Mental stress is a prevalent psychosocial stressor in contemporary society. Recently, its association with myocardial ischemia and cerebrovascular events has received increasing attention. Mental stress-induced myocardial ischemia (MSIMI) can provoke adverse cardiovascular events and acute cerebrovascular events. Mental stress, the heart, and the brain form a feedback loop that interacts through mechanisms including autonomic dysregulation, systemic inflammation, and endothelial dysfunction. Stress management and drug therapy are effective treatments for patients with MSIMI, but comprehensive interventions for stroke patients are still lacking. It is of great significance to propose comprehensive management for such patients. The purpose of the review is to describe the interaction among mental stress, myocardial ischemia, and cerebrovascular events: stress precipitates cerebrovascular injury and stroke, and stroke, in turn, amplifies mental stress burden and induces secondary cardiac injury. This reciprocal brain–heart interaction constitutes a clinically actionable target for integrated prevention and management.
Background: Non-ST-segment elevation myocardial infarction (NSTEMI) can occur together with acute heart failure (AHF). Because their clinical presentations overlap, it is often difficult to identify NSTEMI in patients with AHF. The triglyceride-glucose (TyG) index has been linked to cardiovascular risk, but whether it helps identify concomitant NSTEMI in patients with AHF is still unclear. We examined the association between the TyG index and NSTEMI, its predictive value, and whether it adds incremental information to risk discrimination. Methods: We retrospectively reviewed 1,766 patients hospitalized for AHF. Among them, 684 were diagnosed with NSTEMI by coronary angiography. We used logistic regression and prespecified subgroup analyses to examine the association between the TyG index and NSTEMI. Boruta feature selection was used to identify relevant predictive features. We then developed six machine learning (ML) algorithms that incorporated the TyG index. Model performance was assessed by discriminative ability, calibration, clinical net benefit, and reclassification. SHapley Additive exPlanations (SHAP) analysis was used to quantify how much each feature contributed to model predictions. Results: A higher TyG index was associated with higher odds of concomitant NSTEMI (odds ratio [OR], 2.03, 95% CI, 1.67–2.46; P < 0.001). This association was consistent across subgroups defined by diabetes status, but it varied according to lipid-lowering medication use ( P for interaction = 0.028). Boruta feature selection identified N-terminal pro-B-type natriuretic peptide (NT-proBNP), cardiac troponin T (cTnT), the TyG index, triglycerides, creatinine, and fasting blood glucose as important features. Among the algorithms tested, the enhanced random forest model had the highest area under the receiver operating characteristic curve (AUROC) (0.8558 vs. 0.8488) and a slightly lower Brier score (0.1473 vs. 0.1488). At the selected classification threshold, sensitivity was 83.9% and specificity was 78.1%. Decision curve analysis showed similar clinical utility for the standard and enhanced random forest models. SHAP analysis showed that NT-proBNP and cTnT had the greatest influence on model predictions, and the TyG index also contributed to NSTEMI classification. Conclusions: In patients with AHF, a higher TyG index was independently associated with concomitant NSTEMI. The TyG index added incremental information to multivariable prediction models, although the improvement was modest and differed across algorithms. The TyG index may therefore be better used as a supplementary marker than as a standalone diagnostic tool.
Myocardial infarction (MI) is a leading global health challenge necessitating therapeutic interventions, such as cardiac rehabilitation (CR), to mitigate adverse cardiac remodeling and restore functional capacity. Exercise-based CR, a cornerstone of post-MI management, significantly improves cardiovascular outcomes, likely owing to the stimulation of macrophage activity. Macrophages play a pivotal role in post-MI inflammatory and reparative phases because of their phenotypic plasticity. Lactate is a key molecule involved in phenotypic shifts in macrophages. Traditionally viewed as a metabolic byproduct, lactate has recently been identified as a crucial signaling molecule and substrate for lactylation, a post-translational modification involving covalent attachment to lysine residues on histones and non-histone proteins. This epigenetic modification directly links cellular metabolism to gene expression and protein function. Emerging evidence indicates that exercise profoundly influences lactate dynamics and lactylation events in various tissues, including heart and immune cells. In this review, the current knowledge on the intricate interplay between exercise, lactate metabolism, lactylation, and macrophage function in MI is summarized. Furthermore, we propose a novel mechanistic framework in which different exercise regimens enhance macrophage pro-reparative polarization and functional reprogramming to promote cardiac repair by modulating lactate availability and specific lactylation events. The molecular mechanisms underlying lactylation, its known roles in macrophage biology, and how exercise-induced metabolic shifts can leverage this pathway to foster a pro-reparative cardiac microenvironment have been critically examined in the current literature. Furthermore, significant research gaps are highlighted, and future directions for investigating lactylation as a therapeutic target to optimize macrophage-mediated CR strategies post-MI are suggested.
Background: Cardiometabolic diseases (CMDs) represent critical health burdens for middle-aged and elderly populations. Nevertheless, the mechanism by which alterations in sleep patterns affect CMD risk and all-cause mortality remain unclear. This research aimed to explore the relationships between dynamic sleep pattern shifts and both CMDs as well as all-cause mortality among adult residents in China. Methods: We performed a prospective cohort investigation based on datasets obtained from the China Health and Retirement Longitudinal Study (CHARLS) (2011–2020). A total of 8,106 individuals aged 50–70 years who were free of CMDs at study entry were enrolled for baseline-related analyses, whereas 6,374 participants were selected for transition-oriented analyses. Sleep patterns were evaluated through three self-reported sleep phenotypes (sleep duration, napping duration, and insomnia) and summarized into a composite sleep score. Incident CMDs (heart disease, stroke, or diabetes) together with all-cause mortality constituted the main study outcomes. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results: Over a median follow-up period of 7.01 years for CMDs and 9.01 years for all-cause mortality, varying changes in sleep patterns corresponded to differing risks of CMDs and death from any cause. Compared with those whose sleep duration remained consistently unfavourable, participants who shifted from unfavourable to favourable sleep duration demonstrated lower risks of CMDs (HR = 0.86, 95% CI 0.75–0.98) and all-cause mortality (HR = 0.78, 95% CI 0.61–0.99). Furthermore, participants who progressed from low-risk to moderate-risk or high-risk sleep patterns were associated with increased risks of CMDs (low-risk to moderate-risk: HR = 1.24, 95% CI 1.00–1.55; low-risk to high-risk: HR = 1.66, 95% CI 1.17–2.36) compared with those with stable low-risk sleep patterns. In contrast, compared with those whose high-risk sleep patterns remained stable, participants who transitioned from high-risk back to low-risk or moderate-risk patterns demonstrated lower CMD risks (high-risk to low-risk: HR = 0.63, 95% CI 0.48–0.82; high-risk to moderate-risk: HR = 0.73, 95% CI 0.58–0.92). Conclusions: Sleep pattern deterioration is linked to increased risks of CMDs and death from any cause, whereas sleep pattern improvement is associated with decreased risks of both outcomes. Our findings underscore the notion that preserving low-risk sleep patterns may play a key role in preventing CMDs and all-cause mortality.
