IGF1 Gene Is Associated With Triglyceride Levels In Subjects With Family History Of Hypertension From The SAPPHIRe And TWB Projects

Higher triglyceride levels are associated with the higher prevalence of layered plaques in non-culprit coronary plaques

High triglyceride (TG) levels have been recognized as a risk factor for cardiovascular events in patients with coronary artery disease (CAD). This study aimed to clarify the association between TG levels and characteristics of non-culprit coronary plaques in patients with CAD. A total of 531 consecutive patients with stable CAD who underwent percutaneous coronary intervention for culprit lesions and optical coherence tomography (OCT) assessment of non-culprit plaques in the culprit vessel were included in this study. The morphology of the non-culprit plaques assessed by OCT imaging were compared between the higher TG (TG ≥ 150 mg/dL, n = 197) and lower TG (TG < 150 mg/dL, n = 334) groups. The prevalence of layered plaques (40.1 vs. 27.5%, p = 0.004) was significantly higher in the higher TG group than in the lower TG group, although the prevalence of other plaque components was comparable between the two groups. High TG levels were an independent factor for the presence of layered plaques (odds ratio 1.761, 95% confidence interval 1.213-2.558, p = 0.003) whereas high low-density lipoprotein cholesterol levels (≥ 140 mg/dL) and low eicosapentaenoic acid/arachidonic acid ratios (< 0.4) were independently associated with a higher prevalence of thin-cap fibroatheroma and macrophages. Higher TG levels were associated with a higher prevalence of layered plaques in non-culprit plaques among patients with stable CAD. These results may partly explain the effect of TG on the progression of coronary plaques and the increased incidence of recurrent events in patients with CAD.

Investigation of the Interplay between Circulating Lipids and IGF-I and Relevance to Breast Cancer Risk: An Observational and Mendelian Randomization Study

BACKGROUND

Circulating lipids and insulin-like growth factor 1 (IGF-I) have been reliably associated with breast cancer. Observational studies suggest an interplay between lipids and IGF-I, however, whether these relationships are causal and if pathways from these phenotypes to breast cancer overlap is unclear.

METHODS

Mendelian randomization (MR) was conducted to estimate the relationship between lipids or IGF-I and breast cancer risk using genetic summary statistics for lipids (low-density lipoprotein cholesterol, LDL-C; high-density lipoprotein cholesterol, HDL-C; triglycerides, TGs), IGF-I and breast cancer from GLGC/UKBB (N = 239,119), CHARGE/UKBB (N = 252,547), and Breast Cancer Association Consortium (N = 247,173), respectively. Cross-sectional observational and MR analyses were conducted to assess the bi-directional relationship between lipids and IGF-I in SHIP (N = 3,812) and UKBB (N = 422,389), and using genetic summary statistics from GLGC (N = 188,577) and CHARGE/UKBB (N = 469,872).

RESULTS

In multivariable MR (MVMR) analyses, the OR for breast cancer per 1-SD increase in HDL-C and TG was 1.08 [95% confidence interval (CI), 1.04-1.13] and 0.94 (95% CI, 0.89-0.98), respectively. The OR for breast cancer per 1-SD increase in IGF-I was 1.09 (95% CI, 1.04-1.15). MR analyses suggested a bi-directional TG-IGF-I relationship (TG-IGF-I β per 1-SD: -0.13; 95% CI, -0.23 to -0.04; and IGF-I-TG β per 1-SD: -0.11; 95% CI, -0.18 to -0.05). There was little evidence for a causal relationship between HDL-C and LDL-C with IGF-I. In MVMR analyses, associations of TG or IGF-I with breast cancer were robust to adjustment for IGF-I or TG, respectively.

CONCLUSIONS

Our findings suggest a causal role of HDL-C, TG, and IGF-I in breast cancer. Observational and MR analyses support an interplay between IGF-I and TG; however, MVMR estimates suggest that TG and IGF-I may act independently to influence breast cancer.

IMPACT

Our findings should be considered in the development of prevention strategies for breast cancer, where interventions are known to modify circulating lipids and IGF-I.

Identification of Hub Genes Associated with Hypertension and Their Interaction with miRNA Based on Weighted Gene Coexpression Network Analysis (WGCNA) Analysis

Background

Hypertension is one of the most widespread health conditions in the world, and the molecular mechanism of it is still unclear. In this study, we identified the hub genes (hub miRNA genes) associated with hypertension and explored the relationship between hypertension miRNA-gene by constructing a mRNA co-expression network and a miRNA co-expression network, which can help to reveal the mechanism and predict the prognosis of hypertension progression.

Material/Methods

Based on gene expression profile data of hypertensive samples from the Gene Expression Omnibus database, WGCNA was used to detect hypertension-related biomarkers and key mRNA and miRNA modules. Then, DAVID was used to perform gene-annotation enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) and miRPath were used for pathway analysis of mRNA and miRNAs genes.

Results

We identified 3 key modules relating to hypertension, 2 mRNA modules named M_saddlebrown and M_greenyellow and 1 miRNA module named M_salmon. In addition, 12 hub genes (RPL21, RPS28, LOC442727/PTGAP10, LOC100129599/RPS29P14, TBXAS1, FCER1G, CFP, FURIN, PECAM1, IGSF6, NCF1C, and LOC285296/UNC93B3) and 7 hub miRNAs (hsa-miR-1268a/b, hsa-miR-513c-3p, hsa-miR-4799-5p, hsa-miR-296-3p, hsa-miR-5195-5p, hsa-miR-219-2-3p, and hsa-miR-548d-5p) relating to hypertension were identified. HIF-1 signaling pathway and insulin signaling pathway were closely related to the 3 key modules. We also discovered 4 miRNAs (hsa-miR-548am-3p, hsa-miR-513c-3p, hsa-miR-182-5p, and hsa-miR-548d-5p) and 6 genes (IGF1R, GSK3B, FOXO1, PRKAR2B, HIF1A, and PIK3R1) were the core nodes in the hypertension-related miRNA-gene network, and hsa-miR-548am-3p was at the center of the network.

Conclusions

These findings will help improve the understanding of the pathogenesis of hypertension, and the discovered genes can serve as signatures for early diagnosis of hypertension.