Exploring the Causal Roles of Circulating Remnant Lipid Profile on Cardiovascular and Cerebrovascular Diseases: Mendelian Randomization Study.

BACKGROUND: Causal evidence of circulating lipids especially the remnant cholesterol with cardiovascular and cerebrovascular disease (CVD) is lacking. This research aimed to explore the causal roles of extensive lipid traits especially the remnant lipids in CVD. METHODS: Two-sample Mendelian randomization (TSMR) analysis was performed based on large-scale meta-analysis datasets in European ancestry. The causal effect of 15 circulating lipid profiles including 6 conventional lipids and 9 remnant lipids on coronary heart disease (CHD) and ischemic stroke (IS), as well as the subtypes, was assessed. RESULTS: Apolipoprotein B (Apo B), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) were still important risk factors for CHD and myocardial infarction (MI) but not for IS. Apo B is the strongest which increased the CHD and MI risk by 44% and 41%, respectively. The odds ratios (ORs) of total TG on CHD and MI were 1.25 (95% confidence interval [CI], 1.13-1.38) and 1.24 (95% CI, 1.11-1.38), respectively. A one standard deviation difference increased TG in medium very-low-density lipoproteins (M.VLDL.TG), TG in small VLDL (S.VLDL.TG), TG in very small VLDL (XS.VLDL.TG), TG in intermediate-density lipoproteins (IDL.TG), TG in very large HDL (XL.HDL.TG), and TG in small HDL (S.HDL.TG) particles also robustly increased the risk of CHD and MI by 9-28% and 9-27%, respectively. TG in very/extremely large VLDL (XXL.VLDL.TG and XL.VLDL.TG) were insignificant or even negatively associated with CHD (in multivariable TSMR), and negatively associated with IS as well. CONCLUSION: The remnant lipids presented heterogeneity and two-sided effects for the risk of CHD and IS that may partially rely on the particle size. The findings suggested that the remnant lipids were required to be intervened according to specific components. This research confirms the importance of remnant lipids and provides causal evidence for potential targets for intervention.

Identifying causality, genetic correlation, priority and pathways of large-scale complex exposures of breast and ovarian cancers.

BACKGROUND: Genetic correlations, causalities and pathways between large-scale complex exposures and ovarian and breast cancers need systematic exploration. METHODS: Mendelian randomisation (MR) and genetic correlation (GC) were used to identify causal biomarkers from 95 cancer-related exposures for risk of breast cancer [BC: oestrogen receptor-positive (ER + BC) and oestrogen receptor-negative (ER - BC) subtypes] and ovarian cancer [OC: high-grade serous (HGSOC), low-grade serous, invasive mucinous (IMOC), endometrioid (EOC) and clear cell (CCOC) subtypes]. RESULTS: Of 31 identified robust risk factors, 16 were new causal biomarkers for BC and OC. Body mass index (BMI), body fat mass (BFM), comparative body size at age 10 (CBS-10), waist circumference (WC) and education attainment were shared risk factors for overall BC and OC. Childhood obesity, BMI, CBS-10, WC, schizophrenia and age at menopause were significantly associated with ER + BC and ER - BC. Omega-6:omega-3 fatty acids, body fat-free mass and basal metabolic rate were positively associated with CCOC and EOC; BFM, linoleic acid, omega-6 fatty acids, CBS-10 and birth weight were significantly associated with IMOC; and body fat percentage, BFM and adiponectin were significantly associated with HGSOC. Both GC and MR identified 13 shared factors. Factors were stratified into five priority levels, and visual causal networks were constructed for future interventions. CONCLUSIONS: With analysis of large-scale exposures for breast and ovarian cancers, causalities, genetic correlations, shared or specific factors, risk factor priority and causal pathways and networks were identified.

Exploring the causal pathway from ischemic stroke to atrial fibrillation: a network Mendelian randomization study.

BACKGROUND AND PURPOSE: Previous studies have found ischemic stroke is associated with atrial fibrillation. However, the causal association between ischemic stroke and atrial fibrillation is not clear. Furthermore, the network relationship among ischemic stroke, atrial fibrillation and its risk factors need further attention. This study aims to examine the potential causal association between ischemic stroke and atrial fibrillation and further to explore potential mediators in the causal pathway from ischemic stroke to atrial fibrillation. METHODS: Summary statistics from the ISGC (case = 10,307 and control = 19,326) were used as ischemic stroke genetic instruments, AFGen Consortium data (case = 65,446 and control = 522,744) were used for atrial fibrillation, and other consortia data were used for potential mediators (fasting insulin, white blood cell count, procalcitonin, systolic and diastolic blood pressure, body mass index, waist circumference, and height). Under the framework of network Mendelian randomization, two-sample Mendelian randomization study was performed using summary statistics from several genome-wide association studies. Inverse-variance weighted method was performed to estimate causal effect. RESULTS: Blood pressure mediates the causal pathways from ischemic stroke to atrial fibrillation. The total odds ratio of ischemic stroke on atrial fibrillation was 1.05 (95% confidence interval [CI], 1.02 to 1.07; P = 1.3 × 10-5). One-unit increase of genetically determined ischemic stroke was associated with 0.02 (DBP: 95% CI, 0.001 to 0.034, P = 0.029; SBP: 95% CI, 0.006 to 0.034, P = 0.003) upper systolic and diastolic blood pressure levels. Higher genetically determined systolic and diastolic blood pressure levels were associated with higher atrial fibrillation risk (DBP: RR, 1.18; 95% CI, 1.03 to 1.35; P = 0.012. SBP: RR, 1.18; 95% CI, 1.01 to 1.38; P = 0.04). Specially, we also found the bidirectional causality between blood pressure and ischemic stroke. CONCLUSIONS: Our study provided a strong evidence that raised blood pressure in stroke patients increases the risk of atrial fibrillation and active acute blood pressure lowering can improve the outcome in ischemic stroke patients.

Roles for circulating polyunsaturated fatty acids in ischemic stroke and modifiable factors: a Mendelian randomization study.

BACKGROUND: Available data about the effects of circulating polyunsaturated fatty acids (PUFAs) on ischemic stroke (IS) and its main risk factors remains limited and conflicting. Therefore, we conducted Mendelian randomization (MR) to assess whether genetically predicted PUFA affected IS, lipids and blood pressure (BP). METHODS: Genetic instruments associated with IS were derived from ISGC Consortium (n = 29,633), with lipids were derived from GLGC(n = 188,577), with BP were derived from Neale Lab(n = 337,000). The inverse-variance weighted method was the main analysis to estimate the effect of exposure on outcome. Sensitivity analyses included principal components analysis, MR-Egger, weighted median, and weighted mode. RESULTS: Per SD increases in serum α-linolenic acid (ALA) were associated with lower IS risk, with odd ratio (OR) of 0.867(0.782,0.961), arachidonic acid (AA) were associated with higher IS risk (OR: 1.053(1.014,1.094)). Likewise, Per SD increases in ALA were associated with the lower-level low-density lipoprotein cholesterol(LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC) (ß:-0.122(- 0.144, - 0.101), - 0.159(- 0.182, - 0.135), - 0.148(- 0.171, - 0.126), respectively), AA were associated with the higher-level of LDL-C, HDL-C and TC (ß:0.045(0.034,0.056), 0.059(0.050,0.067), 0.055(0.046,0.063), respectively). Linoleic acid (LA), eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA) and docosapentaenoic acid (DPA) had little or no association with IS, lipids or BP at Bonferroni-corrected significance. Different analytic methods supported these findings. The intercept test of MR-Egger implied no pleiotropy. CONCLUSIONS: High-level plasma ALA was protective for IS but AA was the opposite. LA, EPA, DHA, and DPA had no effects on IS.

[Evaluating the causal relationship between hip circumference and coronary heart disease: a Mendelian randomization study].

OBJECTIVE: To test the causal effect of hip circumference adjusted for body mass index(HCadjBMI) and coronary heart disease(CHD) using a Mendelian randomization analysis. METHODS: Based on genome-wide association study, the associations between the genetic instruments(IVs) and HCadjBMI were obtained from the GIANT consortium(n=211 114, European), the associations between IVs and CHD were derided from CARDIoGRAM consortium(n=86 995, European). The inverse-variance weighted method was used to estimate a pooled OR for the effect of a 1 cm higher HCadjBMI on CHD. Evidence of directional pleiotropy averaged across all variants was sought using MR-Egger regression. RESULTS: A total of 70 genetic variants that reached genome-wide significance and independent of each other were identified as IVs. A combined genetic variants expected to confer a lifetime exposure of per SD higher HCadjBMI was associated with a lower risk of CHD(OR=0. 831, 95%CI 0. 730-0. 946). MR-Egger regression intercept suggested that directional pleiotropy was unlikely to be biasing the result(intercept-0. 0012, P=0. 875). There was no specific single nucleotide polymorphism(SNP) detected by "leave one out" analysis. CONCLUSION: A genetic predisposition to higher HCadjBMI was associated with lower risk of CHD.

Genomic risk score provides predictive performance for type 2 diabetes in the UK biobank.

AIMS: Type 2 diabetes (T2D) is affected by a combination of genetic and environmental factors. However, the comprehensive genomic risk scores (GRSs) for T2D prediction have not been evaluated. METHODS: Using a meta-scoring approach, we developed a metaGRS for T2D; T2D-related traits consist of 1,692 genetic variants in the UK Biobank training set (n = 40,423 + 7,558 events) and evaluate this score in the validation set (n = 303,053). RESULTS: The hazard ratio (HR) for T2D was 1.32 (95% confidence interval [CI]: 1.29-1.35) per standard deviation of metaGRS and was larger than previously published T2D-GRS. Individuals, in the top 25% of metaGRS, have an HR of 2.08 (95%CI: 1.93-2.23) compared with those in the bottom 25%. The addition of metaGRS to all conventional risk factors significantly increased the AUC (P < 0.001). Adding metaGRS to all conventional risk factors significantly improved the reclassification accuracy (continuous net reclassification improvement = 11.8%, 95%CI: 9.2%-14.2%). All analyses adjusted for age, sex, and 10PCs. CONCLUSIONS: The metaGRS significantly improves T2D prediction ability.

Causal effect of sex hormone-binding globulin and testosterone on coronary heart disease: A multivariable and network Mendelian randomization analysis.

BACKGROUND: Although observational studies have shown an association between sex hormone-binding globulin (SHBG), testosterone (T) and cardiovascular diseases (CVD), controversy remains. In this study, we aim to explore the causal effects of SHBG and T on Coronary heart disease (CHD). METHODS: We used univariable, network and multivariable mendelian randomization (MR) analysis to investigate the causal effect of SHBG and T on CHD. We performed inverse variance weighted (IVW) MR as the primary analysis, with the robustness of this approach further tested by other methods in sensitivity analysis. The SHBG and T were collected from the UK Biobank data, about 180,000 men aged 40 to 69 years. CHD was collected from CARDIoGRAMplusC4D 1000 Genomes-based GWAS, which was a meta-analysis including 48 studies and involving 60,801 CHD cases and 123,504 controls. RESULTS: Using univariable MR-IVW, the results suggested that a one standard deviation (SD) increase in SHBG, the risk of CHD decreased by approximately 14% (OR (95% CI): 0.86(0.76,0.97)), and that a SD increase in total testosterone (TT), the risk also decreased, approximately 8% (OR (95% CI): 0.92(0.85,0.99)). Multivariable MR showed that both SHBG and TT had no direct causal effect with CHD (a SD increase in SHBG: OR (95% CI):0.75(0.57,1.00), P = 0.053; a SD increase in TT: OR (95% CI): 1.05(0.90,1.22), P = 0.53). In the network MR analysis, the results suggested that TT might act as mediator in the causal pathway from SHBG to CHD and account for 93% of the total effect of SHBG on CHD, and that SHBG might be a mediator in the causal pathway from TT to CHD and account for 67% of the total effect of TT on CHD. CONCLUSIONS: Genetically predicted SHBG and TT were negatively correlated with CHD in both univariable and network MR, which may provide a causal explanation behind the observed conclusion. In addition, TT and SHBG had a bidirectional causal effect. Further work is required to disentangle the downstream effects of SHBG/TT on CHD and the molecular pathways involved, as the simultaneous regulation of SHBG and TT may make it a viable strategy for the prevention or treatment of CHD.

Causal Pathways from Body Components and Regional Fat to Extensive Metabolic Phenotypes: A Mendelian Randomization Study.

OBJECTIVE: The aim of this study was to explore the causal effects and pathways from body components to extensive metabolic phenotypes. METHODS: Summarized data including 24 metabolic phenotypes from 10 consortiums were used to perform univariate, multivariable, and bidirectional Mendelian randomization analysis based on the network design. RESULTS: For metabolically related biomarkers, a 1-SD increase in body fat mass (BFM) was robustly associated with increased fasting insulin, systolic blood pressure, diastolic blood pressure, and urate and decreased high-density lipoprotein cholesterol levels. For metabolically related diseases, the odds ratios and 95% CIs of a 1-SD increase in BFM were 1.76 (1.37 to 2.25) for type 2 diabetes mellitus (T2DM), 1.11 (1.09 to 1.13) for hypertension, 1.40 (1.25 to 1.57) for coronary artery disease, 1.41 (1.25 to 1.59) for myocardial infarction, 1.25 (1.12 to 1.40) for ischemic stroke, and 1.62 (1.02 to 2.57) for gout. The effects of body fat on diseases were mediated by extensive intermediate biomarkers, including blood pressure, lipids, glycemic traits, and urate. Regional fats had a similar effect with body fat in both absolute and relative scales, whereas fat-free components increased only the risk of T2DM 1.73 (1.11 to 2.68) and chronic kidney disease 1.51 (1.11 to 2.06). CONCLUSIONS: Several potential pathways were found and confirmed the tremendous benefits of fat-lowering measures, including lowering of various regional fats. Future policies or interventions should focus more on the role of body fat.

Causal Effect of the Triglyceride-Glucose Index and the Joint Exposure of Higher Glucose and Triglyceride With Extensive Cardio-Cerebrovascular Metabolic Outcomes in the UK Biobank: A Mendelian Randomization Study.

Background: The causal evidence of the triglyceride-glucose (TyG) index, as well as the joint exposure of higher glucose and triglyceride on the risk of cardio-cerebrovascular diseases (CVD), was lacking. Methods: A comprehensive factorial Mendelian randomization (MR) was performed in the UK Biobank cohort involving 273,368 individuals with European ancestry to assess and quantify these effects. The factorial MR, MR-PRESSO, MR-Egger, meta-regression, sensitivity analysis, positive control, and external verification were utilized. Outcomes include major outcomes [overall CVD, ischemic heart diseases (IHD), and cerebrovascular diseases (CED)] and minor outcomes [angina pectoris (AP), acute myocardial infarction (AMI), chronic IHD (CIHD), heart failure (HF), hemorrhagic stroke (HS), and ischemic stroke (IS)]. Results: The TyG index significantly increased the risk of overall CVD [OR (95% CI): 1.20 (1.14-1.25)], IHD [OR (95% CI): 1.22 (1.15-1.29)], CED [OR (95% CI): 1.14 (1.05-1.23)], AP [OR (95% CI): 1.29 (1.20-1.39)], AMI [OR (95% CI): 1.27 (1.16-1.39)], CIHD [OR (95% CI): 1.21 (1.13-1.29)], and IS [OR (95% CI): 1.22 (1.06-1.40)]. Joint exposure to genetically higher GLU and TG was significantly associated with a higher risk of overall CVD [OR (95% CI): 1.17 (1.12-1.23)] and IHD [OR (95% CI): 1.22 (1.16-1.29)], but not with CED. The effect of GLU and TG was independent of each other genetically and presented dose-response effects in bivariate meta-regression analysis. Conclusions: Lifelong genetic exposure to higher GLU and TG was jointly associated with higher cardiac metabolic risk while the TyG index additionally associated with several cerebrovascular diseases. The TyG index could serve as a more sensitive pre-diagnostic indicator for CVD while the joint GLU and TG could offer a quantitative risk for cardiac metabolic outcomes.