Nomogram to Predict Cognitive Dysfunction After a Minor Ischemic Stroke in Hospitalized-Population

Post-stroke cognitive impairment: exploring molecular mechanisms and omics biomarkers for early identification and intervention

Post-stroke cognitive impairment (PSCI) is a major stroke consequence that has a severe impact on patients' quality of life and survival rate. For this reason, it is especially crucial to identify and intervene early in high-risk groups during the acute phase of stroke. Currently, there are no reliable and efficient techniques for the early diagnosis, appropriate evaluation, or prognostication of PSCI. Instead, plenty of biomarkers in stroke patients have progressively been linked to cognitive impairment in recent years. High-throughput omics techniques that generate large amounts of data and process it to a high quality have been used to screen and identify biomarkers of PSCI in order to investigate the molecular mechanisms of the disease. These techniques include metabolomics, which explores dynamic changes in the organism, gut microbiomics, which studies host-microbe interactions, genomics, which elucidates deeper disease mechanisms, transcriptomics and proteomics, which describe gene expression and regulation. We looked through electronic databases like PubMed, the Cochrane Library, Embase, Web of Science, and common databases for each omics to find biomarkers that might be connected to the pathophysiology of PSCI. As all, we found 34 studies: 14 in the field of metabolomics, 5 in the field of gut microbiomics, 5 in the field of genomics, 4 in the field of transcriptomics, and 7 in the field of proteomics. We discovered that neuroinflammation, oxidative stress, and atherosclerosis may be the primary causes of PSCI development, and that metabolomics may play a role in the molecular mechanisms of PSCI. In this study, we summarized the existing issues across omics technologies and discuss the latest discoveries of PSCI biomarkers in the context of omics, with the goal of investigating the molecular causes of post-stroke cognitive impairment. We also discuss the potential therapeutic utility of omics platforms for PSCI mechanisms, diagnosis, and intervention in order to promote the area's advancement towards precision PSCI treatment.

Blood biomarkers for post-stroke cognitive impairment: A systematic review and meta-analysis

BACKGROUND AND PURPOSE

Post-stroke cognitive impairment (PSCI) is a frequent consequence of stroke, which affects the quality of life and prognosis of stroke survivors. Numerous studies have indicated that blood biomarkers may be the key determinants for predicting and diagnosing cognitive impairment, but the results remain varied. Therefore, this meta-analysis aims to summarize potential biomarkers associated with PSCI.

METHODS

PubMed, Web of Science, Embase, and Cochrane Library were comprehensively searched for studies exploring blood biomarkers associated with PSCI from inception to 15 April 2022.

RESULTS

63 studies were selected from 4,047 references, which involves 95 blood biomarkers associated with the PSCI. We meta-analyzed 20 potential blood biomarker candidates, the results shown that the homocysteine (Hcy) (SMD = 0.35; 95 %CI: 0.20-0.49; P < 0.00001), c-reactive protein (CRP) (SMD = 0.49; 95 %CI: 0.20-0.78; P = 0.0008), uric acid (UA) (SMD = 0.41; 95 %CI: 0.06-0.76; P = 0.02), interleukin 6 (IL-6) (SMD = 0.92; 95 % CI: 0.27-1.57; P = 0.005), cystatin C (Cys-C) (SMD = 0.58; 95 %CI: 0.28-0.87; P = 0.0001), creatinine (SMD = 0.39; 95 %CI: 0.23-0.55; P < 0.00001) and tumor necrosis factor alpha (TNF-α) (SMD = 0.45; 95 %CI: 0.08-0.82; P = 0.02) levels were significantly higher in patients with PSCI than in the non-PSCI group.

CONCLUSION

Based on our findings, we recommend that paramedics focus on the blood biomarkers levels of Hcy, CRP, UA, IL-6, Cys-C, creatinine and TNF-α in conjunction with neuroimaging and neuropsychological assessment to assess the risk of PSCI, which may help with early detection and timely preventive measures. At the same time, other potential blood biomarkers should be further validated in future studies.

Stroke and Vascular Cognitive Impairment: The Role of Intestinal Microbiota Metabolite TMAO

The gut microbiome interacts with the brain bidirectionally through the microbiome-gutbrain axis, which plays a key role in regulating various nervous system pathophysiological processes. Trimethylamine N-oxide (TMAO) is produced by choline metabolism through intestinal microorganisms, which can cross the blood-brain barrier to act on the central nervous system. Previous studies have shown that elevated plasma TMAO concentrations increase the risk of major adverse cardiovascular events, but there are few studies on TMAO in cerebrovascular disease and vascular cognitive impairment. This review summarized a decade of research on the impact of TMAO on stroke and related cognitive impairment, with particular attention to the effects on vascular cognitive disorders. We demonstrated that TMAO has a marked impact on the occurrence, development, and prognosis of stroke by regulating cholesterol metabolism, foam cell formation, platelet hyperresponsiveness and thrombosis, and promoting inflammation and oxidative stress. TMAO can also influence the cognitive impairment caused by Alzheimer's disease and Parkinson's disease via inducing abnormal aggregation of key proteins, affecting inflammation and thrombosis. However, although clinical studies have confirmed the association between the microbiome-gut-brain axis and vascular cognitive impairment (cerebral small vessel disease and post-stroke cognitive impairment), the molecular mechanism of TMAO has not been clarified, and TMAO precursors seem to play the opposite role in the process of poststroke cognitive impairment. In addition, several studies have also reported the possible neuroprotective effects of TMAO. Existing therapies for these diseases targeted to regulate intestinal flora and its metabolites have shown good efficacy. TMAO is probably a new target for early prediction and treatment of stroke and vascular cognitive impairment.

Predicting post-stroke cognitive impairment using machine learning: A prospective cohort study

BACKGROUND

Post-stroke cognitive impairment (PSCI) is a serious complication of stroke that warrants prompt detection and management. Consequently, the development of a diagnostic prediction model holds clinical significance.

OBJECTIVE

Machine learning algorithms were employed to identify crucial variables and forecast PSCI occurrence within 3-6 months following acute ischemic stroke (AIS).

METHODS

A prospective study was conducted on a developed cohort (331 patients) utilizing data from the Affiliated Zhongda Hospital of Southeast University between January 2022 and August 2022, as well as an external validation cohort (66 patients) from December 2022 to January 2023. The optimal model was determined by integrating nine machine learning classification models, and personalized risk assessment was facilitated by a Shapley Additive exPlanations (SHAP) interpretation.

RESULTS

Age, education, baseline National Institutes of Health Scale (NIHSS), Cerebral white matter degeneration (CWMD), Homocysteine (Hcy), and C-reactive protein (CRP) were identified as predictors of PSCI occurrence. Gaussian Naïve Bayes (GNB) model was determined to be the optimal model, surpassing other classifier models in the validation set (area under the curve [AUC]: 0.925, 95 % confidence interval [CI]: 0.861 - 0.988) and achieving the lowest Brier score. The GNB model performed well in the test sets (AUC: 0.919, accuracy: 0.864, sensitivity: 0.818, and specificity: 0.932).

CONCLUSIONS

The present study involved the development of a GNB model and its elucidation through employment of the SHAP method. These findings provide compelling evidence for preventing PSCI, which could serve as a guide for high-risk patients to undertake appropriate preventive measures.

TMAO Promotes NLRP3 Inflammasome Activation of Microglia Aggravating Neurological Injury in Ischemic Stroke Through FTO/IGF2BP2

Objective

Stroke is a kind of cerebrovascular disease with high mortality. TMAO has been shown to aggravate stroke outcomes, but its mechanism remains unclear.

Materials and Methods

Mice were fed with 0.12% TMAO for 16 weeks. Then, mice were made into MCAO/R models. Neurological score, infarct volume, neuronal damage and markers associated with inflammation were assessed. Since microglia played a crucial role in ischemic stroke, microglia of MCAO/R mice were isolated for high-throughput sequencing to identify the most differentially expressed gene following TMAO treatment. Afterward, the downstream pathways of TMAO were investigated using primary microglia.

Results

TMAO promoted the release of inflammatory cytokines in the brain of MCAO/R mice and promoted the activation of OGD/R microglial inflammasome, thereby exacerbating ischemic stroke outcomes. FTO/IGF2BP2 inhibited NLRP3 inflammasome activation in OGD/R microglia by downregulating the m6A level of NLRP3. TMAO can inhibit the expression of FTO and IGF2BP2, thus promoting the activation of NLRP3 inflammasome in OGD/R microglia. In conclusion, these results demonstrated that TMAO promotes NLRP3 inflammasome activation of microglia aggravating neurological injury in ischemic stroke through FTO/IGF2BP2.

Conclusion

Our results demonstrated that TMAO promotes NLRP3 inflammasome activation of microglia aggravating neurological injury in ischemic stroke through FTO/IGF2BP2. These findings explained the molecular mechanism of TMAO aggravating ischemic stroke in detail and provided molecular mechanism for clinical treatment.

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.

Outcomes in Patients with Minor Stroke: Diagnosis and Management in the Post-thrombectomy Era

In the era of mechanical thrombectomy and better preventative strategies, a higher number of patients are being discharged home from the hospital with the so-called minor strokes. This has significantly changed the landscape of stroke recovery. Unfortunately, while symptoms may be categorized as mild compared to individuals with higher NIH Stroke Scale scores, the physical, cognitive, and emotional sequelae can be disabling and result in failure to return to work and poor quality of life in a population with significant potential to recover fully. In this review, we discuss the current state of minor stroke, the most common pattern of resulting deficits, what is known about the underlying pathophysiology that leads to a relatively global pattern of impaired cognition following an infarct in any location, and special considerations for treatment based on this population's unique needs. Raising awareness of the current morbidity associated with minor stroke, the need for a uniform definition that allows for comparisons of individuals across studies, and further research focused on this population to optimize outcomes, has the potential to significantly improve recovery.

Nonlinear relationship between glycated hemoglobin and cognitive impairment after acute mild ischemic stroke

BACKGROUND

Stroke is the second most common cause of morbidity and mortality. Even mild stroke survivors have an increased risk of cognitive impairment. Studies have been conducted on the relationship between glycated hemoglobin (HbA1c) and cognitive decline, but the findings have been inconsistent. Therefore, this study examined the link between HbA1c levels and cognitive impairment following acute mild ischemic stroke.

METHODS

Data from 311 patients with acute mild ischemic stroke admitted to Suining Central Hospital, Sichuan Province, China, from January 1, 2015, to December 31, 2018, were evaluated. Fasting venous blood was taken to assess HbA1c levels on the day after admission. Cognitive function was assessed using the Chinese version of the Montreal Cognitive Assessment Scale (MoCA) 3-6 months after stroke onset. We used a generalized additive model and smooth curve fitting (penalty spline method) to assess the nonlinear relationship between HbA1c and poststroke cognitive impairment (PSCI).

RESULTS

This study included 311 patients aged 23 to 96 years old (mean age: 67.37 ± 11.92 years), of whom 198 (63.67%) were men. Among the 311 stroke patients, 120 (38.59%) had PSCI. After adjusting for potential confounders, there was a nonlinear relationship between HbA1c and PSCI, with an inflection point of 8.2. To the left of the inflection point, the effect size, 95% confidence interval, and P value were 0.87, 0.58 to 1.31, and 0.5095, respectively; however, to the right of the inflection point, these numbers were 1.96, 1.08 to 3.58, and 0.0280.

CONCLUSION

We found a nonlinear relationship between HbA1c and PSCI. When HbA1c was greater than 8.2%, HbA1c was positively correlated with PSCI.

Association between plasma trimethylamine-N-oxide and cognitive impairment in patients with transient ischemic attack

BACKGROUND

There is increasing evidence for the association of trimethylamine-N-oxide (TMAO) with cognitive impairment after minor stroke or transient ischemic attack (TIA). However, how TMAO affects cognitive function in TIA patients has seldom been studied.

METHODS

A total of 310 TIA participants were retrospectively collected from our stroke register between January 2020 and July 2021. Plasma TMAO was measured by liquid chromatography‒mass spectrometry at baseline. Cognitive performance was assessed by neuropsychological evaluation at 3 months after TIA onset.

RESULTS

A total of 310 patients were included (mean age, 74 years; male, 160 [51.6%]; mean ABCD2 score, 2.6). TMAO was positively associated with cognitive impairment after TIA (aOR, 1.423; 95% CI, 1.125-2.561). The highest quartile of TMAO was related to an almost 2-fold increased risk of cognitive decline compared to the lowest quartile. Furthermore, executive and memory function were more susceptible to impairment after TIA in groups with higher levels of TMAO. Mediation analysis revealed that the overall mediated effect was-0.347 (p < 0.001), and the intermediary effect of CRP was-0.108.

CONCLUSION

Plasma TMAO at baseline was independently associated with cognitive impairment at the 3-month follow-up after TIA. In addition, the inflammatory marker CRP may serve as an important mediator in this relationship. Our study may provide some insights into anti-inflammatory therapy to improve the cognitive trajectory of TIA patients with high TMAO levels.

Trimethylamine-N-oxide: a potential biomarker and therapeutic target in ischemic stroke

Ischemic stroke is by far the most common cerebrovascular disease and a major burden to the global economy and public health. Trimethylamine-N-oxide (TMAO), a small molecule compound produced by the metabolism of intestinal microorganisms, is reportedly associated with the risk of stroke, as well as the severity and prognosis of stroke; however, this conclusion remains contentious. This article reviews the production of TMAO, TMAO's relationship with different etiological types of ischemic stroke, and the possibility of reducing TMAO levels to improve the prognosis of ischemic stroke.

Prediction of long-term mortality in patients with ischemic stroke based on clinical characteristics on the first day of ICU admission: An easy-to-use nomogram

Background

This study aimed to establish and validate an easy-to-use nomogram for predicting long-term mortality among ischemic stroke patients.

Methods

All raw data were obtained from the Medical Information Mart for Intensive Care IV database. Clinical features associated with long-term mortality (1-year mortality) among ischemic stroke patients were identified using least absolute shrinkage and selection operator regression. Then, binary logistic regression was used to construct a nomogram, the discrimination of which was evaluated by the concordance index (C-index), integrated discrimination improvement (IDI), and net reclassification index (NRI). Finally, a calibration curve and decision curve analysis (DCA) were employed to study calibration and net clinical benefit, compared to the Glasgow Coma Scale (GCS) and the commonly used disease severity scoring system.

Results

Patients who were identified with ischemic stroke were randomly assigned into developing (n = 1,443) and verification (n = 646) cohorts. The following factors were associated with 1-year mortality among ischemic stroke patients, including age on ICU admission, marital status, underlying dementia, underlying malignant cancer, underlying metastatic solid tumor, heart rate, respiratory rate, oxygen saturation, white blood cells, anion gap, mannitol injection, invasive mechanical ventilation, and GCS. The construction of the nomogram was based on the abovementioned features. The C-index of the nomogram in the developing and verification cohorts was 0.820 and 0.816, respectively. Compared with GCS and the commonly used disease severity scoring system, the IDI and NRI of the constructed nomogram had a statistically positive improvement in predicting long-term mortality in both developing and verification cohorts (all with p < 0.001). The actual mortality was consistent with the predicted mortality in the developing (p = 0.862) and verification (p = 0.568) cohorts. Our nomogram exhibited greater net clinical benefit than GCS and the commonly used disease severity scoring system.

Conclusion

This proposed nomogram has good performance in predicting long-term mortality among ischemic stroke patients.

Assessment of cognitive impairment after acute cerebral infarction with T1 relaxation time measured by MP2RAGE sequence and cerebral hemodynamic by transcranial Doppler

Objective

This study aimed to investigate early brain microstructural changes discovered using magnetization-prepared two rapid acquisition gradient echo (MP2RAGE) sequence and cerebral hemodynamic using TCD for cognitive impairment after acute cerebral infarction.

Methods

We enrolled 43 patients with acute cerebral infarction and 21 healthy people in the study, who were subjected to cognitive assessments, the MP2RAGE sequence, and a cerebral hemodynamic examination. A total of 26 brain regions of interest were investigated. Furthermore, we used cerebral hemodynamics to explain brain microstructural changes, which helped us better understand the pathophysiology of cognitive impairment after acute cerebral infarction and guide treatment.

Results

T1 relaxation times in the left frontal lobe, right frontal lobe, right temporal lobe, left precuneus, left thalamus, right hippocampus, right head of caudate nucleus, and splenium of corpus callosum were substantially different across the three groups, which were significantly correlated with neuropsychological test scores. CI group patients had significantly lower cerebral blood flow velocity than those in the N-CI and Normal groups. The receiver operating curve analysis revealed that most T1 relaxation times had high sensitivity and specificity, especially on the right temporal lobe and right frontal lobe. There was a potential correlation between T1 relaxation times and MMSE scores through TCD parameters.

Conclusion

The MP2RAGE sequence can detect alterations in whole brain microstructure in patients with cognitive impairment after acute cerebral infarction. Brain microstructural changes could influence cognitive function through cerebral hemodynamics. T1 relaxation times on the right temporal lobe and the right frontal lobe are expected to be a prospective biomarker of cognitive impairment after acute cerebral infarction.

Clinical significance and potential role of trimethylamine N-oxide in neurological and neuropsychiatric disorders

In the past three decades, research on the gut microbiome and its metabolites, such as trimethylamines (TMA), trimethylamine N-oxide (TMAO), short-chain fatty acids (SCFAs), branched-chain amino acids (BCAAs), bile acids, tryptophan and indole derivatives, has attracted the attention of many scientists and industrialists. Among these metabolites, TMAO is produced from dietary choline, phosphatidylcholine, carnitine,andbetaine. TMAO and other gut metabolites, such as TMA and SCFAs, reach the brain by crossing the blood-brain barrier (BBB) and are involved in brain development, neurogenesis, and behavior. Gut-microbiota composition is influenced by diet, lifestyle, antibiotics, and age. Several studies have confirmed that altered TMAO levels contribute to metabolic, vascular, psychiatric, and neurodegenerative disorders. This review focuses on how altered TMAO levels impact oxidative stress, microglial activation, and the apoptosis of neurons, and may lead to neuroinflammation, which can subsequently result in the development of psychiatric, cognitive, and behavioral disorders.

A nomogram incorporating red blood cell indices to predict post-stroke cognitive impairment in the intracerebral hemorrhage population

Background

Post-stroke cognitive impairment (PSCI) plagues 20–80% of stroke survivors worldwide. There is a lack of an easy and effective scoring tool to predict the risk of PSCI in intracerebral hemorrhage (ICH) patients. We aimed to develop a risk prediction model incorporating red blood cell (RBC) indices to identify ICH populations at risk of PSCI.

Methods

Patients diagnosed with ICH at the stroke center were consecutively enrolled in the study as part of the development cohort from July 2017 to December 2018, and of the validation cohort from July 2019 to February 2020. Univariable and multivariable analyses were applied in the development cohort to screen the patients for PSCI risk factors. Then, a nomogram based on RBC indices and other risk factors was developed and validated to evaluate its performance in predicting PSCI occurrence.

Results

A total of 123 patients were enrolled in the development cohort, of which 69 (56.1%) were identified as PSCI, while 38 (63.3%) of 60 patients in the validation cohort were identified as PSCI. According to the multivariate analysis, seven independent risk factors, including three RBC indices (hemoglobin, mean corpuscular volume, RBC distribution width), as well as age, education level, hematoma volume, and dominant-hemisphere hemorrhage were incorporated into the model. The nomogram incorporating RBC indices displayed good discrimination and calibration. The area under the receiver operating characteristic curve was 0.940 for the development cohort and 0.914 for the validation cohort. Decision curve analysis and clinical impact curve showed that the nomogram was clinically useful.

Conclusion

RBC indices are independent and important predictors of PSCI. A nomogram incorporating RBC indices can be used as a reasonable and reliable graphic tool to help clinicians identify high cognition impairment-risk patients and adjust individualized therapy.

Association of Trimethylamine N-Oxide with Normal Aging and Neurocognitive Disorders: A Narrative Review

Aging-related neurocognitive disorder (NCD) is a growing health concern. Trimethylamine-N-oxide (TMAO), a gut microbiota-derived metabolite from dietary precursors, might emerge as a promising biomarker of cognitive dysfunction within the context of brain aging and NCD. TMAO may increase among older adults, Alzheimer’s disease patients, and individuals with cognitive sequelae of stroke. Higher circulating TMAO would make them more vulnerable to age- and NCD-related cognitive decline, via mechanisms such as promoting neuroinflammation and oxidative stress, and reducing synaptic plasticity and function. However, these observations are contrary to the cognitive benefit reported for TMAO through its positive effects on blood–brain barrier integrity, as well as from the supplementation of TMAO precursors. Hence, current disputable evidence does not allow definite conclusions as to whether TMAO could serve as a critical target for cognitive health. This article provides a comprehensive overview of TMAO documented thus far on cognitive change due to aging and NCD.