Dataset Information

When drug treatments bias genetic studies: Mediation and interaction.

ABSTRACT: BACKGROUND:Increasingly, genetic analyses are conducted using information from subjects with established disease, who often receive concomitant treatment. We determined when treatment may bias genetic associations with a quantitative trait. METHODS:Graph theory and simulated data were used to explore the impact of drug prescriptions on (longitudinal) genetic effect estimates. Analytic derivations of longitudinal genetic effects are presented, accounting for the following scenarios: 1) treatment allocated independently of a genetic variant, 2) treatment that mediates the genetic effect, 3) treatment that modifies the genetic effect. We additionally evaluate treatment modelling strategies on bias, the root mean squared error (RMSE), coverage, and rejection rate. RESULTS:We show that in the absence of treatment by gene effect modification or mediation, genetic effect estimates will be unbiased. In simulated data we found that conditional models accounting for treatment, confounding, and effect modification were generally unbiased with appropriate levels of confidence interval coverage. Ignoring the longitudinal nature of treatment prescription, however (e.g. because of incomplete records in longitudinal data), biased these conditional models to a similar degree (or worse) as simply ignoring treatment. CONCLUSION:The mere presence of (drug) treatment affecting a GWAS phenotype is insufficient to bias genetic associations with quantitative traits. While treatment may bias associations through effect modification and mediation, this might not occur frequently enough to warrant general concern at the presence of treated subjects in GWAS. Should treatment by gene effect modification or mediation be present however, current GWAS approaches attempting to adjust for treatment insufficiently account for the multivariable and longitudinal nature of treatment trajectories and hence genetic estimates may still be biased.

SUBMITTER: Schmidt AF

PROVIDER: S-EPMC6713387 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

When drug treatments bias genetic studies: Mediation and interaction.

Schmidt Amand F AF Heerspink Hiddo J L HJL Denig Petra P Finan Chris C Groenwold Rolf H H RHH

PloS one 20190828 8

<h4>Background</h4>Increasingly, genetic analyses are conducted using information from subjects with established disease, who often receive concomitant treatment. We determined when treatment may bias genetic associations with a quantitative trait.<h4>Methods</h4>Graph theory and simulated data were used to explore the impact of drug prescriptions on (longitudinal) genetic effect estimates. Analytic derivations of longitudinal genetic effects are presented, accounting for the following scenarios ...[more]

PMID: 31461463

Similar Datasets

Project description:BACKGROUND AND OBJECTIVES:Darolutamide is a novel androgen receptor (AR) antagonist approved for the treatment of nonmetastatic castration-resistant prostate cancer (nmCRPC). Accordingly, the drug-drug interaction (DDI) potential of darolutamide was investigated in both nonclinical and clinical studies. METHODS:In vitro studies were performed to determine the potential for darolutamide to be a substrate, inducer or inhibitor for cytochrome P450 (CYP) isoforms, other metabolizing enzymes and drug transporters. A phase I drug-interaction study in healthy volunteers evaluated the impact of co-administering rifampicin [CYP3A4 and P-glycoprotein (P-gp) inducer] and itraconazole [CYP3A4, P-gp and breast cancer resistance protein (BCRP) inhibitor] on the pharmacokinetics of darolutamide. Two further phase I studies assessed the impact of co-administering oral darolutamide on the pharmacokinetics of midazolam (sensitive CYP3A4 substrate) and dabigatran etexilate (P-gp substrate) and the impact on the pharmacokinetics of co-administered rosuvastatin [a substrate for BCRP, organic anion-transporting polypeptide (OATP)1B1, OATP1B3 and organic anion transporter (OAT)3]. RESULTS:In vitro, darolutamide was predominantly metabolized via oxidative biotransformation catalyzed by CYP3A4 and was identified as a substrate for P-gp and BCRP. The enzymatic activity of nine CYP isoforms was not inhibited or slightly inhibited in vitro with darolutamide, and a rank order and mechanistic static assessment indicated that risk of clinically relevant DDIs via CYP inhibition is very low. In vitro, darolutamide exhibited no relevant induction of CYP1A2 or CYP2B6 activity. Inhibition of BCRP-, P-gp-, OAT3-, MATE1-, MATE2-K-, OATP1B1- and OATP1B3-mediated transport was observed in vitro. Phase I data showed that darolutamide exposure increased 1.75-fold with co-administered itraconazole and decreased by 72% with rifampicin. Co-administration of darolutamide with CYP3A4/P-gp substrates showed no effect or only minor effects. Rosuvastatin exposure increased 5.2-fold with darolutamide because of BCRP and probably also OATPB1/OATPB3 inhibition. CONCLUSIONS:Darolutamide has a low potential for clinically relevant DDIs with drugs that are substrates for CYP or P-gp; increased exposure of BCRP and probably OATP substrates was the main interaction of note.

Project description:Background and objectiveObservational studies may provide valuable evidence on real-world causal effects of drug effectiveness in patients with coronavirus disease 2019 (COVID-19). As patients are usually observed from hospital admission to discharge and drug initiation starts during hospitalization, advanced statistical methods are needed to account for time-dependent drug exposure, confounding and competing events. Our objective is to evaluate the observational studies on the three common methodological pitfalls in time-to-event analyses: immortal time bias, confounding bias and competing risk bias.MethodsWe performed a systematic literature search on 23 October 2020, in the PubMed database to identify observational cohort studies that evaluated drug effectiveness in hospitalized patients with COVID-19. We included articles published in four journals: British Medical Journal, New England Journal of Medicine, Journal of the American Medical Association and The Lancet as well as their sub-journals.ResultsOverall, out of 255 articles screened, 11 observational cohort studies on treatment effectiveness with drug exposure-outcome associations were evaluated. All studies were susceptible to one or more types of bias in the primary study analysis. Eight studies had a time-dependent treatment. However, the hazard ratios were not adjusted for immortal time in the primary analysis. Even though confounders presented at baseline have been addressed in nine studies, time-varying confounding caused by time-varying treatment exposure and clinical variables was less recognized. Only one out of 11 studies addressed competing event bias by extending follow-up beyond patient discharge.ConclusionsIn the observational cohort studies on drug effectiveness for treatment of COVID-19 published in four high-impact journals, the methodological biases were concerningly common. Appropriate statistical tools are essential to avoid misleading conclusions and to obtain a better understanding of potential treatment effects.

Dataset Information

When drug treatments bias genetic studies: Mediation and interaction.

Publications

When drug treatments bias genetic studies: Mediation and interaction.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets