Objective. We investigated the relationships between interleukin‐ (IL‐) 1β and IL‐1 receptor antagonist (IL‐1Ra) gene polymorphism and plasma levels in patients with diabetic nephropathy (DN). Methods. The genotype and allele frequency distribution of IL‐1β and IL‐1Ra in 61 patients with DN and 48 healthy controls (HCs) were determined by kompetitive allele‐specific PCR (KASP), and the plasma concentrations of IL‐1β and IL‐1Ra in DN patients and HCs were measured by enzyme‐linked immunosorbent assays (ELISA). Results. Significant differences were detected in the distribution of IL‐1β (−511C/T) genotype and allele frequencies between the DN and HC groups (P < 0.05), with the T genotype being more frequent in DN patients than HCs (OR = 2.84, 95% CI: 1.489–5.416). The IL‐1β (+3953C/T) and IL‐1Ra (+8006C/T) genotypes and allele frequencies were not significantly different between the two groups (P > 0.05). The plasma IL‐1β level was significantly higher (P < 0.01), while the plasma IL‐1Ra concentration was significantly lower in the DN group than the HC group (P < 0.05). Furthermore, the plasma IL‐1β level was significantly different between IL‐1β (−511C/T) locus variants (P < 0.05). Conclusion. The IL‐1β (−511C/T) gene polymorphism was significantly associated with DN risk in the population of northern Guangxi, China, and the T allele maybe responsible for genetic susceptibility to DN.

Long-chain n-3 polyunsaturated fatty acid (PUFA) supplementation has shown potential benefits in the prevention of coronary heart disease (CHD); however, the impact of omega-3 fatty acid levels on CHD risk remains a subject of debate. Here, we aimed to investigate the association between n-3 PUFA levels and the risk of CHD, with particular reference to the subtypes of n-3 PUFA. Methods: Prospective studies and retrospective case-control studies analyzing n-3 PUFA levels in CHD, published up to 30 July 2022, were selected. A random effects meta-analysis was used for pooled assessment, with relative risks (RRs) expressed as 95% confidence intervals (CIs) and standardized mean differences expressed as weight mean differences (WMDs). Subgroup and meta-regression analyses were conducted to assess the impact of n-3 PUFA exposure interval on the CHD subtype variables of the study. Results: We included 20 prospective studies (cohort and nested case-control) and 16 retrospective case-control studies, in which n-3 PUFAs were measured. Higher levels of n-3 PUFAs (ALA, EPA, DPA, DHA, EPA + DHA, total n-3 PUFAs) were associated with a reduced risk of CHD, with RRs (95% CI) of 0.89 (0.81, 0.98), 0.83 (0.72, 0.96); 0.80 (0.67,0.95), 0.75 (0.64, 0.87), 0.83 (0.73, 0.95), and 0.80 (0.70, 0.93), respectively, p < 0.05. CHD patients had significantly lower n-3 PUFA levels compared to healthy controls (p < 0.05). In the subgroup analysis, a significant inverse trend was found for both fatal CHD and non-fatal CHD with n-3 PUFA (EPA + DHA) levels. Also, the link between n-3 PUFA levels in erythrocytes with total CHD was generally stronger than other lipid pools. Conclusions: n-3 PUFAs are significantly related to CHD risk, and these findings support the beneficial effects of n-3 PUFAs on CHD.

Background: Gastric cancer remains a major global health challenge due to its high mortality rate and complex pathophysiological mechanisms. Emerging evidence highlights that dysregulated lipid metabolism contributes to gastric cancer progression and prognosis, but the associations between lipid metabolism-associated genes, gastric cancer patient survival, and tumor immune microenvironment remodeling are not fully elucidated. Methods: We analyzed publicly available omics and clinical data, including RNA sequencing data from 371 gastric cancer samples in The Cancer Genome Atlas database and 433 gastric cancer samples in the Gene Expression Omnibus database. We first curated the top 100 lipid metabolism-associated genes based on relevance scores. Then, univariate Cox regression was used to identify genes significantly associated with overall survival. Consensus clustering was applied to these survival-related genes to define gastric cancer molecular subtypes. Copy number variation analysis was performed to assess genomic alterations of these genes in tumor samples. A prognostic risk model was constructed using least absolute shrinkage and selection operator regression and validated via multivariate Cox regression. Immune infiltration analysis using CIBERSORT and ESTIMATE algorithms was conducted to explore associations between lipid metabolism-associated genes and tumor immune microenvironment characteristics. Results: A total of 3911 differentially expressed genes were identified between gastric cancer and adjacent normal tissues. Among the top 100 lipid metabolism-associated genes, 43 were significantly linked to patient survival, most of which were considered as poor prognostic factors. Copy number variation analysis revealed frequent copy number gains of these genes in tumor samples. Consensus clustering stratified patients into two molecular subtypes (LMAGcluster A and LMAGcluster B), with LMAGcluster A showing significantly worse survival outcomes (median survival: 2.6 years vs. 8.3 years in LMAGcluster B, p < 0.001). LMAGcluster A was also characterized by elevated infiltration of pro-tumor immune cells, such as regulatory T cells and follicular helper T cells. The prognostic model based on 14 key lipid metabolism-associated genes exhibited robust predictive performance, with area under the receiver operating characteristic curve values of 0.702–0.761 in The Cancer Genome Atlas cohort and 0.621–0.638 in the Gene Expression Omnibus cohort for 1-, 3-, and 5-year survival. Conclusion: Lipid metabolism-associated genes are closely associated with gastric cancer prognosis and tumor immune microenvironment remodeling. The identified gene-based molecular subtypes and prognostic model provide novel insights into gastric cancer progression, and the 14 key genes may serve as potential biomarkers and therapeutic targets.