Dataset Information

An investigation of the effects of lipid-lowering medications: genome-wide linkage analysis of lipids in the HyperGEN study.

ABSTRACT:

Background

Use of anti-hyperlipidemic medications compromises genetic analysis because of altered lipid profiles. We propose an empirical method to adjust lipid levels for medication effects so that the adjusted lipid values substitute the unmedicated lipid values in the genetic analysis.

Results

Published clinical trials were reviewed for HMG-CoA reductase inhibitors and fibric acid derivatives as mono-drug therapy. HMG-CoA reductase inhibitors showed similar effects in African Americans (AA) and non-African Americans (non-AA) for lowering total cholesterol (TC, -50.7 mg/dl), LDL cholesterol (LDL-C, -48.1 mg/dl), and triglycerides (TG, -19.7 mg/dl). Their effect on increasing HDL cholesterol (HDL-C) in AA (+0.4 mg/dl) was lower than in Non-AA (+2.3 mg/dl). The effects of fibric acid derivatives were estimated as -46.1 mg/dl for TC, -40.1 mg/dl for LDL-C, and +5.9 mg/dl for HDL-C in non-AA. The corresponding effects in AA were less extreme (-20.1 mg/dl, -11.4 mg/dl, and +3.1 mg/dl). Similar effect for TG (59.0 mg/dl) was shown in AA and non-AA. The above estimated effects were applied to a multipoint variance components linkage analysis on the lipid levels in 2,403 Whites and 2,214 AA in the HyperGEN study. The familial effects did vary depending on whether the lipids were adjusted for medication use. For example, the heritabilities increased after medication adjustment for TC and LDL-C, but did not change significantly for HDL-C and TG.

Conclusion

Ethnicity-specific medication adjustments using our empirical method can be employed in epidemiological and genetic analysis of lipids.

SUBMITTER: Wu J

PROVIDER: S-EPMC2045675 | biostudies-literature | 2007 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An investigation of the effects of lipid-lowering medications: genome-wide linkage analysis of lipids in the HyperGEN study.

Wu Jun J Province Michael A MA Coon Hilary H Hunt Steven C SC Eckfeldt John H JH Arnett Donna K DK Heiss Gerardo G Lewis Cora E CE Ellison R Curtis RC Rao Dabeeru C DC Rice Treva T Kraja Aldi T AT

BMC genetics 20070910

<h4>Background</h4>Use of anti-hyperlipidemic medications compromises genetic analysis because of altered lipid profiles. We propose an empirical method to adjust lipid levels for medication effects so that the adjusted lipid values substitute the unmedicated lipid values in the genetic analysis.<h4>Results</h4>Published clinical trials were reviewed for HMG-CoA reductase inhibitors and fibric acid derivatives as mono-drug therapy. HMG-CoA reductase inhibitors showed similar effects in African A ...[more]

PMID: 17845730

Similar Datasets

Project description:Statins effectively lower total and plasma LDL-cholesterol, but the magnitude of decrease varies among individuals. To identify single nucleotide polymorphisms (SNPs) contributing to this variation, we performed a combined analysis of genome-wide association (GWA) results from three trials of statin efficacy.Bayesian and standard frequentist association analyses were performed on untreated and statin-mediated changes in LDL-cholesterol, total cholesterol, HDL-cholesterol, and triglyceride on a total of 3932 subjects using data from three studies: Cholesterol and Pharmacogenetics (40 mg/day simvastatin, 6 weeks), Pravastatin/Inflammation CRP Evaluation (40 mg/day pravastatin, 24 weeks), and Treating to New Targets (10 mg/day atorvastatin, 8 weeks). Genotype imputation was used to maximize genomic coverage and to combine information across studies. Phenotypes were normalized within each study to account for systematic differences among studies, and fixed-effects combined analysis of the combined sample were performed to detect consistent effects across studies. Two SNP associations were assessed as having posterior probability greater than 50%, indicating that they were more likely than not to be genuinely associated with statin-mediated lipid response. SNP rs8014194, located within the CLMN gene on chromosome 14, was strongly associated with statin-mediated change in total cholesterol with an 84% probability by Bayesian analysis, and a p-value exceeding conventional levels of genome-wide significance by frequentist analysis (P = 1.8 x 10(-8)). This SNP was less significantly associated with change in LDL-cholesterol (posterior probability = 0.16, P = 4.0 x 10(-6)). Bayesian analysis also assigned a 51% probability that rs4420638, located in APOC1 and near APOE, was associated with change in LDL-cholesterol.Using combined GWA analysis from three clinical trials involving nearly 4,000 individuals treated with simvastatin, pravastatin, or atorvastatin, we have identified SNPs that may be associated with variation in the magnitude of statin-mediated reduction in total and LDL-cholesterol, including one in the CLMN gene for which statistical evidence for association exceeds conventional levels of genome-wide significance.PRINCE and TNT are not registered. CAP is registered at Clinicaltrials.gov NCT00451828.

Project description:BackgroundPrevious studies have yielded conflicting findings regarding the association between circulating lipids and lipid-lowering drugs with urinary stones, and the causal relationship between the two remains inconclusive.ObjectiveThis study aimed to assess the causal relationship between circulating lipids (Triglycerides [TG], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C], apolipoprotein A [APOA], apolipoprotein B [APOB] and Pure hypercholesterolaemia), lipid-lowering drugs (HMGCR [HMG-CoA reductase] inhibitors and PCSK9[Proprotein Convertase Subtilisin/Kexin Type 9] inhibitors) and the risk of urinary stones, using genetic data.MethodsGenetic instrumental variables (GIVs) for circulating lipids and lipid-lowering drugs were obtained from the UK Biobank and existing literature. Outcome data were extracted from a genetic association database with 3,625 urinary stone cases and 459,308 controls. Two-sample MR analysis, employing the TwoSampleMR software package in R 4.2.3, was conducted to assess the associations between multiple exposures. The primary outcome was determined using the inverse variance weighted (IVW) method for the causal relationship between exposure and outcome, while additional methods such as MR-Egger, weighted median, simple mode, and weighted mode were utilized as supplementary analyses. Robustness of the Mendelian Randomization (MR) analysis results was assessed through leave-one-out analysis and funnel plots.ResultsThe MR analysis revealed a significant association between elevated TG levels per 1 standard deviation and the occurrence of urinary stones (odds ratio [OR]: 1.002, 95% confidence interval [CI]: 1.000-1.003, P = 0.010). However, no significant association was observed between factors other than TG exposure and the risk of urinary stone occurrence across all methods(LDL-C: [OR], 1.001; 95% [CI], 1.000-1.003, P=0.132;HDL-C: [OR], 0.999; 95% [CI], 0.998-1.000, P=0.151;APOA:[OR] being 1.000 (95% [CI], 0.999-1.001, P=0.721;APOB: [OR] of 1.001 (95% [CI], 1.000-1.002, P=0.058;Pure hypercholesterolaemia: [OR] of 1.015 (95% [CI], 0.976-1.055, P=0.455) and lipid-lowering drugs (HMGCR inhibitors: [OR], 0.997; 95% [CI], 0.990-1.003, P=0.301 and PCSK9 inhibitors:[OR], 1.002; 95% [CI], 1.000-1.005, P=0.099).ConclusionOur findings provide conclusive evidence supporting a causal relationship between an increased risk of urinary stones and elevated serum TG levels. However, we did not find a significant association between urinary stone occurrence and the levels of LDL-C, HDL-C, APOA, APOB, Pure hypercholesterolaemia and lipid-lowering drugs.

Project description:BACKGROUND:Carotid artery intima-media thickness (cIMT) progression is a surrogate marker of atherosclerosis with a high predictive value for future CVD risk. This study evaluates the comparative efficacies of lipid lowering, hypoglycemic, antihypertensive and antiplatelet medications on cIMT progression. METHODS:We conducted a network meta-analysis (NMA) to evaluate the relative efficacies of several drug classes in modifying cIMT progression. After a literature search in several electronic databases, studies were selected by following predetermined eligibility criteria. An inverse variance-heterogeneity model was used for NMA. Sensitivity analyses were performed to check the reliability of the overall NMA, and transitivity analyses were performed to examine the effects of modifiers on the NMA outcomes. RESULTS:Data were taken from 47 studies (15,721 patients; age: 60.2 years [95% confidence interval (CI) 58.8, 61.6]; BMI: 27.2 kg/m2 [95% CI 26.4, 28.0]; and gender: 58.3% males [95% CI 48.3, 68.3]). Treatment duration was 25.8 months [95% CI 22.9, 28.7]. Of the 13 drug classes in the network, treatment with phosphodiesterase III inhibitors was the most effective in retarding annual mean cIMT against network placebo (weighted mean difference (WMD) - 0.059 mm [95% CI - 0.099, - 0.020) followed by the calcium channel blockers (WMD - 0.055 mm [95% CI - 0.099, 0.001]) and platelet adenosine diphosphate inhibitors (WMD - 0.033 mm [95% CI - 0.058, 0.008]). These 3 drug classes also attained the same positions when the NMA was conducted by using first-year changes in mean cIMT. In transitivity analyses, longer treatment duration, higher body mass index (BMI), and a higher baseline cIMT were found to be independently associated with a lesser reduction in annual mean cIMT. However, in a multivariate analysis with these 3 modifiers, none of these factors was significantly associated with annual change in mean cIMT. In the placebo group, age was inversely associated with annual change in mean cIMT independently. CONCLUSION:Phosphodiesterase III inhibitors and calcium channel blockers are found more effective than other drug classes in retarding cIMT progression. Age, BMI, and baseline cIMT may have some impact on these outcomes.

Dataset Information

An investigation of the effects of lipid-lowering medications: genome-wide linkage analysis of lipids in the HyperGEN study.

Background

Results

Conclusion

Publications

An investigation of the effects of lipid-lowering medications: genome-wide linkage analysis of lipids in the HyperGEN study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets