57, p < 0.00001, I2 = 0%) were significant risk factors for CI. However, no significant associations were observed between CI and male gender (OR = 0.84, 95% CI:0.64-1.11, p = 0.22, I2 = 81%), body mass index (mean = -0.32 kg/m2, 95% CI: -0.82, 0.18, p = 0.2, I2 = 82%), alcohol consumption (OR = 1.11, 95% CI:0.95-1.3, p = 1.35, I2 = 0%), and smoking (OR = 0.99, 95% CI:0.87-1.13, p = 0.86, I2 = 0%). Meta-regression found that diabetes and hypertension, but not age, significantly moderated the association between NLR and CI.

Conclusion

This meta-analysis showed a significant association between high NLR and increased CI risk. Moreover, meta-regression identified diabetes and hypertension, but not age, as significant moderating factors in the relationship between NLR and CI. To validate and strengthen these findings, further large-scale studies are required.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023430384, identifier CRD42023430384.

Machine learning in the prediction of post-stroke cognitive impairment: a systematic review and meta-analysis

Objective

Cognitive impairment is a detrimental complication of stroke that compromises the quality of life of the patients and poses a huge burden on society. Due to the lack of effective early prediction tools in clinical practice, many researchers have introduced machine learning (ML) into the prediction of post-stroke cognitive impairment (PSCI). However, the mathematical models for ML are diverse, and their accuracy remains highly contentious. Therefore, this study aimed to examine the efficiency of ML in the prediction of PSCI.

Methods

Relevant articles were retrieved from Cochrane, Embase, PubMed, and Web of Science from the inception of each database to 5 December 2022. Study quality was evaluated by PROBAST, and c-index, sensitivity, specificity, and overall accuracy of the prediction models were meta-analyzed.

Results

A total of 21 articles involving 7,822 stroke patients (2,876 with PSCI) were included. The main modeling variables comprised age, gender, education level, stroke history, stroke severity, lesion volume, lesion site, stroke subtype, white matter hyperintensity (WMH), and vascular risk factors. The prediction models used were prediction nomograms constructed based on logistic regression. The pooled c-index, sensitivity, and specificity were 0.82 (95% CI 0.77-0.87), 0.77 (95% CI 0.72-0.80), and 0.80 (95% CI 0.71-0.86) in the training set, and 0.82 (95% CI 0.77-0.87), 0.82 (95% CI 0.70-0.90), and 0.80 (95% CI 0.68-0.82) in the validation set, respectively.

Conclusion

ML is a potential tool for predicting PSCI and may be used to develop simple clinical scoring scales for subsequent clinical use.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=383476.

Association of Neutrophil-to-Lymphocyte Ratio with the Risk of Fatal Stroke Occurrence in Older Chinese

BACKGROUND

Associations of neutrophil-to-lymphocyte ratio (NLR) and its longitudinal change with risk of fatal strokes are unclear in older populations.

METHODS

In this retrospective analysis, a total of 27,799 participants were included and followed up for a mean of 14.3 years (standard deviation = 3.2). 838 stroke deaths were recorded. Cox proportional hazards regression was used to assess associations of NLR with fatal strokes.

RESULTS

Compared to those in the first quartile and after adjustment for a series of factors, the participants in the highest neutrophil quartile had an increased risk of fatal all stroke (adjusted hazard ratio (aHR) = 1.45, 95% confidence interval (CI), 1.18-1.79) and fatal ischaemic stroke (aHR = 1.58, 95% CI, 1.17-2.12). Restricted cubic splines showed an increased trend of relationship between the NLR and fatal all stroke. The participants with the highest NLR quartile had an increased risk of fatal all stroke (aHR = 1.52, 95% CI, 1.23-1.88) and fatal ischaemic stroke (aHR = 1.59, 95% CI, 1.13-2.26), respectively; Similar associations repeated after further C-reactive protein adjustment; a 21% and a 32% increased risk of fatal all stroke and fatal ischaemic stroke showed in a continuous variable model. Those in NLR change with 5% increase had a 70% increased risk of fatal all stroke (aHR = 1.70, 95%CI, 1.13-2.57), compared to those in stable (-5%∼5%).

CONCLUSIONS

Higher NLR was associated with an increased risk of fatal all stroke and fatal ischaemic stroke, and its longitudinal change increase of ≥ 5% was associated with an increased risk of fatal all stroke in a relatively healthy older population.

The Managing Cancer and Living Meaningfully (CALM) Intervention Alleviates Chemotherapy-Related Cognitive Impairment in Patients with Breast Cancer by Modulating Pan-Immune-Inflammation Values

Background:

The number of patients with breast cancer is increasing worldwide, resulting in a growing number of patients with chemotherapy-related cognitive impairment (CRCI), which seriously affects their quality of life. CRCI is associated with inflammatory factors and systemic inflammatory markers such as pan-immune-inflammation value (PIV) and monocyte-to-lymphocyte ratio (MLR), which can reflect the level of inflammation in the body. While the Managing Cancer and Living Meaningfully (CALM) intervention has been demonstrated to alleviate CRCI in patients with breast cancer, the specific mechanism remains unclear.

Objective:

This study evaluated the impact of the CALM intervention on systemic inflammation.

Methods:

Ninety patients with breast cancer with CRCI were enrolled and randomized into care as usual (CAU) and CALM intervention groups. All patients were assessed using the Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog), Mini-Mental State Exam (MMSE), and Functional Assessment of Cancer Therapy-Breast (FACT-B) before and after the CAU/CALM intervention. The blood levels of inflammatory markers were also analyzed before and after the intervention.

Results:

Compared to the CAU group, the CALM group showed significantly improved cognitive function and significantly decreased PIV ( P < .05). PIV was significantly negatively correlated with FACT-Cog ( P < .05). The levels of other inflammatory markers, including MLR, neutrophil-to-lymphocyte ratio (NLR), granulocyte-to-lymphocyte ratio (GLR), and systemic immune-inflammation index (SII), were also reduced in the CALM group.

Conclusion:

PIV is an important marker of inflammation. The CALM intervention may improve the cognitive function of patients by regulating the systemic inflammation marker PIV through the neuroimmune axis.