Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables

Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes.

Effect of the ACE inhibitor lisinopril on mortality in diabetic patients with acute myocardial infarction: data from the GISSI-3 study

BACKGROUND

Mortality of diabetic patients with acute myocardial infarction (MI) remains high despite recent improvement in their management. There is a need to evaluate efficacy and safety of novel treatments of MI in this high-risk population. We evaluated whether treatment with an ACE inhibitor begun within 24 hours from the onset of symptoms is able to decrease mortality and morbidity of diabetic patients with acute MI.

METHODS AND RESULTS

A retrospective analysis of the data of the GISSI-3 study in patients with and without a history of diabetes was performed. Patients with suspected acute MI were randomized to treatment with lisinopril (2.5 to 5 up to 10 mg/d) with or without nitroglycerin (5 to 20 microg I.V. then 10 mg/d) begun within 24 hours and continued for 6 weeks. The main end point was mortality at 6 weeks, and the secondary end point was a combined evaluation of mortality and severe left ventricular dysfunction. Information on diabetic status was available for 18,131 patients (approximately 94% of the total population enrolled), of whom 2790 patients had a history of diabetes. Treatment with lisinopril was associated with a decreased 6-week mortality in diabetic patients (8.7% versus 12.4%; OR, 0.68; 95% CI, 0.53 to 0.86; 37+/-12 lives saved per 1000 treated patients), an effect that was significantly (P<.025) higher than that observed in nondiabetic patients. The survival benefit in diabetics was mostly maintained at 6 months despite withdrawal from treatment at 6 weeks (12.9% versus 16.1%; OR, 0.77; 95% CI, 0.62 to 0.95).

CONCLUSIONS

Early treatment with the ACE inhibitor lisinopril in diabetic patients with acute MI is associated with a decreased 6-week mortality. This beneficial effect supports a widespread and early use of ACE inhibitors in diabetic patients with acute MI. The burden of mortality plus morbidity for ventricular dysfunction in diabetics remains clinically important and warrants further testing of novel therapeutic approaches.