Lipids in Health and Disease最新文献

Background: While the U-shaped association between high-density lipoprotein cholesterol (HDL-C) levels and the risk of all-cause and cardiovascular mortality is well-established, the underlying contributions of HDL subclasses remain poorly understood. This study aimed to comprehensively analyze the variations of HDL subclass components across different HDL-C levels and assess their associations with the risk of all-cause and cardiovascular mortality.

Methods: This study enrolled 1,585 participants aged 35-75 years from China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART) (2014-2023). Lipoprotein parameters were measured by nuclear magnetic resonance, with a focus on triglycerides (TG), cholesterol (CH), free cholesterol (FC), phospholipids (PL), apolipoprotein A1 (Apo-A1) and apolipoprotein A2 (Apo-A2) within four density-separated HDL subclasses (HDL1-HDL4). Between-group comparisons were performed using analysis of variance with post-hoc least significant difference tests. Cox proportional hazards regression models and competing risk models were used to assess the association of HDL subclass components with all-cause and cardiovascular mortality. Potential nonlinear associations were examined using models with restricted cubic splines (RCS).

Results: During a median follow-up of 7.6 years, 84 all-cause (5.3%) and 23 (1.5%) cardiovascular deaths were documented. As HDL-C concentration increased, most HDL subclass components (including CH, FC, PL, and Apo-A1) also increased across low (≤ 30 mg/dL), intermediate (50-60 mg/dL), and high (≥ 100 mg/dL) HDL-C groups. Regression models showed that components in larger, more buoyant HDL subclasses (such as H1TG, H2TG, H1CH, H1FC, H1PL, H1A1, H1A2 and H2A2) were positively associated with all-cause mortality, whereas smaller, denser ones (including H4CH, H4FC, H4PL, H4A1 and H4A2) exhibited protective effects. H1PL, H1A1 and H1A2 also emerged as independent risk factors for cardiovascular mortality. The RCS analysis revealed positive linear associations of H1CH and H1A1 with all-cause mortality, while H4CH and H4A1 were inversely associated.

Conclusions: Larger, more buoyant HDL subclasses showed a positive association with all-cause mortality, whereas smaller, denser ones were protectively associated. The U-shaped association between HDL-C and mortality may be primarily explained by lower levels of H4CH at very low HDL-C concentrations and higher levels of H1CH at extremely high HDL-C levels. Similar explanations could also account for the association between Apo-A1 and mortality.

Trial registration: ClinicalTrials.gov, NCT02536456. Registered 24 August 2015.

Background: The triglyceride-glucose index (TyG) and atherogenic index of plasma (AIP) are emerging metabolic biomarkers associated with cardiovascular diseases. However, their combination prognostic value in patients with critical chronic heart failure (CHF) remains unclear. This study aimed to evaluate the combined predictive effect of these two biomarkers and clarify their interactive patterns in this association.

Methods: 1,238 patients were recruited via the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, with a median age of 71 years. Multivariable Cox regression, Kaplan-Meier analysis, and receiver operating characteristic (ROC) curve were employed to explore associations between TyG, AIP, and mortality. Mediation analysis was applied to assess their bidirectional mediation effects. Additionally, we developed a machine learning-driven prediction model, which was further utilized to evaluate the two indicators' incremental predictive value.

Results: 1,238 patients were included, with 478 (38.61%) dying during follow-up. Fully adjusted Cox regression models revealed that the high TyG and high AIP group was associated with the highest risk of mortality relative to the low TyG and low AIP group (14-day HR 2.18, 95% Cl: 1.44-3.31; 365-day HR 1.92, 95% Cl: 1.38-2.68). ROC analyses demonstrated that the combined TyG-AIP outperformed either marker alone in predicting mortality at all follow-up observation time points (all P < 0.05). Mediation analysis revealed that TyG mediated the effect of AIP on mortality across all time frames, with a more pronounced effect at 365 days (65.47%) than at 14 days (37.32%). In contrast, AIP served as a mediator in the association between TyG and short-term mortality only (14-day: 30.39%; 30-day: 25.76%). The random forest model confirmed that the incorporation of both the TyG index and AIP remarkably improved predictive capability, corroborating their combined incremental value.

Conclusion: Combined elevation of TyG and AIP was independently related to an elevated risk of mortality in patients with critical CHF. Combined assessment of these biomarkers may facilitate the early recognition of high-risk subjects and support stage-specific metabolic interventions.

Background: Previous observations have reported inconsistent results on the association between low-density lipoprotein cholesterol (LDL-C) at presentation and long-term outcomes after acute myocardial infarction (AMI). We aimed to clarify the potential impact of baseline characteristics on the inverse association between LDL-C and all-cause mortality, known as the lipid paradox.

Methods: A total of 1,305 critically ill patients with AMI from the Medical Information Mart for Intensive Care IV database were included in the analysis. Patients were stratified according to LDL-C quartiles. The primary outcome was 180-day and 360-day all-cause mortality. Baseline characteristics were included in stepwise Cox regression models. Restricted cubic spline analyses across multiple models and subgroups were performed to assess the influence of baseline characteristics on the association between LDL-C and long-term outcomes.

Results: A total of 244 (18.7%) and 291 (22.3%) mortality events occurred at 180 and 360 days of follow-up, respectively. Patients in the lowest LDL-C quartile had the highest all-cause mortality at both 180 and 360 days (28.7% and 35.2%, respectively). After stepwise adjustment for baseline covariates, the J-shaped relationship observed in the unadjusted model was gradually attenuated and disappeared. The inverse association between LDL-C and mortality was more pronounced in subgroups characterized by elevated mortality risk, including patients with low albumin levels, elevated neutrophil-to-lymphocyte ratio, and higher SOFA scores. Nevertheless, in-hospital statin use was consistently associated with reduced all-cause mortality across nearly all subgroups.

Conclusions: The lipid paradox observed in critically ill patients with AMI is attributed to differences in baseline characteristics across LDL-C strata. After adjusting for potential confounders, baseline LDL-C was not an independent predictor of long-term mortality in AMI. Lipid-lowering therapy was associated with favorable long-term outcomes irrespective of baseline LDL-C levels.

Background: Inflammation and metabolic disorders significantly contribute to frailty development. The C-reactive protein-triglyceride-glucose index (CTI) indicates both inflammation and insulin resistance (IR). This study delves into the connection between various dimensions of CTI-baseline CTI, cumulative CTI (cumCTI), and CTI change-and the incidence of frailty among the Chinese middle-aged and elderly demographic. Inflammation and metabolic disorders significantly contribute to frailty development.

Methods: This research employed the China Health and Retirement Longitudinal Study (CHARLS). K-means clustering was utilized to categorize the dynamic variations in CTI. The connection between various CTI dimensions and the frailty risk was evaluated through the Cox proportional hazards model and restricted cubic spline (RCS) regression model. Subgroup analyses, interaction tests, and sensitivity analyses were performed to ensure result robustness.

Results: The research involved a total of 5,366 participants. Through the application of K-means clustering, 3 classifications of changes in CTI trajectories were identified.Baseline characteristics from the K-means clustering analysis showed that the median age of individuals was 58 years (52, 64). Within the studied group, there were 2,899 males, constituting 54.0% of the total sample. During follow-up, there were 964 newly identified instances of frailty, accounting for 18.0% of the total cases documented. A notable positive linear correlation between increased CTI levels and the likelihood of experiencing frailty. In Model 3, each unit increment in the baseline CTI was associated with a 35% escalation in the likelihood of frailty (HR, 1.35; 95% CI, 1.21-1.50). Furthermore, each additional unit of cumCTI was linked to a 14% escalation in frailty risk (HR, 1.14; 95% CI, 1.09-1.19).The RCS analysis revealed a positive linear correlation between the initial CTI, cumCTI, and the likelihood of developing frailty. Subgroup and interaction analyses did not demonstrate any significant variations among the different subgroups (P>0.05). Sensitivity analyses further validated the consistency and reliability of these findings.

Conclusion: Elevated CTI are linked to an increased likelihood of frailty. Ongoing longitudinal assessment of CTI levels across multiple dimensions can facilitate the timely detection of patients who are at a significant risk of developing frailty.