Discussion
In this post-hoc analysis using a large three-country European cohort of patients with T2DM, we found a statistically significant 42% and 37% reduction in risk of CV and non-CV mortality, respectively, in patients whose antidiabetic therapy was modified to include pioglitazone compared with an alternative antidiabetic medication at a similar stage of disease progression. Comparators included a full range of antidiabetic treatment regimens from metformin alone (6%) to insulin used alone or in combination (37%). We also observed a statistically significant 39%, 52% and 40% reduction for MI, stroke and heart failure cause-specific mortality.
There was no evidence of a consistent trend in mortality risk (CV or non-CV) with increasing duration of pioglitazone use or with increasing cumulative dose of pioglitazone. Discontinuation of exposure to pioglitazone within the last year was associated with an increase in the mortality risk. This may be due to switch to insulin therapy for end-stage patients or undocumented pioglitazone treatment during hospitalization that could be missing in drug prescription/dispensing registers.
Strengths of the primary study4 include the large number of patients included in the study, long follow-up period (up to 10 years) and extensive measures to reduce or investigate potential biases. These include a PS matched cohort design to minimize channeling bias related to factors that were hypothesized to influence prescribing decisions and be associated with the bladder cancer outcome, which was the outcome of interest in the main pan-European study. This resulted in the pioglitazone-exposed groups in the matched cohort for the bladder cancer analysis being balanced with regards to the distribution of measured confounders included as exact matching variables or in the PS. Marginal exceptions included CHF at baseline, early cohort entry (2000–2003) and higher duration of medication database membership before CED (5–6 and 7+years), where the standardized differences marginally exceeded 10. The design included further adjustment for matching variables and additional baseline and TV confounders in the main regression model. We also ran numerous stratifications to identify potential effect modification and sensitivity analyses to evaluate robustness of study results. These included an assessment of the impact of BMI, smoking status and TV HbA1c as covariates in countries where these variables were available. Remaining limitations were the potential for residual confounding due to unmeasured covariates such as general health and socioeconomic status11; and left truncation of prescribing data, particularly in SWE where data collection started in July 2005.4 This left truncation may have led to misclassification of incident pioglitazone exposure despite the requirement for at least 1 year of baseline data prior to index.
These strengths and limitations broadly apply to this extended analysis as well. However, the covariates and likely mechanisms through which they influence the association between pioglitazone exposure and outcomes differ for bladder cancer and specific causes of mortality. For example, urinary tract or other cancers diagnosed either prior to baseline or developing during follow-up were not found to be significant confounders in the primary analyses for each cause-specific mortality outcome and therefore not included in the final models. In a study designed specifically to test the association of pioglitazone use with all cancers, patients diagnosed with cancer prior to baseline would likely be removed. There is also a potential risk that residual confounders such as general health and severity of pre-existing conditions have a stronger impact on the association between pioglitazone use and cause-specific mortality and may partially contribute to the observed association. Conversely, there is an increased risk that covariates included in the PS model were not associated with the mortality outcomes. In order to minimize the effect of this, exact matching and individual PS variables were not automatically included in the adjusted models in the analyses reported here. They were tested as confounders using a stepwise methodology but did not meet the threshold for inclusion in the final model. This change in approach decreased the effect size for all-cause mortality from 25% reported in the all-cause mortality manuscript5 to 14% in the UK-HOSP dataset. In FIN, we observed an increase in effect from 46% to 52% which could be due to one or both of this change in methodology or the loss of matched pairs with CEDs in 2011 and resulting loss of covariate balance in multiple PS covariates, notably MI or stroke at baseline, CHF at baseline, year of CED and duration of medication database membership prior to CED. There was no change in SWE.
CV and non-CV mortality risk reductions were observed in all countries. In SWE, this reduction was statistically significant for CV mortality risk (HR 0.35, 95% CI 0.22 to 0.56) but smaller and directional for non-CV mortality risk (HR 0.83, 95% CI 0.62 to 1.10). Similar differences between CV and non-CV mortality risk were not observed in FIN and the UK, although both effect sizes were larger in FIN and the risk reduction for CV mortality risk was not significant in the UK.
Stratified analyses demonstrated statistically significant effect modification by history of diabetic complications, chronic kidney disease and macrovascular disease for both CV and non-CV mortality, with a weaker risk reduction associated with pioglitazone use in patients with a history of these conditions. Diabetic complications and renal disease were more common in the UK than in SWE and FIN. The reduced risk of both CV and non-CV mortality was weaker in the UK sensitivity analysis. Given the use of primary care data in the UK in addition to the secondary care data sources used in all three countries, these differences may in part reflect differences in data recording. This may lead to variation in PS modeling and matching with the UK model better able to minimize channeling bias. Conversely misclassification of exposure may be higher in the UK where analyses rely on prescribing data than in FIN and SWE where dispensing data were used. Assuming that misclassification was non-differential, this would attenuate the observed protective effect in the UK. Country differences may also reflect true differences in diabetic complications and renal disease attenuating the beneficial effect of pioglitazone or differences in other characteristics and care received by patients prescribed pioglitazone in the three countries. Cause of death registers in all three countries use WHO ICD-10 to code underlying cause of death and follow internationally agreed rules.12–15 Differential coding of cause of death between countries and between pioglitazone-exposed and unexposed groups is therefore unlikely.
Stratified analyses for history of TZD at cohort entry showed a stronger protective effect with pioglitazone use in patients with no history of TZD use for non-CV mortality where the effect was statistically significant and CV mortality where the effect was directional. For CV mortality, this risk difference may be explained by the propensity of rosiglitazone to increase CV risk which led to its withdrawal from the UK market in 2010.11 Interestingly, history of TZD use at cohort entry was less common in FIN than in SWE and the UK and the risk reduction for non-CV mortality was greater in the FIN sensitivity analysis.
Liao et al recently published a meta-analysis of nine randomized controlled trials evaluating the effect of pioglitazone on CV and safety outcomes. This meta-analysis demonstrated a significantly reduced risk of major CV events (composite of non-fatal MI, non-fatal stroke and CV death) in patients with diabetes (relative risk (RR) 0.83, 95% CI 0.72 to 0.97) and pre-diabetes or insulin resistance (RR 0.77, 95% CI 0.64 to 0.93).8 This is in line with our finding of a protective effect for pioglitazone on CV mortality compared with other antidiabetic treatments prescribed at similar stage of disease progression. The meta-analysis also demonstrated an increased risk of CHF with pioglitazone (RR 1.32; CI 1.14 to 1.54). This contradicts our findings of a protective effect for CHF mortality (HR 0.60, 95% CI 0.42 to 0.86), perhaps reflecting adherence to warnings about the safety of pioglitazone prescribing in patients with heart failure when used in the real-world setting, following publication of the PROspective pioglitAzone Clinical Trial In macroVascular Events (PROactive) randomized control trial.9
There is little evidence from randomized controlled trials and observational studies examining the association of pioglitazone with non-CV patient outcomes and mortality with which to compare findings from this study. However, no statistically significant difference was observed in the rate of all-cause mortality in Liao et al’s meta-analysis (seven trials; RR 0.93, 95% CI 0.80 to 1.09).8 The vast majority (90%) of the 11 319 subjects in the meta-analysis were enrolled in two large trials: the PROactive16 17 trial in patients with type 2 diabetes at high risk of CV disease and the Insulin Resistance Intervention after Stroke (IRIS) trial in pre-diabetic patients with a history of ischemic stroke or transient ischemic attack. The result from the meta-analysis contradicts the findings from the current analysis and other large observational studies with long follow-up periods that have demonstrated significant reductions in all-cause mortality risk with pioglitazone use over longer follow-up periods.5–7 This lack of agreement is likely to be due to a combination of reduced statistical power in randomized control trials designed primarily to study CV and composite outcomes, residual bias in observational studies primarily designed to study cancer outcomes and differences between the characteristics of patients enrolled or included in the studies.
Cancer was assessed as a safety outcome in Liao et al’s meta-analysis in which a directional protective effect was observed (four trials; RR 0.91, CI 0.77 to 1.08).8 Suggested mechanisms for CV and non-CV benefits of pioglitazone, based on preclinical and clinical studies, include improvements in insulin resistance, decreased hyperglycemia, fat redistribution, renoprotection and improved liver function.9 A protective effect of pioglitazone for non-CV mortality is therefore plausible. Further research is required in this area before conclusions can be drawn.
Conclusions
This extended analysis following a large, observational multidatabase European cohort study found that prescribing pioglitazone compared with an alternative treatment decision at a similar stage of disease progression was associated with a reduction in both CV and non-CV mortality. Further observational and prospective studies that are specifically designed to test the association between pioglitazone use and patient-focused outcomes such as cause-specific mortality are suggested. Observational study designs should consider the nature of prescribing over time in the countries of interest and effect modification by prior TZD prescribing, history of diabetic complications and history of macrovascular disease.