Causal Effects of Circulating Lipid Traits on Epithelial Ovarian Cancer: A Two-Sample Mendelian Randomization Study.
Hongen MengRong WangZijun SongFudi WangPublished in: Metabolites (2022)
Ovarian cancer (OC), and particularly epithelial OC (EOC), is an increasing challenge for women. Circulating lipids play different roles in the occurrence and development of OC, but no causal relationship has been confirmed. We used two-sample Mendelian randomization (MR) to evaluate the genetic effects of circulating Apolipoprotein A1 (APOA1), Apolipoprotein B (APOB), high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and triglyc-erides (TG) on EOC risks based on summary data obtained from the UK Biobank and the Ovarian Cancer Association Consortium. We used the inverse-variance weight as the main statistical method and the MR-Egger, weighted median, and MR-PRESSO for sensitivity analysis. A 1-SD increment in HDL gave odds ratios (OR) and 95% confidence intervals (CI) of OR = 0.80 (95% CI: 0.69-0.93), OR = 0.77 (95% CI: 0.66-0.90), and OR = 0.76 (95% CI: 0.63-0.90) for low malignant potential OC (LMPOC), low-grade low malignant OC (LGLMSOC), and low malignant serous OC (LMSOC), respectively. Genetic liability due to TG was associated with an increased risk of LGLMSOC and LGSOC and a suggestive association with an increased risk of LMSOC ( p = 0.001, p = 0.007, and p = 0.027, respectively). Circulating HDL was negatively associated with the risk of LMPOC, LGLMSOC, and LMSOC, while elevated circulating TG levels genetically predicted an increased risk of LGLMSOC and LGSOC. Further research is needed to investigate the causal effects of lipids on EOC and potential intervention and therapeutic targets.
Keyphrases
- low density lipoprotein
- low grade
- high grade
- high density
- magnetic resonance
- genome wide
- human health
- randomized controlled trial
- risk assessment
- body mass index
- fatty acid
- copy number
- magnetic resonance imaging
- electronic health record
- weight loss
- climate change
- pregnant women
- adipose tissue
- computed tomography
- artificial intelligence
- deep learning
- network analysis
- cervical cancer screening