Non-response to (statin) therapy: the importance of distinguishing non-responders from non-adherers in pharmacogenetic studies

Trompet, Stella; Postmus, Iris; Slagboom, P. Eline; Heijmans, B.T.; Smit, Roelof A.J.; Maier, Andrea B.; Buckley, Brendan M.; Sattar, Naveed; Stott, David J.; Ford, Ian; Westendorp, Rudi G.J.; Craen, Anton J. M. de; Jukema, J. Wouter

doi:10.1007/s00228-015-1994-9

Cited by 30 publications

(30 citation statements)

References 19 publications

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“…However, in many cases where expected response to statin therapy is not observed, poor adherence to therapy or discontinuation of therapy is the usual cause 81 , 82 . Such cases will not be helped by pharmacometabolomics.…”

Section: Existing Examples Of Use Of Metabolomics In Trials and Intermentioning

confidence: 99%

Applying metabolomics to cardiometabolic intervention studies and trials: past experiences and a roadmap for the future: Table 1.

et al. 2016

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Metabolomics and lipidomics are emerging methods for detailed phenotyping of small molecules in samples. It is hoped that such data will: (i) enhance baseline prediction of patient response to pharmacotherapies (beneficial or adverse); (ii) reveal changes in metabolites shortly after initiation of therapy that may predict patient response, including adverse effects, before routine biomarkers are altered; and( iii) give new insights into mechanisms of drug action, particularly where the results of a trial of a new agent were unexpected, and thus help future drug development. In these ways, metabolomics could enhance research findings from intervention studies. This narrative review provides an overview of metabolomics and lipidomics in early clinical intervention studies for investigation of mechanisms of drug action and prediction of drug response (both desired and undesired). We highlight early examples from drug intervention studies associated with cardiometabolic disease. Despite the strengths of such studies, particularly the use of state-of-the-art technologies and advanced statistical methods, currently published studies in the metabolomics arena are largely underpowered and should be considered as hypothesis-generating. In order for metabolomics to meaningfully improve stratified medicine approaches to patient treatment, there is a need for higher quality studies, with better exploitation of biobanks from randomized clinical trials i.e. with large sample size, adjudicated outcomes, standardized procedures, validation cohorts, comparison witth routine biochemistry and both active and control/placebo arms. On the basis of this review, and based on our research experience using clinically established biomarkers, we propose steps to more speedily advance this area of research towards potential clinical impact.

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Section: Existing Examples Of Use Of Metabolomics In Trials and Intermentioning

confidence: 99%

Applying metabolomics to cardiometabolic intervention studies and trials: past experiences and a roadmap for the future: Table 1.

et al. 2016

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“…As mention in the method section, only 24 out of 120 articles were evaluated by JADAD grading scale and our evaluation resulted in 22 articles with a score of zero (very poor quality), one article with three points and one article that scored 4 as high-quality evidence. [9,10]…”

Section: Resultsmentioning

confidence: 99%

“…[12] In data used from PROSPER study which was a prospective multicenter randomised placebocontrolled trial with JADAD score of 3 showed a significant association of statin-induced myopathy (SIM) with rs2900478 (SLCO1B1) SNPs. [9] In two meta-analysis and one case-control studies, an association was demonstrated between SLCO1B1 c.521T>C polymorphism and statininduced myopathy. [13][14][15] The last two population-based case-control articles in Czech and Japanese population showed no association between SLCO1B1 gene polymorphism and risk of myalgia/myopathy in these two specific population.…”

Section: Slco1b1 Genementioning

confidence: 98%

Clinical evidence of pharmacogenetics of statins: systematic literature review

Aldawsari

Shawaqfeh

2019

J Pharm Health Serv Res

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Objectives A systematic review was conducted to explore the clinical evidence of pharmacogenetic testing with statins in a personalised approach to minimise the incidence of adverse drug reaction (ADR), ensure better response and optimise medication adherence. Methods Systematic literature review using PubMed and EMBASE/Medline database that includes all clinically relevant pharmacogenomic studies involving statins. Key findings Twenty-four articles were evaluated for a total of 120 articles screened. The included studies were classified according to study design (two randomised controlled trial studies (RCTs), three meta-analysis studies, two interventional studies, four prospective studies, 10 case-control studies, two cross-sectional studies and one data analysis study). There were 23 different genetic polymorphisms related to statins were studied clinically with the specified outcome either LDL-lowering response or adverse drug reaction. Those genes were diverse and involve metabolic enzymes, transporters and other receptors. Conclusions Many studies suggest a strong association between clinical outcomes and certain genes relevant to statins. However, these studies were observational and only a few were randomised controlled trials (RCTs). The strength of evidence to support pharmacogenetic testing for statin is not adequate.

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“…When the outcome of interest has a continuous distribution, sampling individuals from the extremes of the outcome distribution (eg, comparing high with non‐responders in LDL‐C reduction after starting statin treatment) may greatly increase statistical power when faced with budgetary restrictions for genotyping . However, as shown for non‐responders to statin therapy in the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) trial, issues of treatment non‐adherence are especially important to consider here . This strategy may also be promising when rare variants are investigated, as their prevalence may be greater on the extreme ends of the outcome spectrum …”

Section: Study Designsmentioning

confidence: 99%

“…22 However, as shown for non-responders to statin therapy in the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) trial, issues of treatment non-adherence are especially important to consider here. 23 This strategy may also be promising when rare variants are investigated, as their prevalence may be greater on the extreme ends of the outcome spectrum. 24 There are some notable challenges in performing case-control studies, the first and foremost being the selection of an appropriate In general, the catchment area for a hospital or clinic is likely to differ for different diseases, which will need to be considered when sampling controls.…”

Section: Outcome-based Designsmentioning

confidence: 99%

A critical appraisal of pharmacogenetic inference

et al. 2018

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In essence, pharmacogenetic research is aimed at discovering variants of importance to gene-treatment interaction. However, epidemiological studies are rarely set up with this goal in mind. It is therefore of great importance that researchers clearly communicate which assumptions they have had to make, and which inherent limitations apply to the interpretation of their results. This review discusses considerations of, and the underlying assumptions for, utilizing different response phenotypes and study designs popular in pharmacogenetic research to infer gene-treatment interaction effects, with a special focus on those dealing with of clinical effects of drug treatment.

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Non-response to (statin) therapy: the importance of distinguishing non-responders from non-adherers in pharmacogenetic studies

Cited by 30 publications

References 19 publications

Applying metabolomics to cardiometabolic intervention studies and trials: past experiences and a roadmap for the future: Table 1.

Applying metabolomics to cardiometabolic intervention studies and trials: past experiences and a roadmap for the future: Table 1.

Clinical evidence of pharmacogenetics of statins: systematic literature review

A critical appraisal of pharmacogenetic inference

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