This meta-analysis aimed to compare the efficacy and adverse events, either serious or mild/moderate, of all generic versus brand-name cardiovascular medicines. We searched randomized trials in MEDLINE, Scopus, EMBASE, Cochrane Controlled Clinical Trial Register, and ClinicalTrials.gov (last update December 1, 2014). Attempts were made to contact the investigators of all potentially eligible trials. Two investigators independently extracted and analyzed soft (including systolic blood pressure, LDL cholesterol, and others) and hard efficacy outcomes (including major cardiovascular adverse events and death), minor/moderate and serious adverse events. We included 74 randomized trials; 53 reported ≥1 efficacy outcome (overall sample 3051), 32 measured mild/moderate adverse events (n = 2407), and 51 evaluated serious adverse events (n = 2892). We included trials assessing ACE inhibitors (n = 12), anticoagulants (n = 5), antiplatelet agents (n = 17), beta-blockers (n = 11), calcium channel blockers (n = 7); diuretics (n = 13); statins (n = 6); and others (n = 3). For both soft and hard efficacy outcomes, 100 % of the trials showed non-significant differences between generic and brand-name drugs. The aggregate effect size was 0.01 (95 % CI −0.05; 0.08) for soft outcomes; −0.06 (−0.71; 0.59) for hard outcomes. All but two trials showed non-significant differences in mild/moderate adverse events, and aggregate effect size was 0.07 (−0.06; 0.20). Comparable results were observed for each drug class and in each stratified meta-analysis. Overall, 8 serious possibly drug-related adverse events were reported: 5/2074 subjects on generics; 3/2076 subjects on brand-name drugs (OR 1.69; 95 % CI 0.40–7.20). This meta-analysis strengthens the evidence for clinical equivalence between brand-name and generic cardiovascular drugs. Physicians could be reassured about prescribing generic cardiovascular drugs, and health care organization about endorsing their wider use.Electronic supplementary materialThe online version of this article (doi:10.1007/s10654-015-0104-8) contains supplementary material, which is available to authorized users.

Objective: To investigate whether changes in body energy balance induced by long-term high-fat feeding in adult rats could be associated with modifications in energetic behaviour and oxidative stress of skeletal muscle subsarcolemmal (SS) and intermyofibrillar (IMF) mitochondrial populations. Design: Adult rats were fed low-fat or high-fat diet for 7 weeks. Measurements: Body energy balance and composition analysis together with plasma insulin and glucose level determination in the whole animal. Oxidative capacity, basal and induced proton leaks as well as aconitase and superoxide dismutase activities in SS and IMF mitochondria from skeletal muscle. Results: High-fat fed rats exhibit increased body lipid content, as well as hyperinsulinemia, hyperglycaemia and higher plasma non-esterified fatty acids. In addition, SS mitochondria display lower respiratory capacity and a different behaviour of SS and IMF mitochondria is found in the prevention from oxidative damage. Conclusions: A deleterious consequence of decreased oxidative capacity in SS mitochondria from rats fed high-fat diet would be a reduced utilization of energy substrates, especially fatty acids, which may lead to intracellular triglyceride accumulation, lipotoxicity and insulin resistance development. Our results thus reveal a possible role for SS mitochondria in the impairment of glucose homeostasis induced by high-fat diet.

Given the rapid development of genetic tests, an assessment of their benefits, risks, and limitations is crucial for public health practice. We performed a systematic review aimed at identifying and comparing the existing evaluation frameworks for genetic tests. We searched PUBMED, SCOPUS, ISI Web of Knowledge, Google Scholar, Google, and gray literature sources for any documents describing such frameworks. We identified 29 evaluation frameworks published between 2000 and 2017, mostly based on the ACCE Framework (n = 13 models), or on the HTA process (n = 6), or both (n = 2). Others refer to the Wilson and Jungner screening criteria (n = 3) or to a mixture of different criteria (n = 5). Due to the widespread use of the ACCE Framework, the most frequently used evaluation criteria are analytic and clinical validity, clinical utility and ethical, legal and social implications. Less attention is given to the context of implementation. An economic dimension is always considered, but not in great detail. Consideration of delivery models, organizational aspects, and consumer viewpoint is often lacking. A deeper analysis of such context-related evaluation dimensions may strengthen a comprehensive evaluation of genetic tests and support the decision-making process.

ImportanceNonrandomized studies using insurance claims databases can be analyzed to produce real-world evidence on the effectiveness of medical products. Given the lack of baseline randomization and measurement issues, concerns exist about whether such studies produce unbiased treatment effect estimates.ObjectiveTo emulate the design of 30 completed and 2 ongoing randomized clinical trials (RCTs) of medications with database studies using observational analogues of the RCT design parameters (population, intervention, comparator, outcome, time [PICOT]) and to quantify agreement in RCT-database study pairs.Design, Setting, and ParticipantsNew-user cohort studies with propensity score matching using 3 US claims databases (Optum Clinformatics, MarketScan, and Medicare). Inclusion-exclusion criteria for each database study were prespecified to emulate the corresponding RCT. RCTs were explicitly selected based on feasibility, including power, key confounders, and end points more likely to be emulated with real-world data. All 32 protocols were registered on ClinicalTrials.gov before conducting analyses. Emulations were conducted from 2017 through 2022.ExposuresTherapies for multiple clinical conditions were included.Main Outcomes and MeasuresDatabase study emulations focused on the primary outcome of the corresponding RCT. Findings of database studies were compared with RCTs using predefined metrics, including Pearson correlation coefficients and binary metrics based on statistical significance agreement, estimate agreement, and standardized difference.ResultsIn these highly selected RCTs, the overall observed agreement between the RCT and the database emulation results was a Pearson correlation of 0.82 (95% CI, 0.64-0.91), with 75% meeting statistical significance, 66% estimate agreement, and 75% standardized difference agreement. In a post hoc analysis limited to 16 RCTs with closer emulation of trial design and measurements, concordance was higher (Pearson r, 0.93; 95% CI, 0.79-0.97; 94% meeting statistical significance, 88% estimate agreement, 88% standardized difference agreement). Weaker concordance occurred among 16 RCTs for which close emulation of certain design elements that define the research question (PICOT) with data from insurance claims was not possible (Pearson r, 0.53; 95% CI, 0.00-0.83; 56% meeting statistical significance, 50% estimate agreement, 69% standardized difference agreement).Conclusions and RelevanceReal-world evidence studies can reach similar conclusions as RCTs when design and measurements can be closely emulated, but this may be difficult to achieve. Concordance in results varied depending on the agreement metric. Emulation differences, chance, and residual confounding can contribute to divergence in results and are difficult to disentangle.