Guoan Zhao

Introduction: Unstable angina (UA) represents a critical condition within the broader context of acute coronary syndromes, characterized by episodes of sudden chest pain resulting from insufficient blood flow to the myocardium. The pathophysiological mechanisms underlying UA remain complex and poorly understood, necessitating further investigation into the metabolic alterations associated with this condition. Identifying specific biomarkers for UA is crucial for improving diagnostic accuracy and facilitating timely therapeutic interventions. Methods: The present study was designed to elucidate the metabolic profile of UA by enrolling 60 patients diagnosed with UA, alongside 60 healthy controls. Participants were stratified into discovery and validation cohorts, with each group comprising 30 UA patients and 30 controls. The diagnosis of UA was performed in accordance with the 2020 European Society of Cardiology guidelines and the 2019 Chinese clinical pathway for UA management. Blood samples were meticulously collected following an overnight fast, processed, and subsequently stored at −80 °C. A comprehensive lipidomic analysis was executed utilizing ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), with compound identification referenced from the Metware Database. Data analysis involved advanced multivariate statistical techniques, including principal component analysis and orthogonal partial least squares-discriminant analysis. Results: The analysis revealed significant metabolic discrepancies between the UA and control groups, identifying a total of 193 differential metabolites, of which 67 were notably upregulated in the UA cohort. Pathway enrichment analysis highlighted critical alterations in glycerophospholipid metabolism and necroptosis pathways. Furthermore, the application of machine learning algorithms, specifically neural networks and random forests, enabled the identification of key metabolic biomarkers, including TxB3, LPC(16:0/0:0), and DL-Carnitine, which exhibited robust diagnostic potential. Conclusion: In summary, our findings indicate that these metabolites may serve as promising candidate diagnostic biomarkers for UA, providing valuable insights into its pathophysiology.

Ventricular arrhythmia is the primary cause of sudden cardiac death in patients with myocardial infarction (MI). Myocardial inflammation and Na⁺/Ca²⁺ imbalance are the main triggering factors for life-threatening tachyarrhythmias after MI, which induce ion channel dysfunction, intracellular environment imbalance, tissue damage, and other alterations, subsequently resulting in modifications in cardiac conduction velocity and pathways. Subsequent adverse fibrotic remodeling provides a substrate for ventricular tachyarrhythmia (VT). Mitochondria, as the intersection site of these pathophysiological changes and the center of Na⁺/Ca²⁺ homeostasis and inflammatory crosstalk, may be key sites for the occurrence and development of ischemic arrhythmia. This review briefly outlines the roles of inflammation, Na⁺/Ca²⁺ homeostasis, and mitochondria in the damage, repair, and structural remodeling of infarcted hearts, in which these three are interconnected to provide a large number of substrates for VT.

Epidemiological studies have found an association between air pollution and cardiovascular and cerebrovascular diseases (CACD) and its subtypes. However, there is a lack of individual-level data to explore the associations of air pollutants on CACD and its subtypes, the interaction among them, and the potential mechanism. This study employed a two-stage design, combining a time-stratified case-crossover study with a cohort study, analyzing data from one million individuals from China and half million from the UK. The study assessed the impact of air pollutants on CACD and its subtypes, while also examining the mediating effects of inflammation. Distributed lag non-linear models were used to analyze the lagged effects of pollutants, and mediation analysis was conducted to evaluate the role of inflammatory markers (SII, SIRI, AISI) in the relationship between air pollution and CACD. A total of 829,135 CSDs patients were recorded in this study. An interquartile range (IQR) increase in concentrations of PM2.5, PM10, NO2, SO2, CO, and O3 was associated with increases of 11.3% [95% confidence interval (CI) 9.5%-13.2%], 10.5% (95% CI 8.6%-12.3%), 3% (95% CI 1%-5%), 15.2% (95% CI 13.3%-17.1%), 15.5% (95% CI 11.6%-19.5%), and 2.8% (95% CI 2.2%-3.4%) in CSDs, respectively. A similar positive association was also observed for cardiovascular and ischemic heart diseases. A significant synergistic interaction between PM2.5 and NO2 and CO for CSDs. Approximately 64.75%, 21.13%, 32.2%, 2.31%, 43.7% and 43.7% of the effects of PM2.5, PM10, NO2, SO2, CO, and O3 on CSDs were significantly mediated by SII. This study provides robust evidence that short-term exposure to common air pollutants significantly increases the risk of CACD and its subtypes, with inflammation playing a crucial mediating role. The findings underscore the importance of coordinated air pollution control strategies and public health interventions to mitigate the cardiovascular risks associated with air pollution.

Introduction: Coronary heart disease (CHD) is a leading cause of cardiovascular mortality, with air pollution serving as a significant risk factor. Henan Province, characterized by both a high incidence of CHD and severe air pollution, faces substantial health and economic challenges. However, limited research has explored the relationship between air pollution and CHD in this region.Methods: This study employs a case-crossover design combined with a distributed lag non-linear model (DLNM) to examine the short-term effects of extreme concentrations of air pollutants (PM₂.₅, PM₁₀, NO₂, SO₂, CO, and O₃) on CHD hospitalizations in Henan. Data on 133,294 confirmed CHD patients from seven large hospitals across five cities (2016–2021) were collected, with patients' addresses linked to nearby air quality monitoring stations to assess exposure to air pollutants and meteorological factors. The time-stratified case-crossover design and DLNM were used to calculate relative risks (RRs) for pollutant exposure on CHD hospitalizations, and subgroup analyses were conducted to identify sensitive groups.Results: Significant increases in CHD hospitalizations were associated with extremely high concentrations of NO₂, SO₂, and PM₁₀, with maximum RRs of 1.768 for NO₂, 2.821 for SO₂, and 1.728 for PM₁₀ on the 7th cumulative day, while high O₃ levels showed a protective effect. Younger individuals (≤64y) and males were more sensitive to these effects, and high CO concentrations only increase the risk of CHD incidence in the younger (≤64y) subgroup. Synergistic interactions were observed between certain pollutants, such as CO and NO₂/SO₂/PM₁₀, suggesting that the negative impact of CO on CHD is amplified in a multi-pollutant environment due to interactions with other pollutants.Discussion: These findings highlight the significant public health impact of air pollution on CHD in Henan Province.

Air pollution and extreme weather events pose a serious threat to human health. We collected atmospheric pollution, meteorological factors, and hospitalisation data for ischemic heart disease (IHD) in Xinxiang, Henan Province, from 2016 to 2021. Using a time-stratified case-crossover design and conditional Poisson regression analysis, we explored the association between atmospheric pollutants (particulate matter with diameter ≤ 2.5 μm [PM_2.5], particulate matter with diameter ≤ 10 μm [PM₁₀], nitrogen dioxide [NO₂], carbon monoxide [CO]), meteorological factors, and IHD hospitalizations. We evaluated synergistic effects using relative excess risk due to interaction (RERI), attribute proportion (AP), and synergy index (S). PM_2.5, PM₁₀, NO₂, CO, relative humidity, and cold spells were significantly associated with IHD hospitalization risk. Significant interaction effects (RERI > 0, AP > 0, S > 1) were found in PM_2.5-PM₁₀-NO₂ combinations. The attributable fractions were 3.4-7.3% for pollutant combinations and 8-17% during cold spells with different PM_2.5 levels. Males and individuals aged ≥ 65 were more susceptible to pollutants, while females and elderly individuals showed higher sensitivity to cold spells. These findings provide evidence for optimizing extreme weather warning systems and reducing air pollution exposure to protect public health.

The research aims to investigate the mechanical response of footfalls at different velocities to understand the mechanism of heel injury and provide a scientific basis for the prevention and treatment of heel fractures. A three-dimensional solid model of foot drop was constructed using anatomical structures segmented from medical CT scans, including bone, cartilage, ligaments, plantar fascia, and soft tissues, and the impact velocities of the foot were set to be 2 m/s, 4 m/s, 6 m/s, 8 m/s, and 10 m/s. Explicit kinetic analysis methods were used to investigate the mechanical response of the foot landing with different speeds to explore the damage mechanism of heel bone at different impact velocities. Lower impact velocities result in relatively low stress on the medial cortex and posterior talar articular bony surfaces, which may result in minor injury or stress adaptation in the heel. As the impact velocity increases, the stresses on the medial cortex and posterior taller articular surface also increase significantly, greatly raising the risk of heel fractures. This study holds significant implications for safeguarding foot health and enhancing the safety of athletes and individuals engaged in high-impact sports. Graphical Abstract

Background: Environmental factors have been identified as primary risk factors for type 2 diabetes mellitus (T2DM). However, studies on the association between environmental factors and T2DM have mainly focused on morbidity and mortality, which do not fully reflect the disease burden stemming from air pollution. Therefore, we aimed to evaluate the correlation between air pollution and T2DM, including hospital length of stay (LOS) and costs.Methods: We collected data on patients with T2DM from three healthcare institutions in Xinxiang from 2016–2021. Data on particulate and gaseous pollutants in Xinxiang and daily meteorological data were collected from national databases. The distribution lag nonlinear model was used to evaluate the correlation between air pollution and the number of inpatients with T2DM, LOS, and hospital costs. Subgroup analyses were conducted to identify potential modifying factors.Results: Overall, 13,797 patients with T2DM were included in our analysis. Within the cumulative lag of 7 days, with every increase of 1 mg/m³ of carbon monoxide (CO) and 10 μg/m³ of 2.5 microns particulate matter, nitrogen dioxide and ozone exhibited significant associations with an increase in diabetes hospitalization risk. CO exhibited adverse effects on LOS on most lag days. Moreover, hospital costs were significantly associated with the attributable fraction of LOS and hospital costs attributed to diabetes.Conclusions: Exposure to air pollutants increased T2DM risk, imposing significant economic and social burdens in Xinxiang, China. Implementing policies to reduce air pollutant exposure may decrease T2DM admissions, costs, and LOS.