Sepsis-associated encephalopathy (SAE) is a cognitive impairment associated with sepsis that occurs in the absence of direct infection in the central nervous system or structural brain damage. Microglia are thought to be macrophages of the central nervous system, devouring bits of neuronal cells and dead cells in the brain. They are activated in various ways, and microglia-mediated neuroinflammation is characteristic of central nervous system diseases, including SAE. Here, we systematically described the pathogenesis of SAE and demonstrated that microglia are closely related to the occurrence and development of SAE. Furthermore, we comprehensively discussed the function and phenotype of microglia and summarized their activation mechanism and role in SAE pathogenesis. Finally, this review summarizes recent studies on treating cognitive impairment in SAE by blocking microglial activation and toxic factors produced after activation. We suggest that targeting microglial activation may be a putative treatment for SAE.

The C-reactive protein-triglyceride-glucose index (CTI) has emerged as an innovative composite marker for evaluating metabolic-inflammatory dysregulation, integrating markers of insulin resistance and systemic inflammation. However, the association between CTI and cardiovascular disease (CVD) or its mortality has rarely been studied. This study sought to examine CTI's associations with CVD mortality, CVD incidence, and all-cause mortality. This study included 8,679 adults from the National Health and Nutrition Examination Survey (NHANES) 2001–2010, 2015–2018. The CTI was derived as: 0.412* Ln (CRP [mg/L]) + Ln (TG [mg/dl] × FPG [mg/dl])/2, with participants categorized into quartiles. We employed Kaplan–Meier curves, cox proportional hazards model, logistic regression analyses, and restricted cubic spline (RCS) to evaluate CTI's associations with CVD mortality, total CVD incidence, and all-cause mortality across sex-stratified, age-specific, and glycemic subgroups. In this study, CTI was significantly and positively associated with CVD mortality, total CVD incidence, and all-cause mortality. CTI significantly predicted both CVD mortality (HR 2.28 [1.69–3.24]) and all-cause mortality (HR 2.14 [1.76–2.55]). Additionally, the CTI index correlated with the risk of total CVD (OR 2.85, 95% CI 2.32–3.52), congestive heart failure (OR 3.66, 95% CI 2.46–5.35), coronary heart disease (OR 2.82, 95% CI 1.95–3.97), angina pectoris (OR 2.85, 95% CI 1.89–4.22), heart attack (OR 2.59, 95% CI 1.89–3.52), and stroke (OR 2.86, 95% CI 2.00–3.85). Specifically, the association was similar between male and female, and similar in young participants and elderly participants. In different glycemic status, high levels of CTI were found to be linked to an increased risk of CVD in individuals without diabetes mellitus (DM). However, this association was not observed in individuals with DM. Our analysis revealed that elevated CTI levels were significantly associated with CVD incidence and mortality. CTI may emerge as a unique predictive marker for CVD risk.

Background: The impact of integrated lifestyles on health has attracted a lot of attention. It remains unclear whether adherence to low-risk healthy lifestyle factors is protective in individuals with metabolic syndrome and metabolic syndrome-like characteristics. We aimed to explore whether and to what extent overall lifestyle scores mitigate the risk of all-cause mortality in individuals with metabolic syndrome and metabolic syndrome-like characteristics. Methods: In total, 6934 participants from the 2007 to 2014 National Health and Nutrition Examination Survey (NHANES) were included. The weighted healthy lifestyle score was constructed based on smoking, alcohol consumption, physical activity, diet, sleep duration, and sedentary behavior information. Generalized linear regression models and restricted cubic splines were used to analyze the association between healthy lifestyle scores and all-cause mortality. ​ Results: Compared to participants with relatively low healthy lifestyle scores, the risk ratio (RR) in the middle healthy lifestyle score group was 0.51 (RR = 0.51, 95% CI 0.30–0.88), and the high score group was 0.26 (RR = 0.26, 95% CI 0.15–0.48) in the population with metabolic syndrome. The difference in gender persists. In females, the RRs of the middle and high score groups were 0.47 (RR = 0.47, 95% CI 0.23–0.96) and 0.21 (RR = 0.21, 95% CI 0.09–0.46), respectively. In males, by contrast, the protective effect of a healthy lifestyle was more pronounced in the high score group (RR = 0.33, 95% CI 0.13–0.83) and in females, the protective effects were found to be more likely. The protective effect of a healthy lifestyle on mortality was more pronounced in those aged < 65 years. Higher lifestyle scores were associated with more prominent protective effects, regardless of the presence of one metabolic syndrome factor or a combination of several factors in 15 groups. What's more, the protective effect of an emerging healthy lifestyle was more pronounced than that of a conventional lifestyle. Conclusions: Adherence to an emerging healthy lifestyle can reduce the risk of all-cause mortality in people with metabolic syndrome and metabolic syndrome-like characteristics; the higher the score, the more obvious the protective effect. Our study highlights lifestyle modification as a highly effective nonpharmacological approach that deserves further generalization.

Aging is widespread worldwide and a significant risk factor for cardiovascular disease (CVD). Mechanisms underlying aging have attracted considerable attention in recent years. Remarkably, aging and CVD overlap in numerous ways, with deregulated nutrient sensing as a common mechanism and lifestyle as a communal modifier. Interestingly, lifestyle triggers or suppresses multiple nutrient-related signaling pathways. In this review, we first present the composition of the nutrient-sensing network (NSN) and its metabolic impact on aging and CVD. Secondly, we review how risk factors closely associated with CVD, including adverse life states such as sedentary behavior, sleep disorders, high-fat diet, and psychosocial stress, contribute to aging and CVD, with a focus on the bridging role of the NSN. Finally, we focus on the positive effects of beneficial dietary interventions, specifically dietary restriction and the Mediterranean diet, on the regulation of nutrient metabolism and the delayed effects of aging and CVD that depend on the balance of the NSN. In summary, we expound on the interaction between lifestyle, NSN, aging, and CVD.

Macrophages are the central immune cells in atherosclerosis (AS) and play a critical role in the initiation, progression and invasion of atherosclerotic plaques. Metabolic reprogramming is a crucial feature that determines macrophage function and is driven by a combination of intrinsic alterations in macrophages and extrinsic factors such as cytokines acting in the plaque microenvironment. Intrinsic macrophage mechanisms activate signal transduction pathways that change metabolic enzyme activity, and the expression of metabolic regulators. Extrinsic signalling mechanisms involve lipids and cytokines in the microenvironment, promoting and amplifying macrophage metabolic reprogramming. This review describes the intrinsic and extrinsic mechanisms driving macrophage metabolic reprogramming in the AS microenvironment and the interplay of these metabolic rewires in the microenvironment. Moreover, we discuss whether targeting these different pathways to treat macrophage microenvironmental changes can alter the fate of the vulnerable plaques.

The atherogenic index of plasma (AIP) is considered an important marker of atherosclerosis and cardiovascular risk. However, its potential role in predicting length of stay (LOS), especially in patients with atherosclerotic cardiovascular disease (ASCVD), remains to be explored. We investigated the effect of AIP on hospital LOS in critically ill ASCVD patients and explored the risk factors affecting LOS in conjunction with machine learning. Using data from the Medical Information Mart for Intensive Care (MIMIC)-IV. AIP was calculated as the logarithmic ratio of TG to HDL-C, and patients were stratified into four groups based on AIP values. We investigated the association between AIP and two key clinical outcomes: ICU LOS and total hospital LOS. Multivariate logistic regression models were used to evaluate these associations, while restricted cubic spline (RCS) regressions assessed potential nonlinear relationships. Additionally, machine learning (ML) techniques, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGB), were applied, with the Shapley additive explanation (SHAP) method used to determine feature importance. The study enrolled a total of 2423 patients with critically ill ASCVD, predominantly male (54.91%), and revealed that higher AIP values were independently associated with longer ICU and hospital stays. Specifically, for each unit increase in AIP, the odds of prolonged ICU and hospital stays were significantly higher, with adjusted odds ratios (OR) of 1.42 (95% CI, 1.11–1.81; P = 0.006) and 1.73 (95% CI, 1.34–2.24; P < 0.001), respectively. The RCS regression demonstrated a linear relationship between increasing AIP and both ICU LOS and hospital LOS. ML models, specifically LGB (ROC:0.740) and LR (ROC:0.832) demonstrated superior predictive accuracy for these endpoints, identifying AIP as a vital component of hospitalization duration. AIP is a significant predictor of ICU and hospital LOS in patients with critically ill ASCVD. AIP could serve as an early prognostic tool for guiding clinical decision-making and managing patient outcomes.