Brain Bioenergetics in Chronic Hypertension: Risk Factor for Acute Ischemic Stroke

Machine learning approaches to identify the link between heavy metal exposure and ischemic stroke using the US NHANES data from 2003 to 2018

Purpose

There is limited understanding of the link between exposure to heavy metals and ischemic stroke (IS). This research aimed to develop efficient and interpretable machine learning (ML) models to associate the relationship between exposure to heavy metals and IS.

Methods

The data of this research were obtained from the National Health and Nutrition Examination Survey (US NHANES, 2003-2018) database. Seven ML models were used to identify IS caused by exposure to heavy metals. To assess the strength of the models, we employed 10-fold cross-validation, the area under the curve (AUC), F1 scores, Brier scores, Matthews correlation coefficient (MCC), precision-recall (PR) curves, and decision curve analysis (DCA) curves. Following these tests, the best-performing model was selected. Finally, the DALEX package was used for feature explanation and decision-making visualization.

Results

A total of 15,575 participants were involved in this study. The best-performing ML models, which included logistic regression (LR) (AUC: 0.796) and XGBoost (AUC: 0.789), were selected. The DALEX package revealed that age, total mercury in blood, poverty-to-income ratio (PIR), and cadmium were the most significant contributors to IS in the logistic regression and XGBoost models.

Conclusion

The logistic regression and XGBoost models showed high efficiency, accuracy, and robustness in identifying associations between heavy metal exposure and IS in NHANES 2003-2018 participants.

Predictors of early neurological deterioration in patients with acute ischemic stroke

Background

The present study aimed to develop a reliable and straightforward Nomogram by integrating various parameters to accurately predict the likelihood of early neurological deterioration (END) in patients with acute ischemic stroke (AIS).

Methods

Acute ischemic stroke patients from Shaoxing People's Hospital, Shanghai Yangpu District Shidong Hospital, and Shanghai Fifth People's Hospital were recruited based on specific inclusion and exclusion criteria. The primary outcome was END. Using the LASSO logistic model, a predictive Nomogram was generated. The performance of the Nomogram was evaluated using the ROC curve, the Hosmer-Lemeshow test, and a calibration plot. Additionally, the decision curve analysis was conducted to assess the effectiveness of the Nomogram.

Results

It was found that the Nomogram generated in the present study showed strong discriminatory performance in both the training and the internal validation cohorts when their ROC-AUC values were 0.715 (95% CI 0.648-0.782) and 0.725 (95% CI 0.631-0.820), respectively. Similar results were observed in two external validation cohorts when their ROC-AUC values were 0.685 (95% CI 0.541-0.829) and 0.673 (95% CI 0.545-0.800), respectively. In addition, CAD, SBP, neutrophils, TBil, and LDL were found to be positively correlated with the occurrence of END post-stroke, while lymphocytes and UA were negatively correlated.

Conclusion

Our study developed a novel Nomogram that includes CAD, SBP, neutrophils, lymphocytes, TBil, UA, and LDL and it demonstrated strong discriminatory performance in identifying AIS patients who are likely to develop END.

Effects of Chronic Hypertension on the Energy Metabolism of Cerebral Cortex Mitochondria in Normotensive and in Spontaneously Hypertensive Rats During Aging

In this study the subcellular modifications undergone by cerebral cortex mitochondrial metabolism in chronic hypertension during aging were evaluated. The catalytic properties of regulatory energy-linked enzymes of Tricarboxylic Acid Cycle (TCA), Electron Transport Chain (ETC) and glutamate metabolism were assayed on non-synaptic mitochondria (FM, located in post-synaptic compartment) and on intra-synaptic mitochondria of pre-synaptic compartment, furtherly divided in "light" (LM) and "heavy" (HM) mitochondria, purified form cerebral cortex of normotensive Wistar Kyoto Rats (WKY) versus Spontaneously Hypertensive Rats (SHR) at 6, 12 and 18 months. During physiological aging, the metabolic machinery was differently expressed in pre- and post-synaptic compartments: LM and above all HM were more affected by aging, displaying lower ETC activities. In SHR at 6 months, FM and LM showed an uncoupling between TCA and ETC, likely as initial adaptive response to hypertension. During pathological aging, HM were particularly affected at 12 months in SHR, as if the adaptive modifications in FM and LM at 6 months granted a mitochondrial functional balance, while at 18 months all the neuronal mitochondria displayed decreased metabolic fluxes versus WKY. This study describes the effects of chronic hypertension on cerebral mitochondrial energy metabolism during aging through functional proteomics of enzymes at subcellular levels, i.e. in neuronal soma and synapses. In addition, this represents the starting point to envisage an experimental physiopathological model which could be useful also for pharmacological studies, to assess drug actions during the development of age-related pathologies that could coexist and/or are provoked by chronic hypertension.

Designing machine learning for big data: A study to identify factors that increase the risk of ischemic stroke and prognosis in hypertensive patients

Background

Ischemic stroke (IS) accounts large amount of stroke incidence. The aim of this study was to discover the risk and prognostic factors that affecting the occurrence of IS in hypertensive patients.

Method

Study data were obtained from the Medical Information Mart for Intensive Care (MIMIC)-IV database. To avoid biased factors selection process, several approaches were studied including logistic regression, elastic net regression, random forest, correlation analysis, and multifactor logistic regression methods. And seven different machine-learning methods are used to construct predictive models. The performance of the developed models was evaluated using AUC (Area Under the Curve), prediction accuracy, precision, recall, F1 score, PPV (Positive Predictive Value) and NPV (Negative Predictive Value). Interaction analysis was conducted to explore potential relationships between influential factors.

Results

The study included 92,514 hypertensive patients, of which 1746 hypertensive patients experienced IS. The Gradient Boosted Decision Tree (GBDT) model outperformed the other prediction model terms of prediction accuracy and AUC values in both ischemic and prognosis cases. By using the SHapley Additive exPlanations (SHAP), we found that a range of factors and corresponding interactions between factors are important risk factors for IS and its prognosis in hypertensive patients.

Conclusion

The study identified factors that increase the risk of IS and poor prognosis in hypertensive patients, which may provide guidance for clinical diagnosis and treatment.

Identification of hypoxia-related genes and exploration of their relationship with immune cells in ischemic stroke

Ischemic stroke (IS) is a major threat to human health, and it is the second leading cause of long-term disability and death in the world. Impaired cerebral perfusion leads to acute hypoxia and glucose deficiency, which in turn induces a stroke cascade response that ultimately leads to cell death. Screening and identifying hypoxia-related genes (HRGs) and therapeutic targets is important for neuroprotection before and during brain recanalization to protect against injury and extend the time window to further improve functional outcomes before pharmacological and mechanical thrombolysis. First, we downloaded the GSE16561 and GSE58294 datasets from the NCBI GEO database. Bioinformatics analysis of the GSE16561 dataset using the limma package identified differentially expressed genes (DEGs) in ischemic stroke using adj. p. values < 0.05 and a fold change of 0.5 as thresholds. The Molecular Signature database and Genecards database were pooled to obtain hypoxia-related genes. 19 HRGs associated with ischemic stroke were obtained after taking the intersection. LASSO regression and multivariate logistic regression were applied to identify critical biomarkers with independent diagnostic values. ROC curves were constructed to validate their diagnostic efficacy. We used CIBERSORT to analyze the differences in the immune microenvironment between IS patients and controls. Finally, we investigated the correlation between HRGs and infiltrating immune cells to understand molecular immune mechanisms better. Our study analyzed the role of HRGs in ischemic stroke. Nineteen hypoxia-related genes were obtained. Enrichment analysis showed that 19 HRGs were involved in response to hypoxia, HIF-1 signaling pathway, autophagy, autophagy of mitochondrion, and AMPK signaling pathway. Because of the good diagnostic properties of SLC2A3, we further investigated the function of SLC2A3 and found that it is closely related to immunity. We have also explored the relevance of other critical genes to immune cells. Our findings suggest that hypoxia-related genes play a crucial role in the diversity and complexity of the IS immune microenvironment. Exploring the association between hypoxia-related critical genes and immune cells provides innovative insights into the therapeutic targets for ischemic stroke.

Genetic Variants of CYP4F2 Associated with Ischemic Stroke Susceptibility in the Han Population from Southern China

Background

The pathophysiological mechanism of ischemic stroke is complex. Traditional risk factors cannot fully or only partially explain the occurrence and development of IS. Genetic factors are getting more and more attention. Our study aimed to explore the association between CYP4F2 gene polymorphism and susceptibility to IS.

Methods

A total of 1322 volunteers were enrolled to perform an association analysis through SNPStats online software. Using FPRP (false-positive report probability) to detect whether the result is a noteworthy finding. The interaction of SNP-SNP in IS risk was assessed by multi-factor dimensionality reduction. Statistical analysis of this study was mainly completed by SPSS 22.0 software.

Results

Mutant allele "A" (OR = 1.24) and genotype "AA" (OR = 1.49) or "GA" (OR = 1.26) of CYP4F2-rs2108622 are risk genetic factors for IS. Rs2108622 is significantly associated with an increased risk of IS among subjects who are females, aging >60 years old, with BMI ≥24 kg/m², and smoking or drinking volunteers. CYP4F2-rs3093106 and -rs3093105 are associated with susceptibility to IS among smoking, drinking subjects, or IS patients complicated with hypertension.

Conclusion

CYP4F2-rs2108622, -rs3093106, and -rs3093105 are associated with an increased risk of IS.

Construction of a Nomogram Prediction Model for Prognosis in Patients with Large Artery Occlusion-Acute Ischemic Stroke

BACKGROUND

Patients with large artery occlusion-acute ischemic stroke (LAO-AIS) can experience adverse outcomes, such as brain herniation due to complications. This study aimed to construct a nomogram prediction model for prognosis in patients with LAO-AIS in order to maximize the benefits for clinical patients.

METHODS

Retrospective analysis of 243 patients with LAO-AIS from January 2019 to January 2022 with medical history data and blood examination at admission. Univariate and multivariate analyses were conducted through binary logistic regression equation analysis, and a nomogram prediction model was constructed.

RESULTS

Results of this study showed that hyperlipidemia (odds ratio [OR] = 2.849, 95% confidence interval [CI] = 1.100-7.375, P = 0.031), right cerebral infarction (OR = 2.144, 95% CI = 1.106-4.156, P = 0.024), D-Dimer>500 ng/mL (OR = 2.891, 95% CI = 1.398-5.980, P = 0.004), and neutrophil-lymphocyte ratio >7.8 (OR = 2.149, 95% CI = 1.093-4.225, P = 0.027) were independent risk factors for poor early prognosis in patients with LAO-AIS. In addition, hypertension (OR = 1.947, 95% CI = 1.114-3.405, P = 0.019), hyperlipidemia (OR = 2.594, 95% CI = 1.281-5.252, P = 0.008), smoking (OR = 2.414, 95% CI = 1.368-4.261, P = 0.002), D-dimer>500 ng/mL (OR = 3.170, 95% CI = 1.533-6.553, P = 0.002), and neutrophil-lymphocyte ratio >7.8 (OR = 2.144, 95% CI = 1.231-3.735, P = 0.007) were independent risk factors for poor long-term prognosis. The early prognosis nomogram receiver operating characteristic curve area under the curve value was 0.688 for the training set and 0.805 for the validation set, which was highly differentiated. The mean error was 0.025 for the training set calibration curve and 0.016 for the validation set calibration curve. Both the training and validation set decision curve analyses indicated that the clinical benefit of the nomogram was significant. The long-term prognosis nomogram receiver operating characteristic curve area under the curve values was 0.697 for the training set and 0.735 for the validation set, showing high differentiation. The mean error was 0.041 for the training set calibration curve and 0.021 for the validation set calibration curve. Both of the training and validation set decision curve analyses demonstrated a substantial clinical benefit of the nomogram.

CONCLUSIONS

The nomogram prediction model based on admission history data and blood examination are easy-to-use tools that provide an accurate individualized prediction for patients with LAO-AIS and can assist in early clinical decisions and in obtaining an early prognosis.