Examining the effects of the CLU and APOE polymorphisms' combination on coronary artery disease complexed with type 2 diabetes mellitus

Apolipoprotein E E3/E4 genotype is associated with an increased risk of type 2 diabetes mellitus complicated with coronary artery disease

OBJECTIVE

Dyslipidemia is a co-existing problem in patients with diabetes mellitus (DM) and coronary artery disease (CAD), and apolipoprotein E (APOE) plays an important role in lipid metabolism. However, the relationship between the APOE gene polymorphisms and the risk of developing CAD in type 2 DM (T2DM) patients remains controversial. The aim of this study was to assess this relationship and provide a reference for further risk assessment of CAD in T2DM patients.

METHODS

The study included 378 patients with T2DM complicated with CAD (T2DM + CAD) and 431 patients with T2DM alone in the case group, and 351 individuals without DM and CAD were set as controls. The APOE rs429358 and rs7412 polymorphisms were genotyped by polymerase chain reaction (PCR) - microarray. Differences in APOE genotypes and alleles between patients and controls were compared. Multiple logistic regression analysis was performed after adjusting for age, gender, body mass index (BMI), history of smoking, and history of drinking to access the relationship between APOE genotypes and T2DM + CAD risk.

RESULTS

The frequencies of the APOE ɛ3/ɛ4 genotype and ε4 allele were higher in the T2DM + CAD patients, and the frequencies of the APOE ɛ3/ɛ3 genotype and ε3 allele were lower than those in the controls (all p < 0.05). The T2DM + CAD patients with ɛ4 allele had higher level in low-density lipoprotein cholesterol (LDL-C) than those in patients with ɛ2 and ɛ3 allele (p < 0.05). The results of logistic regression analysis showed that age ≥ 60 years old, and BMI ≥ 24.0 kg/m2 were independent risk factors for T2DM and T2DM + CAD, and APOE ɛ3/ɛ4 genotype (adjusted odds ratio (OR) = 1.93, 95% confidence interval (CI) = 1.18-3.14, p = 0.008) and ɛ4 allele (adjusted OR = 1.97, 95% CI = 1.23-3.17) were independent risk factors for T2DM + CAD. However, the APOE genotypes and alleles were not found to have relationship with the risk of T2DM.

CONCLUSIONS

APOE ε3/ε4 genotype and ε4 allele were independent risk factors for T2DM complicated with CAD, but not for T2DM.

Investigation of the Relationship Between Serum Low-Density Lipoprotein Cholesterol Levels with Genetic Polymorphisms, Gut Microbiota, and Nutrition

Background: To prevent cardiovascular disease (CVD), it is important to determine the factors that are associated with its development. High serum low-density lipoprotein (LDL) cholesterol (LDL-C) levels are a modifiable prevention and treatment target known to contribute to the development of CVD, but the factors affecting blood cholesterol levels, including LDL-C, remain controversial. Objective: In this study, the factors (genetic, nutritional, and gut microbiota) thought to be effective on serum LDL-C levels were discussed from a holistic perspective, and the effects of the relationship between these factors on LDL-C levels were examined. Methods: The study was carried out with 609 adults (48% male) who applied to a private health institution between 2016 and 2022. Results: It was observed that serum LDL-C levels were positively correlated with body mass index (BMI) (P = 0.000) and different ApoE alleles had significant effects on LDL-C levels. It was observed that the highest LDL-C levels were in the ɛ4+ group, followed by ɛ3+ and ɛ2+ groups, respectively (P = 0.000). Results showed that dietary cholesterol and fiber consumption did not significantly affect serum LDL-C levels (P = 0.705 and P = 0.722, respectively). It was also observed that enterotypes and the butyrate synthesis potential of intestinal microbiota did not cause significant changes in serum LDL-C levels (P = 0.369 and P = 975, respectively). Conclusion: Serum LDL-C levels are affected by modifiable factors such as BMI and nonmodifiable factors such as APOE genotype. By identifying these factors and conducting further studies on them, new ways to improve serum LDL-C levels, which is an important factor in the development of CVD, can be identified. In addition, no significant effect of gene-nutrient or microbiota-nutrient interactions on serum LDL-C levels was detected. Further research is needed, especially on the relationship between intestinal microbiota and serum LDL levels.

Investigation of miR-26a-5p and miR-19a-3p expression levels in angiographically confirmed coronary artery disease

BACKGROUND

MicroRNAs have been found to have an essential role in cardiovascular diseases. In previous experiments, the changed expressions of miR-26a-5p and miR-19a-3p were confirmed in patients with severe coronary atherosclerosis by miRNA microarrays. However, the role of two miRNAs in coronary artery diseases (CAD) still needs to be investigated further. Our current study aimed to analyse two miRNAs in angiographically confirmed CAD and non-CAD with insignificant coronary stenosis. This study aimed to identify the potential diagnostic value of circulating miRNA with CAD.

METHODS

The CAD patients (n = 50) and non-CAD controls (n = 43) were studied. miRNAs (miR-26a-5p and miR-19a-3p) were quantified by TaqMan miRNA assays using real-time PCR. We subsequently assessed the diagnostic value of the miRNAs and correlations of miRNA with clinical parameters. Target prediction tools were utilised to identify miRNA target genes.

RESULTS

The expression of miR-26a-5p was significantly increased in CAD compared to non-CAD controls (p < 0.05). Tertile groups were formed according to the expression levels of miRNAs, and high expression tertile (T3) was compared with low expression tertile (T1). It was found that CAD presence was more prevalent in T3 of miR-26a-5p, and the frequency of diabetes was higher in T3 of miR-19a-3p. There were significant correlations between miRNAs and diabetes risk factors such as HbA1c, glucose levels, and BMI (p < 0.05).

CONCLUSIONS

Our findings show that miR-26a-5p expression is altered in CAD presence while miR-19a-3p expression is different in diabetes. Both miRNAs are closely related to risk factors of CAD, therefore, could be therapeutic targets for CAD treatment.

Insulin resistance in Alzheimer's disease: The genetics and metabolomics links

Alzheimer's disease (AD) is a neurodegenerative disease with significant socioeconomic burden worldwide. Although genetics and environmental factors play a role, AD is highly associated with insulin resistance (IR) disorders such as metabolic syndrome (MS), obesity, and type two diabetes mellitus (T2DM). These findings highlight a shared pathogenesis. The use of metabolomics as a downstream systems' biology (omics) approach can help to identify these shared metabolic traits and assist in the early identification of at-risk groups and potentially guide therapy. Targeting the shared AD-IR metabolic trait with lifestyle interventions and pharmacological treatments may offer promising AD therapeutic approach. In this narrative review, we reviewed the literature on the AD-IR pathogenic link, the shared genetics and metabolomics biomarkers between AD and IR disorders, as well as the lifestyle interventions and pharmacological treatments which target this pathogenic link.

A Signature for Smoking Status of Coronary Heart Disease Patients through Weighted Gene Coexpression Network Analysis

Background. Smoking is one of the risk factors of coronary heart disease (CHD), while its underlying mechanism is less well defined. Purpose. To identify and testify 6 key genes of CHD related to smoking through weighted gene coexpression network analysis (WGCNA), protein-protein interaction (PPI) network analysis, and pathway analysis. Methods. CHD patients’ samples were first downloaded from Gene Expression Omnibus (GEO). Then, genes of interest were obtained after analysis of variance (ANOVA). Thereafter, 23 coexpressed modules that were determined after genes with similar expression were incorporated via WGCNA. The biological functions of genes in the modules were researched by enrichment analysis. Pearson correlation analysis and PPI network analysis were used to screen core genes related to smoking in CHD. Results. The violet module was the most significantly associated with smoking ( $r=-0.28$ , $p=0.006$ ). Genes in this module mainly participated in biological functions related to the heart. Altogether, 6 smoking-related core genes were identified through bioinformatics analyses. Their expressions in animal models were detected through the animal experiment. Conclusion. This study identified 6 core genes to serve as underlying biomarkers for monitoring and predicting smoker’s CHD risk.