Lipids in Health and Disease最新文献

Background: The Zhejiang University (ZJU) Index, a composite metabolic indicator incorporating body mass index (BMI), fasting plasma glucose (FPG), triglycerides (TG), and alanine aminotransferase/aspartate aminotransferase (ALT/AST) ratio, is associated with abnormalities like dyslipidemia and glucose intolerance. Yet its clinical significance in cardiovascular disease (CVD) is underinvestigated, and this study aims to clarify its associations with prevalent CVD, all-cause mortality, and CVD mortality.

Methods: The study included 18,609 adults from the National Health and Nutrition Examination Survey (NHANES) spanning 1999-2018. Associations of the ZJU Index with prevalent CVD were evaluated using multivariable Logistic regression. Associations with all-cause mortality and CVD mortality were assessed using multivariable Cox proportional hazards regression. Restricted cubic spline (RCS) regression and ROC curve analyses were further used to explore non-linear relationships and predictive performance, respectively. Sensitivity analyses, which included excluding participants who died within 2 years and those with baseline CVD, confirmed the robustness of the findings.

Results: Among 18,609 participants, 2,121 had CVD (9.0% weighted prevalence). Logistic regression showed a positive association of the ZJU Index with prevalent CVD risk: after full adjustment, a 1-unit increase was linked to a 2% higher risk (OR = 1.02, 95% CI 1.01-1.03, P < 0.001), and participants in the highest quartile (Q4) had a 50% higher risk than those in the lowest quartile (Q1) (OR = 1.50, 95% CI 1.22-1.84, P < 0.001). Significant non-linearity between the ZJU Index and prevalent CVD was confirmed (P < 0.001). Over a median 71-month follow-up (36-121 months), 2,752 all-cause deaths (11.1% weighted rate) and 934 CVD deaths (3.4% weighted rate) occurred. After full adjustment, the association persisted for CVD mortality (Q4 vs. Q1: HR = 1.30, 95% CI 1.03-1.63, P = 0.027). Restricted cubic spline (RCS) analysis revealed that the ZJU Index had a U-shaped relationship with all-cause mortality and a J-shaped relationship with CVD mortality (both P < 0.001). Sensitivity analyses supported the robustness of these findings.

Conclusions: In conclusion, whether analyzed as a continuous or categorical variable, a higher ZJU Index is significantly associated with higher risks of CVD and CVD mortality, while it shows a U-shaped relationship with all-cause mortality. This indicates that the ZJU Index holds potential as a CVD risk stratification tool to identify high-risk individuals and guide targeted interventions. However, its utility as a CVD screening tool requires further validation to confirm optimal cut-offs and compatibility with existing protocols.

Background: Early identification of high-risk individuals after Acute Coronary Syndrome (ACS) is critical for tailoring intensive lipid-lowering therapy, a cornerstone of secondary prevention to alleviate the growing global burden of cardiovascular diseases, while practical tools integrating lipid burden and vascular aging remain lacking. This study proposes the age-multiplied low density lipoprotein cholesterol (LDL-C) burden (AM-LDL) index to quantify cumulative atherogenic exposure and stratify 1-year ischemic and bleeding risk.

Methods: This post-hoc analysis utilized data from the prospective "Beijing Risk Intervention Clinical (BRIC) Study", which enrolled post-percutaneous coronary intervention (PCI) ACS patients from a 30-center Chinese cohort and followed them for one year. AM-LDL was calculated by multiplying the admission LDL-C concentration by the patient's age. Study endpoints comprised Bleeding Academic Research Consortium (BARC) type ≥ 2 bleeding, major adverse cardiovascular events (MACE), and net adverse clinical events (NACE).

Results: Among the 5658 participants, 309 experienced BARC ≥ 2 bleeding event, 156 had MACE, and 454 developed NACE. AM-LDL showed a significant association with MACE and NACE in Kaplan-Meier analysis (log-rank P = 0.002 for each), in contrast to bleeding (P = 0.055). Subsequent multivariable Cox modeling sustained its independent association with MACE (Hazard Ratio [HR] = 2.623, 95% Confidence Interval [95%CI]: 1.411-4.876, P = 0.002) and NACE (HR = 1.520, 95% CI: 1.094-2.111, P = 0.012), but not with bleeding (HR = 1.203, 95% CI: 0.815-1.775, P = 0.353). Restricted cubic spline analysis revealed a linear dose-response relationship, and an inverted U-shaped association with NACE, while no nonlinear correlation was detected for bleeding.

Conclusion: The AM-LDL index, derived from two readily available admission parameters, is associated with post-discharge one-year MACE and NACE risks among ACS patients. Its application may help improve risk stratification, allowing for timely treatment intensification and a more personalized prevention strategy, while warranting validation in interventional trials before clinical implementation.

Background: Atherosclerosis (AS) is a complex cardiovascular disease characterized by dysregulated macrophage cholesterol metabolism (CM), a central driver of foam cell formation and plaque progression. However, how macrophage CM becomes dysregulated is still not fully understood. Single-cell RNA sequencing (scRNA-seq) was combined with bulk RNA-seq data to identify CM-related genes with diagnostic and therapeutic potential.

Methods: Data for this study were sourced from Gene Expression Omnibus (GEO), comprising one scRNA-seq dataset and several bulk mRNA transcriptomic datasets. ScRNA-seq was utilized to investigate the heterogeneity of CM in different cells in AS-affected tissues and identify genes associated with macrophage CM. For the bulk RNA-seq dataset, machine learning was applied to identify key genes tied to macrophage CM. A risk scoring model was derived with logistic regression and validated externally. Furthermore, in vitro experiments were conducted to validate the expression levels of key genes, and FILIP1L was overexpressed to investigate its effects on macrophage CM.

Results: Analysis of a scRNA-seq dataset employing diverse scoring algorithms revealed a significant increase in CM activity during the lipid plaque stage, particularly in macrophages. By employing machine learning algorithms to analyse bulk RNA-seq data, three feature genes, FABP4, RNASET2, and FILIP1L, were identified as potential hallmark genes for AS. A risk score model constructed with three feature genes demonstrated high accuracy across multiple external datasets. Additionally, these genes were found to be correlated with immune cell infiltration, suggesting their involvement in the immune response to AS. Consensus clustering analysis revealed distinct CM patterns in patients, with Cluster 1 showing increased immune and inflammatory activity. The three feature genes were closely associated with the progression of AS and were implicated in the SPP1 pathway. Cellular experiments confirmed the differential expression of these genes in macrophages before and after intervention with oxidized low-density lipoprotein (oxLDL). FILIP1L overexpression reduces the accumulation of oxLDL in macrophages.

Conclusion: This study provides a comprehensive understanding of macrophage CM in AS and highlights the potential of FABP4, RNASET2, and FILIP1L as diagnostic hallmark genes and therapeutic targets.

Background: Prior studies on the relationship between high-density lipoprotein cholesterol (HDL-C) and depression have reported inconsistent results. Insulin resistance (IR) can alter the composition and function of HDL. This study aims to investigate whether IR influences the association between HDL-C and depression.

Methods: Data from the National Health and Nutrition Examination Survey (2005-2018) were analyzed. Depression was assessed using the Patient Health Questionnaire-9, with a score of ≥ 10 indicating depression. IR was defined by a HOMA2-IR value of ≥ 2.5. Survey-weighted generalized linear models (GLMs) were used to examine the associations between HDL-C, IR, and depression. Multiplicative and additive interaction, along with subgroup analyses, evaluated HDL-C/IR interactions affecting depression. Sensitivity analyses were conducted by: (1) redefining IR, (2) adjusting for total cholesterol and triglycerides in the base models, (3) applying alternative weighting, and (4) including special participants.

Results: The study included 7,779 participants. Survey-weighted GLMs revealed no significant association between HDL-C or IR and depression. However, HDL-C and IR had a significant synergistic effect on the odds of depression (multiplicative scale[P < 0.05] and additive interactions [relative excess risk due to interaction = 3.21]). Subgroup analyses confirmed IR significantly modified HDL-C-depression associations (P_interaction = 0.024). Specifically, in IR-positive individuals, Higher HDL-C linked to increased depression odds (odds ratio = 4.66, 95% confidence interval: 1.23-17.59, P = 0.02).

Conclusion: Elevated HDL-C in the context of IR were associated with increased depression odds. These findings underscore the importance of considering IR when examining the relationship between HDL-C and neuropsychiatric disorders.

Background: Lipid-derived composite indices demonstrate robust epidemiological associations with diverse metabolic and cardiovascular disorders due to their integrative nature; however, their relationship with hyperuricemia (HUA) in older adults remains unexplored. This research examines the links between six blood lipid indices and HUA in older Chinese adults and assesses their diagnostic accuracy for HUA association stratification.

Methods: A total of 3,040 older adults from two communities in Jinan were included in this cross-sectional study. Six blood lipid indices, namely, remnant cholesterol (RC), the atherogenic index of plasma (AIP), Castelli's risk index-I (CRI-I), Castelli's risk index-II (CRI-II), the lipoprotein combination index (LCI), and the atherogenic index (AI), were calculated from the physical examination data. To assess the associations between these lipid indices and HUA, multivariate logistic regression, restricted cubic spline (RCS) analysis, and subgroup analyses were performed. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic accuracy of each lipid index for the overall cohort, male participants, and female participants, with assessments using multiple metrics.

Results: HUA was diagnosed in 484 participants. Significant positive associations were observed between all the composite lipid indices and HUA. A nonlinear association pattern between the LCI and HUA was identified through RCS analysis, whereas linear relationships were observed for the remaining indices. Subgroup analyses revealed consistent directional associations between lipid indices and HUA across all stratified groups. ROC curve analysis demonstrated that the AIP had optimal diagnostic accuracy for HUA in both the overall cohort and the female subgroup, whereas the LCI exhibited superior discriminatory performance among males.

Conclusion: This research revealed statistically significant associations between multiple lipid indices and HUA, with the LCI demonstrating a nonlinear relationship with HUA. The ROC analysis revealed modest diagnostic accuracy across these indices (AUC range: 58.14%-66.03%).

Objective: To characterize the specific pattern of body fat distribution and its association with metabolic syndrome (MetS) among Tibetan adults, an understudied population with distinct high-altitude adaptations, and to identify potential mediating biomarkers in serum lipoprotein profiles.

Methods: A total of 1480 participants from the Tibetan cohort and the NHANES were included. Principal component analysis and Mantel tests were employed to identify Tibetan-specific body fat indicators. Linear models assessed associations with metabolic syndrome (MetS), and mediation analyses evaluated the indirect effects of serum lipoproteins.

Results: Tibetans showed distinct trunk and total fat mass compared to other ethnic/racial groups. Trunk fat percentage was identified as a risk factor for MetS (OR = 1.59, 95% CI: 1.27 ~ 1.91, p = 0.004). The triglycerides to total lipids ratio in low density lipoprotein 3 (L3TGP) and triglycerides to high density lipoprotein cholesterol ratio (TGHCR) exhibited significant mediating effect between trunk fat percentage and MetS (L3TGP:β = 1.7 × 10^- 4g, 95% CI: 4 × 10^- 5~3.6 × 10^- 4, p<0.001;TGHCR: β = 1.8 × 10^- 4g, 95% CI: 4 × 10^- 5~4.6 × 10^- 4, p<0.001).

Conclusions: This study revealed novel evidence for distinct fat distribution in Tibetans, linked to elevated MetS risk. L3TGp and TGHCR were identified as key lipoprotein mediators, supporting the need for environmental- and ethnicity-specific indicators in metabolic risk assessment.

Background: Lipid metabolism may be linked to chronic gastritis, but its causal role remains unclear. While current research emphasizes inflammation, mucosal changes, immune regulation, genetics, and the gut microbiota, the contribution of lipid metabolism is understudied. This study aims to evaluate the impact of serum lipids and the mechanistic roles of lipid-lowering drug targets in chronic gastritis.

Methods: We conducted a cross-sectional study using data from real world. Multivariable logistic regression was performed to assess the association between serum lipid profiles and gastritis. Mendelian randomization (MR) analyses based on genome-wide association study (GWAS) datasets were performed to detect the causal relationship of serum lipids, plasma lipid species, and lipid-lowering drug targets. Experimental validation was conducted using high-fat diet (HFD)-fed mice and chemically induced CAG rat models.

Results: Four thousand sixty one person, including 1,023 patients with chronic atrophic gastritis (CAG), 1,742 with non-atrophic gastritis (NAG), and 1,296 as healthy population were included in the analysis. Through covariates adjustment, TC, ApoA1, and HDL-C showed to be associated with an increased risk of chronic gastritis, whereas TG exhibited a protective effect. MR analysis confirmed a significant inverse causal relationship between TG and gastritis (OR = 0.889, 95% CI: 0.825-0.958). Ten plasma lipid species and lipid-lowering gene targets, including LPL and APOC3, were identified as causally associated with disease risk. Mediation analysis revealed six plasma lipid species as potential intermediaries linking genetic variation to gastritis. In vivo experiments demonstrated progressive hepatic steatosis and mild gastric mucosal changes in HFD-fed mice. Immunohistochemical analysis further revealed a significant reduction in LPL and APOC3 expression in gastric tissue (P < 0.05). In the CAG rat model, histological analysis revealed hepatocyte disarray, edema, and gastric mucosal atrophy. Elevated levels of TNF-α, IL-6, IL-1β and decreased levels of GAS-17 and PG I/II were also observed (P < 0.05). Western blot analyses further confirmed the downregulation of LPL and APOC3 expression in gastric tissue (P < 0.05).

Conclusions: This study provides genetic and experimental evidence, supporting a causal role of lipid metabolism in chronic gastritis. LPL and APOC3 are implicated in its pathogenesis, highlighting potential lipid-targeted strategies for prevention and treatment.