CYP2C9 Genotypes Modify Benzodiazepine-Related Fall Risk: Original Results From Three Studies With Meta-Analysis

Exploring the impact of pharmacogenetics on personalized medicine: A systematic review

INTRODUCTION

Pharmacogenetics evaluates how genetic variations influence drug responses. Nowadays, genetic tests have advanced, becoming more affordable, and its integration is supported by stronger clinical evidence. Guidelines such as those from CPIC (Clinical Pharmacogenetics Implementation Consortium) and resources like PharmGKB facilitate genotype-based prescribing; and organizations like the FDA promote genetic testing before initiating certain medications. Preventive pharmacogenetic panels seem promising, but further research on biomarkers and diverse populations is needed. The aim of this review is to analyze recent evidence on the genotype-drug response relationship to examine how the genetic profile of patients influences the clinical response to treatments, and analyze the areas of research that need further study to advance towards a genetic-based precision medicine.

MATERIALS AND METHODS

A systematic search was conducted on PubMed to identify articles investigating the genotype-drug response relationship. The search strategy included terms such as "pharmacogenetics", "personalized treatment", "precision medicine", "dose adjustment", "individualizing dosing", "clinical routine", and "clinical practice." Clinical trials, observational studies, and meta-analyses published in English or Spanish between 2013 and 2023 were included. The initial search resulted in a total of 136 articles for analysis.

RESULTS

49 articles were included for the final analysis following review by 2 investigators. A relationship between genetic polymorphisms and drug response or toxicity was found for drugs such as opioids, GLP-1 agonists, tacrolimus, oral anticoagulants, antineoplastics, atypical antipsychotics, efavirenz, clopidogrel, lamotrigine, anti-TNFα agents, voriconazole, antidepressants, or statins. However, for drugs like metformin, quetiapine, irinotecan, bisoprolol, and anti-VEGF agents, no statistically significant association between genotype and response was found.

CONCLUSION

The studies analyzed in this review suggest a strong correlation between genetic variability and individual drug responses, supporting the use of pharmacogenetics for treatment optimization. However, for certain drugs like metformin or quetiapine, the influence of genotype on their response remains unclear. More studies with larger sample sizes, greater ethnic diversity, and consideration of non-genetic factors are needed. The lack of standardization in analysis methods and accessibility to genetic testing are significant challenges in this field. As a conclusion, pharmacogenetics shows immense potential in personalized medicine, but further research is required.

Pharmacogenomics of Dementia: Personalizing the Treatment of Cognitive and Neuropsychiatric Symptoms

Dementia is a syndrome of global and progressive deterioration of cognitive skills, especially memory, learning, abstract thinking, and orientation, usually affecting the elderly. The most common forms are Alzheimer's disease, vascular dementia, and other (frontotemporal, Lewy body disease) dementias. The etiology of these multifactorial disorders involves complex interactions of various environmental and (epi)genetic factors and requires multiple forms of pharmacological intervention, including anti-dementia drugs for cognitive impairment, antidepressants, antipsychotics, anxiolytics and sedatives for behavioral and psychological symptoms of dementia, and other drugs for comorbid disorders. The pharmacotherapy of dementia patients has been characterized by a significant interindividual variability in drug response and the development of adverse drug effects. The therapeutic response to currently available drugs is partially effective in only some individuals, with side effects, drug interactions, intolerance, and non-compliance occurring in the majority of dementia patients. Therefore, understanding the genetic basis of a patient's response to pharmacotherapy might help clinicians select the most effective treatment for dementia while minimizing the likelihood of adverse reactions and drug interactions. Recent advances in pharmacogenomics may contribute to the individualization and optimization of dementia pharmacotherapy by increasing its efficacy and safety via a prediction of clinical outcomes. Thus, it can significantly improve the quality of life in dementia patients.

Development of the ADFICE_IT models for predicting falls and recurrent falls in community-dwelling older adults: pooled analyses of European cohorts with special attention to medication

Background

Use of fall prevention strategies requires detection of high-risk patients. Our goal was to develop prediction models for falls and recurrent falls in community-dwelling older adults and to improve upon previous models by using a large, pooled sample and by considering a wide range of candidate predictors, including medications.

Methods

Harmonized data from 2 Dutch (LASA, B-PROOF) and 1 German cohort (ActiFE Ulm) of adults aged ≥65 years were used to fit 2 logistic regression models: one for predicting any fall and another for predicting recurrent falls over 1 year. Model generalizability was assessed using internal–external cross-validation.

Results

Data of 5 722 participants were included in the analyses, of whom 1 868 (34.7%) endured at least 1 fall and 702 (13.8%) endured a recurrent fall. Positive predictors for any fall were: educational status, depression, verbal fluency, functional limitations, falls history, and use of antiepileptics and drugs for urinary frequency and incontinence; negative predictors were: body mass index (BMI), grip strength, systolic blood pressure, and smoking. Positive predictors for recurrent falls were: educational status, visual impairment, functional limitations, urinary incontinence, falls history, and use of anti-Parkinson drugs, antihistamines, and drugs for urinary frequency and incontinence; BMI was a negative predictor. The average C-statistic value was 0.65 for the model for any fall and 0.70 for the model for recurrent falls.

Conclusion

Compared with previous models, the model for recurrent falls performed favorably while the model for any fall performed similarly. Validation and optimization of the models in other populations are warranted.

A Propensity Score Matched Approach to Assess the Associations of Commonly Prescribed Medications with Fall Risk in a Large Harmonized Cohort of Older Ambulatory Persons

Introduction

Several medication classes are considered to present risk factors for falls. However, the evidence is mainly based on observational studies that often lack adequate adjustment for confounders. Therefore, we aimed to assess the associations of medication classes with fall risk by carefully selecting confounders and by applying propensity score matching (PSM).

Methods

Data from several European cohorts, harmonized into the ADFICE_IT cohort, was used. Our primary outcome was time until the first fall within 1-year follow-up. The secondary outcome was a fall in the past year. Our exposure variables were commonly prescribed medications. We used 1:1 PSM to match the participants with reported intake of specific medication classes with participants without. We constructed Cox regression models stratified by the pairs matched on the propensity score for our primary outcome and conditional logistic regression models for our secondary outcome.

Results

In total, 32.6% of participants fell in the 1-year follow-up and 24.4% reported falling in the past year. ACE inhibitor users (prevalence of use 15.3%) had a lower fall risk during follow-up when matched to non-users, with a hazard ratio (HR) of 0.82 (95% CI 0.68–0.98). Also, statin users (prevalence of use 20.1%) had a lower risk, with an HR of 0.76 (95% CI 0.65–0.90). Other medication classes showed no association with risk of first fall. Also, in our secondary outcome analyses, statin users had a significantly lower risk. Furthermore, β-blocker users had a lower fall risk and proton pump inhibitor use was associated with a higher risk in our secondary outcome analysis.

Conclusion

Many commonly prescribed medication classes showed no associations with fall risk in a relatively healthy population of community-dwelling older persons. However, the treatment effects and risks can be heterogeneous between individuals. Therefore, focusing on identification of individuals at risk is warranted to optimize personalized falls prevention.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40266-021-00876-0.

Genetic basis of falling risk susceptibility in the UK Biobank Study

Both extrinsic and intrinsic factors predispose older people to fall. We performed a genome-wide association analysis to investigate how much of an individual’s fall susceptibility can be attributed to genetics in 89,076 cases and 362,103 controls from the UK Biobank Study. The analysis revealed a small, but significant SNP-based heritability (2.7%) and identified three novel fall-associated loci (P_combined ≤ 5 × 10⁻⁸). Polygenic risk scores in two independent settings showed patterns of polygenic inheritance. Risk of falling had positive genetic correlations with fractures, identifying for the first time a pathway independent of bone mineral density. There were also positive genetic correlations with insomnia, neuroticism, depressive symptoms, and different medications. Negative genetic correlations were identified with muscle strength, intelligence and subjective well-being. Brain, and in particular cerebellum tissue, showed the highest gene expression enrichment for fall-associated variants. Overall, despite the highly heterogenic nature underlying fall risk, a proportion of the susceptibility can be attributed to genetics.

Katerina Trajanoska et al. report a genome-wide association study of self-reported falls in UK Biobank participants. They identify three novel fall-associated loci and find that risk of falling shows patterns of polygenic inheritance and a SNP-based heritability of 2.7%.

Optimising Seniors' Metabolism of Medications and Avoiding Adverse Drug Events Using Data on How Metabolism by Their P450 Enzymes Varies with Ancestry and Drug-Drug and Drug-Drug-Gene Interactions

Many individuals ≥65 have multiple illnesses and polypharmacy. Primary care physicians prescribe >70% of their medications and renew specialists’ prescriptions. Seventy-five percent of all medications are metabolised by P450 cytochrome enzymes. This article provides unique detailed tables how to avoid adverse drug events and optimise prescribing based on two key databases. DrugBank is a detailed database of 13,000 medications and both the P450 and other complex pathways that metabolise them. The Flockhart Tables are detailed lists of the P450 enzymes and also include all the medications which inhibit or induce metabolism by P450 cytochrome enzymes, which can result in undertreatment, overtreatment, or potentially toxic levels. Humans have used medications for a few decades and these enzymes have not been subject to evolutionary pressure. Thus, there is enormous variation in enzymatic functioning and by ancestry. Differences for ancestry groups in genetic metabolism based on a worldwide meta-analysis are discussed and this article provides advice how to prescribe for individuals of different ancestry. Prescribing advice from two key organisations, the Dutch Pharmacogenetics Working Group and the Clinical Pharmacogenetics Implementation Consortium is summarised. Currently, detailed pharmacogenomic advice is only available in some specialist clinics in major hospitals. However, this article provides detailed pharmacogenomic advice for primary care and other physicians and also physicians working in rural and remote areas worldwide. Physicians could quickly search the tables for the medications they intend to prescribe.

Objectives, design and main findings until 2020 from the Rotterdam Study

The Rotterdam Study is an ongoing prospective cohort study that started in 1990 in the city of Rotterdam, The Netherlands. The study aims to unravel etiology, preclinical course, natural history and potential targets for intervention for chronic diseases in mid-life and late-life. The study focuses on cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1700 research articles and reports. This article provides an update on the rationale and design of the study. It also presents a summary of the major findings from the preceding 3 years and outlines developments for the coming period.

Benzodiazepines and Z-Drugs: An Updated Review of Major Adverse Outcomes Reported on in Epidemiologic Research

Various adverse events resulting from, or associated with, benzodiazepine and/or Z-drug use have been extensively reported on and discussed in great detail within the biomedical literature. It is widely accepted that motor vehicle accidents and falls leading to fractures in older adults are major adverse events that have been shown to occur more frequently in users of sedative-hypnotic medication, especially of the benzodiazepine and related Z-drug variety. However, the last few years have seen increasing reports in the literature raising the issue of benzodiazepine and Z-drug exposure in the development of other serious medical issues including dementia, infections, respiratory disease exacerbation, pancreatitis, and cancer. This article provides an overview and interpretation on the current state of evidence regarding each of these associations and proposes what gaps in the evidence for drug-exposure–harm associations need to be addressed in the future for the purpose of evaluating causality of harm as it relates to these drugs.

The Rotterdam Study: 2018 update on objectives, design and main results

The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1500 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy ). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.

Falls: the adverse drug reaction of the elderly and the impact of pharmacogenetics

Falls is a frequent type of adverse drug reactions causing significant morbidity and mortality in the elderly. We reviewed, with which drugs the risk of falls is relevant and might depend on genomic variation. Pharmacogenetic variability may contribute to drug-induced falls for instance mediated by impaired drug elimination due to inherited deficiency in enzymes like CYP2C9, CYP2C19 and CYP2D6. The relative role of specific genes and polymorphisms in old age may differ from younger people. Biomarkers for frailty, but also genomic biomarkers might help identifying patients at high risk for drug-induced falls. Many other factors including disease and drug-drug interactions also contribute to risk of falls. Further studies analyzing the impact of genomic variation on the medication-related fall risk in the older adult are urgently needed.