Early Warning of Ischemic Stroke Based on Atherosclerosis Index Combined With Serum Markers

Lipidomic analyses reveal potential biomarkers for predicting death and heart failure after acute myocardial infarction

Background Acute myocardial infarction (AMI) and postmyocardial infarction heart failure (pMIHF) have high mortality rates worldwide. This study aimed to explore lipidomic profiles and identify potential biomarkers for the prediction of death and heart failure (HF) after AMI. Methods All serum samples were collected at Xuanwu Hospital, Capital Medical University, and their clinical characteristics and lipidomic profiles were analyzed in different groups. LC-MS/MS was used for lipidomic analyses, and underlying biomarkers were screened by receiver operating characteristic (ROC) curve analysis. Results Lipidomic analyses of the survival and nonsurvival groups revealed that the decrease of the content of SM (d18:1/22:0), PE (P-20:1/18:0), PC (18:2), LPE (18:2), PE (P-20:0/18:0), LPC (18:0) and PC (20:0/20:3) while increase of the content of PG (18:1/18:1) could increase the risk of death after AMI. In parallel, the lipidomic analysis of the HF and non-HF groups revealed that the decrease of the content of PC (20:3/20:4), LPC (20:3), LPC (18:0), LPC (18:2), LPC (20:0), LPC (18:3), LPE (16:1) and PC (18:2/20:3) could increase the risk of HF after AMI. Conclusion Several lipids could be potential biomarkers for the prediction of death and HF after AMI.

Metabolomic discoveries for early diagnosis and traditional Chinese medicine efficacy in ischemic stroke

Ischemic stroke (IS), a devastating cerebrovascular accident, presents with high mortality and morbidity. Following IS onset, a cascade of pathological changes, including excitotoxicity, inflammatory damage, and blood-brain barrier disruption, significantly impacts prognosis. However, current clinical practices struggle with early diagnosis and identifying these alterations. Metabolomics, a powerful tool in systems biology, offers a promising avenue for uncovering early diagnostic biomarkers for IS. By analyzing dynamic metabolic profiles, metabolomics can not only aid in identifying early IS biomarkers but also evaluate Traditional Chinese Medicine (TCM) efficacy and explore its mechanisms of action in IS treatment. Animal studies demonstrate that TCM interventions modulate specific metabolite levels, potentially reflecting their therapeutic effects. Identifying relevant metabolites in cerebral ischemia patients holds immense potential for early diagnosis and improved outcomes. This review focuses on recent metabolomic discoveries of potential early diagnostic biomarkers for IS. We explore variations in metabolites observed across different ages, genders, disease severity, and stages. Additionally, the review examines how specific TCM extracts influence IS development through metabolic changes, potentially revealing their mechanisms of action. Finally, we emphasize the importance of integrating metabolomics with other omics approaches for a comprehensive understanding of IS pathophysiology and TCM efficacy, paving the way for precision medicine in IS management.

Predictors of High Cardiovascular Risk Among Nonobese Patients with Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease in a Chinese Population

Purpose

This study aims to investigate cardiovascular risk factors in nonobese patients with type 2 diabetes (T2DM) and non-alcoholic fatty liver disease (NAFLD) and to determine whether they might be used to predict high-risk individuals effectively.

Patients and Methods

This cross-sectional study included 245 nonobese patients with T2DM who underwent FibroTouch in the National Metabolic Management Center of the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University from January 2021 to December 2022. All individuals were divided into NAFLD and non-NAFLD groups. Patients with NAFLD were further grouped by UAP tertiles (T1, T2 and T3). We created a Cardiovascular Score (total scale: 0-5 points; ≥3 points was defined as high-risk individual) based on baPWV, carotid ultrasound, and urinary microalbumin creatinine ratio (UA/CR) to assess the risk of cardiovascular disease in non-obese T2DM patients with NAFLD. Risk factors were evaluated using univariate and multivariate analysis. The performance of risk factors was compared according to the area under the receiver operating characteristic (ROC) curve.

Results

Atherogenic index of plasma (AIP), atherosclerosis index (AI), prevalence of hypertension, body mass index (BMI) and homeostatic model assessment of insulin resistance (HOMA-IR) were higher in the NAFLD group compared to the non-NAFLD group. In T3 group, AIP, AI, BMI and HOMA-IR were higher than those of T1 group. Multivariate logistic regression showed that age, systolic blood pressure, low-density lipoprotein-cholesterol (LDL-C) and AIP were risk factors for cardiovascular disease among nonobese patients with T2DM and NAFLD. The area under the ROC curve for age, systolic blood pressure, LDL-C and AIP were 0.705, 0.688, 0.738 and 0.642, respectively. The area under the ROC curve was 0.895 when combining them.

Conclusion

Age, systolic blood pressure, AIP and LDL-C are all independent risk factors for cardiovascular disease in non-obese individuals with T2DM and NAFLD, which can be combined to identify high-risk populations and carry out intervention.

Interacting and joint effects of triglyceride-glucose index (TyG) and body mass index on stroke risk and the mediating role of TyG in middle-aged and older Chinese adults: a nationwide prospective cohort study

BACKGROUND

Individuals who are overweight or obese often develop insulin resistance, mediation of the association between body mass index (BMI) and stroke risk through the triglyceride-glucose index (TyG) seems plausible but has not been investigated. This study aims to examine whether TyG mediates associations of BMI with stroke risk and the extent of interaction or joint relations of TyG and BMI with stroke outcome.

METHODS

The China Health and Retirement Longitudinal Study, initiated in 2011, is a nationally representative, ongoing prospective cohort study involving 8 231 middle-aged and older Chinese adults without a stroke history at baseline. Exposures examined include BMI and the TyG, the latter being the logarithmized product of fasting triglyceride and glucose concentrations. The primary study outcome is stroke incidence, as determined through self-reports, with a follow-up period extending from June 1, 2011, to June 30, 2018.

RESULTS

Of the 8 231 participants, 3 815 (46.3%) were men; mean (SD) age was 59.23 (9.32) years. During a median follow-up of 7.1 years, 585 (7.1%) participants developed stroke. The TyG was found to mediate the association between BMI and incident stroke, proportions mediated were 16.3% for BMI in the 24.0-27.9 kg/m2 group and 53.8% for BMI ≥ 28.0 kg/m2 group. No significant multiplicative and additive interactions were found between BMI and TyG on incident stroke (Additive: RERI = 1.78, 95% CI - 1.29-4.86; Multiplicative, HR = 1.40, 95% CI 0.86-2.27). HRs for individuals with BMI ≥ 28.0 kg/m2 and quartile 4 of TyG compared with those with BMI < 24.0 kg/m2 and quartile 1 of TyG were 2.05 (95% CI 1.37-3.06) for incident stroke. Combining BMI and TyG enhanced predictive performance for stroke when compared to their individual (AUCBMI+TyG vs AUCBMI vs AUCTyG, 0.602 vs 0.581 vs 0.583).

CONCLUSIONS

TyG appeared to be associated with stroke risk and mediates more than 50% of the total association between BMI and stroke in middle-aged and older Chinese adults. Public health efforts aiming at the reduction of body weight might decrease the stroke risk due to insulin resistance and the burden of stroke.

Plasma lipidomic profiling reveals six candidate biomarkers for the prediction of incident stroke in patients with hypertension

INTRODUCTION

The burden of stroke in patients with hypertension is very high, and its prediction is critical.

OBJECTIVES

We aimed to use plasma lipidomics profiling to identify lipid biomarkers for predicting incident stroke in patients with hypertension.

METHODS

This was a nested case-control study. Baseline plasma samples were collected from 30 hypertensive patients with newly developed stroke, 30 matched patients with hypertension, 30 matched patients at high risk of stroke, and 30 matched healthy controls. Lipidomics analysis was performed by ultrahigh-performance liquid chromatography-tandem mass spectrometry, and differential lipid metabolites were screened using multivariate and univariate statistical methods. Machine learning methods (least absolute shrinkage and selection operator, random forest) were used to identify candidate biomarkers for predicting stroke in patients with hypertension.

RESULTS

Co-expression network analysis revealed that the key molecular alterations of the lipid network in stroke implicate glycerophospholipid metabolism and choline metabolism. Six lipid metabolites were identified as candidate biomarkers by multivariate statistical and machine learning methods, namely phosphatidyl choline(40:3p)(rep), cholesteryl ester(20:5), monoglyceride(29:5), triglyceride(18:0p/18:1/18:1), triglyceride(18:1/18:2/21:0) and coenzyme(q9). The combination of these six lipid biomarkers exhibited good diagnostic and predictive ability, as it could indicate a risk of stroke at an early stage in patients with hypertension (area under the curve = 0.870; 95% confidence interval: 0.783-0.957).

CONCLUSIONS

We determined lipidomic signatures associated with future stroke development and identified new lipid biomarkers for predicting stroke in patients with hypertension. The biomarkers have translational potential and thus may serve as blood-based biomarkers for predicting hypertensive stroke.

Blood-Derived Lipid and Metabolite Biomarkers in Cardiovascular Research from Clinical Studies: A Recent Update

The primary prevention, early detection, and treatment of cardiovascular disease (CVD) have been long-standing scientific research goals worldwide. In the past decades, traditional blood lipid profiles have been routinely used in clinical practice to estimate the risk of CVDs such as atherosclerotic cardiovascular disease (ASCVD) and as treatment targets for the primary prevention of adverse cardiac events. These blood lipid panel tests often fail to fully predict all CVD risks and thus need to be improved. A comprehensive analysis of molecular species of lipids and metabolites (defined as lipidomics and metabolomics, respectively) can provide molecular insights into the pathophysiology of the disease and could serve as diagnostic and prognostic indicators of disease. Mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based lipidomics and metabolomics analysis have been increasingly used to study the metabolic changes that occur during CVD pathogenesis. In this review, we provide an overview of various MS-based platforms and approaches that are commonly used in lipidomics and metabolomics workflows. This review summarizes the lipids and metabolites in human plasma/serum that have recently (from 2018 to December 2022) been identified as promising CVD biomarkers. In addition, this review describes the potential pathophysiological mechanisms associated with candidate CVD biomarkers. Future studies focused on these potential biomarkers and pathways will provide mechanistic clues of CVD pathogenesis and thus help with the risk assessment, diagnosis, and treatment of CVD.

Associations Between Atherosclerosis and Elevated Serum Alkaline Phosphatase in Patients With Coronary Artery Disease in an Inflammatory State

BACKGROUND AND AIM

Serum alkaline phosphatase (ALP) has been shown to be associated with cardiovascular disease (CVD) risk. Inflammation is the initiator of atherosclerosis, throughout the life of atherosclerosis. This study investigated the relationship between serum ALP and atherosclerosis in patients with coronary artery disease (CAD) in an inflammatory state.

METHODS

This was a multicentre retrospective study including 22,989 patients with CAD. Serum alkaline phosphatase was converted into the quartiles. C-reactive protein (CRP) was assayed as a marker of systemic inflammation. The atherosclerosis index (AI) was used to assess the degree of atherosclerosis. Binary logistic regression was used to analyse the relationship between ALP and AI. Stratified analysis was performed according to sex and age.

RESULTS

Elevated serum ALP was associated with the risk of atherosclerosis in patients with CAD, and after quartiling ALP, the OR for Q4 was 1.17 (95% CI 1.08-1.26; p<0.001) when using Q1 as reference. The odds ratio (OR) for ALP and risk of atherosclerosis was higher in patients aged ≤60 years (OR 1.33, 95% CI 1.15-1.53; p<0.001) than in patients aged >60 years (OR 1.11, 95% CI 1.01-1.23; p<0.05), and higher in males (OR 1.21, 95% CI 1.09-1.35; p<0.001) than in females (OR 1.16, 95% CI 1.03-1.31; p<0.05). Q4 (ALP >83.00 U/L) was significantly associated with increased risk of atherosclerosis in the inflammatory state (OR 1.48, 95% CI 1.18-1.86; p<0.001), and it remained after stratified analysis according to sex and age.

CONCLUSIONS

The risk of atherosclerosis tended to increase with increasing ALP levels and the correlation between ALP and the degree of atherosclerosis was significantly stronger when ALP was >83.00 U/L. This relationship was more pronounced in inflammatory states, and there were sex and age differences.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT04026724.

Interaction of metabolism-related pathway gene variants with bisphenol A exposure on serum lipid profiles

Bisphenol A (BPA) can be metabolized by metabolic enzymes and may induce abnormal lipid metabolism. We hypothesized that BPA exposure and its interaction with metabolism-related genes might be associated with serum lipid profiles. We performed a two-stage study among 955 middle-aged and elderly participants in Wuhan, China. Urinary BPA level was estimated without (BPA, μg/L) or with (BPA/Cr, μg/g) adjustments for urinary creatinine and ln-transformed values (ln-BPA or ln-BPA/Cr) were used to normalize the asymmetrical distributions. A total of 412 metabolism-related gene variants were selected and used for gene-BPA interaction analysis. Multiple linear regression was used to analyze the interactions between BPA exposure and metabolism-related genes on serum lipid profiles. In the discovery stage, both ln-BPA and ln-BPA/Cr was associated with decreased high-density lipoprotein cholesterol (HDL-C). Gene-urinary BPA interaction for IGFBP7 rs9992658 was observed to associate with HDL-C levels in both discovery and validation stages, with P_interaction equal to 9.87×10^-4 (ln-BPA) and 1.22×10^-3 (ln-BPA/Cr) in combined analyses. In addition, the inverse association of urinary BPA with HDL-C levels was only observed among individuals carrying rs9992658 AA genotype, but not in individuals carrying rs9992658 AC or CC genotypes. The interaction between BPA exposure and metabolism-related gene IGFBP7 (rs9992658) was associated with HDL-C levels. AVAILABILITY OF DATA AND MATERIAL: Not applicable.

Metabolomics as a potential tool for monitoring patients with aneurysmal subarachnoid hemorrhage

Metabolomics has evolved into a particularly useful tool to study interactions between metabolites and serves as an aid in unraveling the complexity of entire metabolomes. Nonetheless, it is increasingly viewed as a methodology with practical applications in the clinical setting, where identifying and quantifying biomarkers of interest could prove useful for diagnostics. Starting from a concise overview of the most prominent analytical techniques employed in metabolomics, herein we present a review of its application in studies of brain metabolism and cerebrovascular diseases, paying most attention to its uses in researching aneurysmal subarachnoid hemorrhage. Both animal models and human studies are considered, and metabolites identified as potential biomarkers are highlighted.

Factors associated with functional disability in patients with acute stroke excluded from alteplase administration due to minor non-disabling neurological deficits

Background

Although the PRISMS study did not demonstrate the benefit of intravenous alteplase administration in patients with mild stroke within 3 h, about 30% of patients presenting with mild symptoms showed unfavorable functional outcomes. We investigated the factors predictive of functional disability at 90 days in patients who were excluded from alteplase administration due to the National Institutes of Health Stroke Scale (NIHSS) scores of 0-5 and a score between 0 and 2 for each NIHSS score item.

Methods

All patients were diagnosed with acute ischemic stroke or transient ischemic attack within 4.5 h of admission to a tertiary hospital and did not receive alteplase due to a minor stroke between January 2013 and December 2020. Radiological data and clinical information were collected, including baseline and discharge NIHSS scores and modified Rankin Scale (mRS) scores at 90 days. Early neurological deterioration (END) was defined as an increase of two or more NIHSS scores. We defined moderate motor weakness as a NIHSS limb motor score of more than 3 and defined a favorable outcome as a mRS score at 90 days that was 0 or 1.

Results

During the investigation period, 400 patients did not receive alteplase. END occurred significantly more frequently in patients with large artery disease (LAD) than in those with other TOAST classifications. In the multivariate regression analysis, NIHSS per 1-point increase, presenting as moderate motor weakness, and LAD were independent predictors of poor functional outcome (OR, 1.811 NIHSS per 1-point increase; 95% confidence interval [CI], 1.503-2.182; P < 0.0001; OR, 2.173 moderate motor weakness; 95% CI 1.028-4.595; P = 0.042; OR, 2.033 LAD; 95% CI 1.099-3.762; P = 0.024, respectively).

Conclusion

Moderate motor weakness presentation and LAD may be important factors associated with poor functional outcomes in patients with acute stroke excluded from alteplase administration due to mild symptoms.