Lipids in Health and Disease最新文献

Cancer remains one of the most formidable diseases affecting human health, particularly because it involves complex reprogramming of metabolic pathways, especially pathways involved in lipid metabolism. Ether lipids (ELs), which alter membrane fluidity and signaling pathways that promote tumor initiation and development, have emerged as important regulators of cancer biology, positioning them as emerging candidate targets for diagnosis and treatment. The main focus of this review is the metabolic dysregulation of ELs in tumors, particularly the metabolic, genetic, and epigenetic processes that promote invasion, proliferation, and drug resistance. This review highlights preclinical treatment strategies designed to target EL synthases, aiming to provide novel perspectives for future translational applications that support more sustainable therapeutic options. In addition to future prospects centered on standardized detection and multiomics integration to improve precision oncology, important hurdles, including tissue specificity and metabolic heterogeneity, are covered.

Introduction: Visceral (VAT), subcutaneous (SFT), and total body fat (FM) contribute to hepatic steatosis, yet their relative and sex-specific effects across total, regional, and site-specific levels remain unclear. We investigated associations between fat depots, standardized skinfold sites, and liver fat (LF) while adjusting for key metabolic covariates.

Methods: In this secondary data analysis of a cross-sectional study, 48 adults (50% women; 49.6 ± 20.9 y; BMI 25.7 ± 3.7 kg/m²) underwent quantitative MRI to assess VAT and LF and ultrasound-based body mapping to quantify total and regional SFT as well as standardized skinfold sites. Bioelectrical impedance analysis determined FM. Regression analyses were conducted with LF as the dependent variable, identifying and controlling for significant covariates (diabetes mellitus [DM], arterial hypertension [aHT], hypercholesterolemia [HC], physical activity, age) (partial r). The Lindeman-Merenda-Gold (LMG) method decomposed total R² into fat-specific contributions. Total fat depots were adjusted for body surface area (BSA).

Results: All regression models examining associations with LF showed total r values ranging from 0.63 to 0.79. In men, DM was the only significant covariate (p = 0.002). Values are given as: partial r, LMG share (% of R²). LF correlated with VAT/BSA (0.40, 51%), total SFT/BSA (0.38, 42%), and FM/BSA (0.36, 51%). Regionally, upper-body SFT (0.40, 45%) and SFT_arms (0.37, 47%) contributed most, whereas lower-body SFT (0.35, 13%) showed minimal impact. The triceps skinfold was the most influential site among skinfolds (0.45, 50%). In women, HC was the only significant covariate (p = 0.02). LF correlated with VAT/BSA (0.40, 49%), total SFT/BSA (0.51, 27%), and FM/BSA (0.47, 36%). Regional models yielded upper-body SFT (0.43, 36%), SFT_arms (0.38, 23%), and lower-body SFT (0.41, 9%). Among single sites, the umbilical skinfold was most relevant (0.43, 36%), followed by the biceps (0.42, 33%).

Conclusion: VAT remains pivotal for LF in both sexes, yet SFT exhibits clear sex- and region-specific relevance. Only subcutaneous fat of the upper-body and arms contributed meaningfully to liver fat. Simple skinfold assessments-particularly triceps in men-may serve as practical indicator for early risk stratification. Larger, prospective cohorts are needed to confirm these findings.

Trial registration: Not applicable this study did not involve any health care intervention.

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.