Beyond Genetics-Stratified and Personalised Medicines Using Multiple Parameters

Associations between statins and adverse events in primary prevention of cardiovascular disease: systematic review with pairwise, network, and dose-response meta-analyses

OBJECTIVE

To assess the associations between statins and adverse events in primary prevention of cardiovascular disease and to examine how the associations vary by type and dosage of statins.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

Studies were identified from previous systematic reviews and searched in Medline, Embase, and the Cochrane Central Register of Controlled Trials, up to August 2020.

REVIEW METHODS

Randomised controlled trials in adults without a history of cardiovascular disease that compared statins with non-statin controls or compared different types or dosages of statins were included.

MAIN OUTCOME MEASURES

Primary outcomes were common adverse events: self-reported muscle symptoms, clinically confirmed muscle disorders, liver dysfunction, renal insufficiency, diabetes, and eye conditions. Secondary outcomes included myocardial infarction, stroke, and death from cardiovascular disease as measures of efficacy.

DATA SYNTHESIS

A pairwise meta-analysis was conducted to calculate odds ratios and 95% confidence intervals for each outcome between statins and non-statin controls, and the absolute risk difference in the number of events per 10 000 patients treated for a year was estimated. A network meta-analysis was performed to compare the adverse effects of different types of statins. An E_max model based meta-analysis was used to examine the dose-response relationships of the adverse effects of each statin.

RESULTS

62 trials were included, with 120 456 participants followed up for an average of 3.9 years. Statins were associated with an increased risk of self-reported muscle symptoms (21 trials, odds ratio 1.06 (95% confidence interval 1.01 to 1.13); absolute risk difference 15 (95% confidence interval 1 to 29)), liver dysfunction (21 trials, odds ratio 1.33 (1.12 to 1.58); absolute risk difference 8 (3 to 14)), renal insufficiency (eight trials, odds ratio 1.14 (1.01 to 1.28); absolute risk difference 12 (1 to 24)), and eye conditions (six trials, odds ratio 1.23 (1.04 to 1.47); absolute risk difference 14 (2 to 29)) but were not associated with clinically confirmed muscle disorders or diabetes. The increased risks did not outweigh the reduction in the risk of major cardiovascular events. Atorvastatin, lovastatin, and rosuvastatin were individually associated with some adverse events, but few significant differences were found between types of statins. An E_max dose-response relationship was identified for the effect of atorvastatin on liver dysfunction, but the dose-response relationships for the other statins and adverse effects were inconclusive.

CONCLUSIONS

For primary prevention of cardiovascular disease, the risk of adverse events attributable to statins was low and did not outweigh their efficacy in preventing cardiovascular disease, suggesting that the benefit-to-harm balance of statins is generally favourable. Evidence to support tailoring the type or dosage of statins to account for safety concerns before starting treatment was limited.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42020169955.

Dielectrophoretic analysis of treated cancer cells for rapid assessment of treatment efficacy

Whilst personalized medicine (where interventions are precisely tailored to a patient's genotype and phenotype, as well as the nature and state of the disease) is regarded as an optimal form of treatment, the time and cost associated with it means it remains inaccessible to the greater public. A simpler alternative, stratified medicine, identifies groups of patients who are likely to respond to a given treatment. This allows appropriate treatments to be selected at the start of therapy, avoiding the common "trial and error" approach of replacing a therapy only once it is demonstrated to be ineffective in the patient. For stratification to be effective, tests are required that rapidly predict treatment effectiveness. Most tests use genetic analysis to identify drug targets, but these can be expensive and may not detect changes in the phenotype that affect drug sensitivity. An alternative method is to assess the whole-cell phenotype by evaluating drug response using cells from a biopsy. We assessed dielectrophoresis to assess drug efficacy on short timescales and at low cost. To explore the principle of assessing drug efficacy we examined two cell lines (one expressing EGFR, one not) with the drug Iressa. We then further explored the sensitive cells using combinations of chemotherapeutic and radiotherapeutic therapies. Our results compare with known effects of these cell/treatment combination, and offer the additional benefit over methods such as TUNEL of detecting drug effects such as cell cycle arrest, which do not cause cell death.

The path to personalized medicine in asthma

INTRODUCTION

Asthma is a common respiratory disorder, since about 10% of the population suffer from this disease, and up to 10% have a severe form. Recent findings have allowed a greater and deeper understanding of the pathophysiological mechanisms, distinguishing two groups of patients according to the prevalent cellular population that drives the inflammatory process, and consequentially, to intervene on different cellular targets.

AREAS COVERED

Currently, several biological drugs directly interfering with these pathophysiological mechanisms (namely IgE, IL-4, IL-5, IL-13, and IL-17) are under investigation. Expert commentary: With the elucidation of mechanisms, new-targeted drugs have been developed. Asthma therapy is changing from a 'one size fits all' therapy to a 'precision medicine' model, where we may prescribe the most appropriate treatment for each patient. Moreover, in the near future, the possibility to act a 'sequential bio-combination therapy' can be envisaged, using different biological drugs in the same patient to act on different pathophysiological mechanisms.