Lipids in Health and Disease最新文献

Background: Cardiometabolic multimorbidity (CMM) poses a significant global health challenge. The atherogenic index of plasma (AIP) is a promising biomarker for cardiometabolic risk, but there is limited information on its cumulative effect on CMM and the underlying mechanisms. This study investigated the association of cumulative AIP exposure with CMM risk, and explored the mediating roles of the triglyceride glucose (TyG) index and body mass index (BMI).

Methods: This study was based on data from 5,454 participants from the China Health and Retirement Longitudinal Study (CHARLS, 2011 baseline). The participants were stratified into tertiles of cumulative AIP (cuAIP) and classified into three distinct AIP trajectory groups using k-means clustering. Associations between cuAIP levels, AIP trajectories, and CMM incidence were assessed using logistic. The relationship between cuAIP and CMM was further examined using receiver operating characteristic (ROC) curve analysis and restricted cubic splines (RCS). Structural equation modeling was used to evaluate the mediating roles of the TyG index and BMI. Finally, subgroup and sensitivity analyses were conducted to validate the results.

Results: A total of 385 CMM cases were observed during the 7-year follow-up. Cluster analysis revealed the highest CMM incidence (12.1%) in the persistently high AIP trajectory group. Logistic regression models indicated that the highest cuAIP group (OR 2.81, 95% CI: 1.95-4.14) and high AIP trajectory group (OR 2.34, 95% CI: 1.68-3.28) had the highest CMM risk, with consistent results in sensitivity analyses and most subgroups. The AUC of cuAIP for predicting CMM was 0.648, and RCS curves demonstrated increasing CMM incidence with rising cuAIP levels. Mediation analysis indicated that the TyG index and BMI mediated 74% and 26% of the total effect, respectively.

Conclusion: This study establishes AIP as an independent predictor of CMM, whereby its effect is primarily mediated by the TyG index and BMI. These findings support the implementation of integrated clinical strategies to effectively prevent CMM and its associated diseases.

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.