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Propranolol and the risk of hospitalized myopathy: translating chemical genomics findings into population-level hypotheses

Propranolol and the risk of hospitalized myopathy:Translating chemical genomics findings intopopulation-level hypothesesSoko Setoguchi, MD, DrPH, a ,d John M. Higgins, MD, b,d,e Helen Mogun, MS, a Vamsi K. Mootha, MD, c andJerry Avorn, MD a Boston, MA Background A recent large-scale, chemical screening study raised the hypothesis that propranolol may increase the riskof myopathy. We tested this hypothesis in a large population to assess whether (1) propranolol use is associated with anincreased risk of myopathy and (2) the concurrent use of propranolol with a statin may further increase risk of myopathy.
Methods New users of propranolol and other β-blockers (BBs) aged ≥65 were identified using data from Medicare anddrug benefit programs in 2 states (1994-2005). The primary end point studied was hospitalization for myopathy orrhabdomyolysis. We used stratified Cox proportional hazards regression to estimate the multivariate-adjusted effect ofpropranolol compared to other BBs and controlled for demographic variables, risk factors for myopathy, other comorbidities,and health service use measures. We also assessed whether co-use of propranolol and statin further increases the risk, byincluding an interaction term for use of propranolol and statins.
Results We identified 9,304 initiators of propranolol and 130,070 initiators of other BBs and found 30 cases ofhospitalized myopathy in 15,477 person-years (PYs) of propranolol use and 523 in 343,132 PYs of other BB use. Comparingpropranolol with other BB users, the adjusted hazard ratio was 1.45 (95% CI 1.00-2.11) for myopathy and 1.48 (95% CI0.82-2.67) for rhabdomyolysis. We could not detect interaction between propranolol and statins due to limited power. Similarresults were observed when propranolol users were compared to other antihypertensive drug users.
Conclusions Propranolol may be associated with a 45% increased risk of hospitalized myopathy in the elderly. Ourstudy illustrates how results from in vitro chemical screens can be translated into hypotheses about drug toxicity at thepopulation level. (Am Heart J 2010;159:428-33.) We recently performed a large-scale, chemical genomic metoprolol or atenolol, gave rise to a very similar screen of nearly 2,500 drugs in cultured mouse muscle signature of toxicity. Moreover, the study revealed that and discovered a molecular and physiologic signature of combination treatment of these cells with a statin and statin toxicity.The signature of toxicity reported in this propranolol gave rise to an additive toxicity in a dose- cell-based study is consistent with previous reports dependent manner. A subsequent study demonstrated suggesting that statins may cause myopathy via a increased cellular toxicity for propranolol as compared to mitochondrial Surprisingly, we found that other β-blockers (BBs) in a different cell type.
treatment of muscle cells with propranolol, but not These cell-based studies raise the possibility that propranolol use in humans might be associated with From the aDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of increased risk of in vivo mitochondrial toxicity and Medicine, Boston, MA, bDepartment of Pathology, Brigham and Women's Hospital and possibly clinically significant myopathy. In the current Harvard Medical School, Boston, MA, and cCenter for Human Genetic Research, article, we conducted a cohort study using large popula- Massachusetts General Hospital, Broad Institute of MIT and Harvard, and Department of tion-based health care use databases to assess whether (1) Systems Biology, Harvard Medical School, Boston, MA.
dCofirst authors.
propranolol may be associated with an increased risk of eCurrent address: Center for Systems Biology and Department of Pathology, Massachusetts myopathy and (2) the concurrent use of propranolol with a General Hospital, and Department of Systems Biology, Harvard Medical School.
statin may further increase the risk of myopathy.
Submitted June 9, 2009; accepted December 2, 2009.
Reprint requests: Soko Setoguchi, MD, DrPH, Division of Pharmacoepidemiology andPharmacoeconomics, Brigham and Women's Hospital, 1620 Tremont St, Suite 3030,Boston, MA 02130.
We conducted a cohort study pooling health care use 2010, Mosby, Inc. All rights reserved.
databases from 2 states: (1) Medicare beneficiaries enrolled in Table I. Characteristics of cohort patients with age ≥65 (Medicare and Pharmacy Assistance Program in PA and NJ combined; 1995-2005) Covariates were assessed during 1 year before initiation of study drugs. Values represent percentage for binary variables and median (interquartile range) for continuous variables.
ACEI, Angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BB, beta-blocker; CCB, calcium-channel blocker; DMARD, disease-modifying antirheumatic drug; SSRI, selective serotonin reuptake inhibitor; THI, thiazide diuretic.
the Pharmaceutical Assistance Contract for the Elderly (PACE) in threshold but less than approximately $35,000, thus, including Pennsylvania from January 1, 1994, to December 31, 2005, and primarily lower middle-class elderly. The linked Medicare/state (2) Medicare beneficiaries enrolled in the Pharmaceutical drug benefit program data provide basic demographic and Assistance to the Aged and Disabled (PAAD) or in Medicaid in coded diagnostic and procedural information as well as the state of New Jersey from January 1, 1994, to December 31, complete pharmacy dispensing information with high accura- 2005. Both drug benefit programs in Pennsylvania and New cy.The Institutional Review Board of the Brigham and Jersey provided comprehensive pharmacy coverage with a small Women's Hospital and Massachusetts General Hospital ap- or no copayment. Patients were eligible for coverage by PACE or proved this study, and data use agreements were established.
PAAD if their income is above the Medicaid annual income All potentially traceable personal identifiers were removed from the data before analyses to protect patients' privacy. The authors rhabdomyolysis would be a subset of hospitalized myopathy had full access to the data and take full responsibility for its cases because ICD-9 codes used to defined rhabdomyolysis in integrity. All authors have read and agreed to the article the algorithm by Andrade and the new code for rhambdomyo- lysis (728.88) were part of ICD-9 codes used to define In the databases, we identified a cohort of subjects aged ≥65 years who were newly started on propranolol or another β-blocker. New use of propranolol was defined as having filled a prescription for propranolol during the study period and not Potential confounders were measured during the 12 months having used the drug during the 12 months before the index use.
before the exposure to propranolol or other BB, using diagnosis Patients who used other BBs during the 12 months before the and procedure codes and/or prescription information in the index use of propranolol were considered as new users of data, including demographic variables; risk factors for myopathy propranolol. The same definition was applied to define new including renal impairment, hypothyroidism, hyperthyroidism, users of other BBs. This “new user” design is preferable because liver disease, other comorbidities; and use of other medications including prevalent users can underestimate the true effect of an (see for the list of comorbidities and medications).
exposure by missing events that might have occurred soon afterthe first exposures, as well as by focusing on patients who were less susceptible to a given risk.All patients were required tohave at least one filled prescription and use of at least one Cox proportional hazards regression was used to estimate the clinical service during each of 2 consecutive 6-month periods unadjusted, age-sex–adjusted, and multivariate-adjusted effect of before the index use of any BB, to ensure ongoing eligibility and propranolol versus other BB on the occurrence of hospitalization to assess prior comorbid conditions. The earliest observation for myopathy or rhabdomyolysis. Patients from the 2 states were included in the analyses was January 1, 1996.
combined and analyzed in stratified Cox proportional hazardsregression, allowing different baseline incidence of the out-comes between the 2 regions. The model was also stratified by calendar year to adjust for any trend or variation in the exposure The exposure of interest was use of propranolol. We chose and outcomes. We also adjusted potential confounders using other BB users as a comparison group because the comparison propensity score methoThe propensity scores were between active users of similar medications can help protect estimated as a probability of receiving propranolol compared against confounding by indication and other selection biases to another BB, given all potential covariates that predicted the related to use of preventive medications.Cohort follow-up use of propranolol. To estimate propensity scores, we also started at the first prescription of propranolol or other BB during included several covariates not included in the final multivariate the study period. We did not allow patients to cross over models: use of other medications and health service. Dose between categories and instead censored them as soon as they response was assessed by replacing indicator variables with stopped taking the exposure medication of interest. We assessed propranolol categories versus the comparison drug.
dose response by determining the daily dose of propranolol To test the hypothesis that concomitant use of statin and based on the closest dispensing to the outcome or censoring propranolol may be associated with a further increase in the risk event, we then categorized the dose into low (≤40 mg) and high of myopathy, we assessed the most recent use of statins in (N40 mg), given a median dose of 40 mg/d.
patients taking propranolol and other comparison drugs beforethe previously specified outcomes or censoring events. We then included an interaction term between propranolol and statin usein the fully adjusted Cox model to determine whether there was Subjects were censored at the earliest of (1) the last use of a synergistic effect for statins and propranolol.
propranolol or other BBs, (2) death, or (3) end of the study We conducted sensitivity analyses using other comparison period. The last use of propranolol or other BB was defined as groups within the same population. We identified new users of 3 the last date of prescription plus the number of days supplied, other classes of antihypertensive medications: angiotensin plus a 14-day grace period to account for the time lag between blockers (ABs), calcium-channel blockers (CCBs), and thiazide filling a prescription and the actual intake of the medication. The diuretics (THI). We repeated the same analyses comparing primary end point studied was the first incidence of severe propranolol users to AB users, CCB users, THI users, and all myopathy after the initiation of the study drugs, defined as comparison drug users (AB + CCB + THI + other BB users).
hospitalization in an acute care facility with myopathy-relatedcodes including International Classification of Diseases, NinthEdition (ICD-9) code for rhabdomyolysis (ICD-9 of 710.4, 728.8X, 728.9, 729.1, 791.3, 359.4. 359.8, 359.9) as the primary or secondary diagnosis listed in the discharge summary. The We identified 9,304 initiators of propranolol and secondary outcome was the first incidence of rhabdomyolysis. A 130,070 initiators of other BBs. Among the 130,070 specific ICD-9 code for rhabdomyolysis (728.88) became other BB users, the most frequent BB used was metoprolol available only after October 2003. We therefore defined (57%) followed by atenolol (27%). We also identified new rhabdomyolysis using a previously developed algorithm byAndrade et up to October 2003. After October 2003, we users of AB (n = 110,328), CCB (n = 70,976), or THI (n = defined rhabdomyolysis as hospitalization in an acute care 81,411) for sensitivity analyses. presents the facility with rhabdomyolysis (ICD-9 of 728.88) as the primary or characteristics of the study population aged ≥65 and older secondary diagnosis in the discharge summary. The cases of measured during the 12-month period before exposure to Table II. Number of cases, person-years, and incidence rate of likely that the misclassification bias brought the estimate toward the null. After October 2003, the specific ICD-9code for rhabdomyolysis became available. Analysis of the subset of the data with patients at risk for developingrhabdomyolysis after October 2003 found that the HR for rhabdomyolysis comparing propranolol to other BB users was 1.96 (95% CI 0.97-3.97) and the HR comparing propranolol to all other comparison drug users was 2.09 The HR of hospitalized myopathy for high-dose propranolol (HR 1.68, 95% CI 1.01-2.77) was somewhat higher than that for low-dose propranolol (HR 1.26, 95% CI 0.74-2.15), suggesting a possible dose response.
Propensity score analyses yielded similar results to the multivariate analyses, with the propensity score-adjustedHR having narrower CIs ). These results were P-Y, Total person-years; IR, incidence rate (per 10000).
consistent when propranolol users were compared tousers of AB, CCB, and THI separately.
the study drugs. Age was similar across the groups, butpropranolol users generally had fewer comorbidities Concurrent use of statins and lack of synergistic effect compared to other BB users or all other users of Concurrent use of any statin was assessed at the time of comparison drugs. Depression, hyperthyroidism, liver initiating propranolol, the time of the last prescription disease, and migraine were slightly more common in before the outcome, or at a censoring event. The co-use propranolol users. Propranolol users were more likely to of statins was relatively infrequent, for example, the use use antipsychotics, selective serotonin reuptake inhibitor, of statin at the time of last dispensing was 17% (n = 1,557) and nonselective serotonin reuptake inhibitor antidepres- for propranolol users, 30% (n = 39,171) for other BB sants than users of comparison drugs and were less likely users, 25% (n = 19,600) for angiotensin-converting than other BB users to have a history of antiplatelet enzyme inhibitor/angiotensin receptor blocker users, 21% (n = 15,251) for CCB users and 23% (n = 27,657)for THI users. We did not find any evidence of a synergistic effect between the use of propranolol and statins in causing myopathy. Among 30 myopathy We identified 30 cases of hospitalized myopathy in hospitalizations for the propranolol users, only 6 were 15,477 person-years of propranolol use and 523 cases in exposed to statin at the same time. We therefore did not 343,132 person-years of other BB use (and 12 pursue further analyses assessing additive interactions.
admissions for rhabdomyolysis in propranolol users and Concurrent use with any statin was also assessed at the 227 in other BB users. Compared to other BB users or all time of initiating the study drug, but we also did not find other comparison drug users (other BB, AB, CCB, and THI any significant interaction between propranolol and combined), the incidence of hospitalization for myopathy and rhabdomyolysis was elevated in propranolol users(crude rate ratio of 1.3:1.7 for hospitalized myopathy and1.2:1.6 for rhabdomyolysis).
Using very large population-based databases of typical Association between propranolol and myopathy elderly patients, we found that propranolol might be associated with a 45% increase in the risk of severe After adjusting for potential confounders in the Cox myopathy. We also found a statistically nonsignificant proportional hazards models, we continued to find a 48% increase in the risk of rhabdomyolysis in propranolol significantly increased risk of hospitalized myopathy in users. These results were consistent using multiple propranolol users compared to other BB users or all other comparison groups. These results are compatible with comparison drug users ). For rhabdomyolysis, the hypothesis raised by an integrated high-throughput we found a similar degree of increase in the risk, but the chemical biology and gene expression study.Although 95% CIs were wider due to a smaller numbers of events there have been a few case reports associating propran- (hazard ratio [HR] for rhabdomyolysis comparing pro- pranolol users to other BB users was 1.48, 95% CI 0.82- to our knowledge, this is the first study to 2.67). Because the definition of rhabdomyolysis by suggest that propranolol may be associated with hospi- Andrade et alhad positive predictive value of 75%, it is talization for myopathy at the population level.
Table III. Cox analyses (propranolol vs other BB) Cox analyses (propranolol vs all other comparison drug users)Unadjusted (crude)⁎ ⁎ Cox proportional hazards model stratified by calendar year of exposure and state with study time as a time-scale.
† Cox proportional hazards model stratified by calendar year of exposure and state with study time as a time-scale and age, sex, and race in the model.
‡ Cox proportional hazard model stratified by calendar year of exposure and state with study time as a time-scale and age, sex, and adjusted for demographic information (age, race,gender), comorbidities (history of acute coronary syndrome, other coronary artery disease, cerebrovascular disease, peripheral vascular disease, hypertension, chronic kidneydisease, chronic airway disease, diabetes, cancer, depression/anxiety, hypothyroidism, hyperthyroidism, liver disease, anemia, depression, inflammatory myositis), and healthservice use measures (prior nursing home, number of prior hospitalization, number of physician's visits, and number of medications).
One of the promises of modern biomedical research is to sufficiently specific. However, such misclassification is inform best practices for patient management with the likely to be nondifferential and therefore may have led to insights emerging from high-throughput chemical and underestimation of the true risk. Second, our population- genomic studies that are now possible. Many previous based database does not have precise clinical information attempts to extrapolate isolated molecular studies or isolated on all risk factors for myopathy such as body mass index or genomic or proteomic analyses to human populations have history of muscle injuries, including creatine kinase failed because these limited experimental systems do not elevations. Although we adjusted for liver dysfunction in always reflect the true complex dynamics of the organism our study, the condition is likely to be undercoded in the We note that the experimental findings motivating this claims data and therefore likely to lead to residual current study are based on an integrated analysis of multiple confounding. However, by selecting users of classes of experimental data sources including studies of cell viability, drugs that have similar indications as a comparison group, gene expression, and cell physiologySome compounds we may have been able to minimize the potential may show a false-positive correlation based on the analysis confounding. Finally, propranolol can be used for of any single source of data, but a correlation based on treatment of hyperthyroidism, which is also associated the integration of several different experimental datasets as with myopathy. To address this potential confounding, in this analysis is much more likely to yield a robust we assessed diagnoses for hyperthyroidism and adjusted prediction. The precise molecular mechanism of the for the condition in the analyses. We also conducted toxicity of propranolol in muscle is still unclear and requires analyses excluding patients who had diagnosis for hyperthyroidism, which yielded similar hazard estimates The in vitro study by Wagner et noted at least an (HR was 1.55 with 95% CI of 0.86-2.81 for rhabdomyolysis additive, and possibly synergistic, effect of propranolol and and 1.45 with 95% CI of 0.98-2.13 for hospitalized statins in causing muscle toxicity. In the present popula- myopathy comparing propranolol to other BB initiators).
tion-level study, we were not able to detect a synergistic However, residual confounding by misclassification of effect because small numbers of dually exposed patients hyperthyroidism cannot be ruled out. Nonetheless, the limited the power of these data to elucidate this degree of residual confounding is expected to be small relationship. Alternatively, an additive effect of the due to the low prevalence of the condition and relatively combination of statin and propranolol on mitochondrial small imbalance of the condition in our population.
toxicity may not necessarily translate into a synergistic Our data indicate that propranolol use may pose a 45% effect of these drugs at the population level. Limited by the greater risk of severe hospitalized myopathy compared to small number of cases in the the propranolol users, we other BBs or other antihypertensive medications. These were unable to pursue further analyses testing interactions.
findings need to be confirmed in other populations. More The present study has a few limitations. First, we used generally, this study illustrates the potential value of ICD-9 diagnosis codes or a previously validated algorithm translating findings from chemical and genomic screen- to define hospitalization for myopathy or rhabdomyolysis.
ing studies into testable hypotheses about drug efficacy These codes and validated algorithm may not have been 4. Stergachis AS. Record linkage studies for postmarketing drug surveillance: data quality and validity considerations. Drug Intell Clin We thank Shawn Murphy and Henry Chueh and the Partners Health Care Research Patient Data Registry 5. McKenzie DA, Semradek J, McFarland BH, et al. The validity of group for facilitating use of their database.
Medicaid pharmacy claims for estimating drug use among elderly Dr Setoguchi had full access to all of the data in the nursing home residents: the Oregon experience. J Clin Epidemiol study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
6. Ray WA. Evaluating medication effects outside of clinical trials: new- user designs. Am J Epidemiol 2003;158:915-20.
7. Glynn RJ, Schneeweiss S, Wang PS, et al. Selective prescribing led to overestimation of the benefits of lipid-lowering drugs. J Clin Epidemiol Financial support: Divisional fund; American Diabetes Association/Smith Family Foundation.
8. Andrade SE, Graham DJ, Staffa JA, et al. Health plan administrative databases can efficiently identify serious myopathy and rhabdo-myolysis. J Clin Epidemiol 2005;58:171-4.
9. Rubin DB. Estimating causal effects from large data sets using 1. Wagner BK, Kitami T, Gilbert TJ, et al. Large-scale chemical dissection propensity scores. Ann Intern Med 1997;127(8 Pt 2):757-63.
of mitochondrial function. Nat Biotechnol 2008;26:343-51.
10. Blessing W, Walsh JC. Myotonia precipitated by propranolol 2. Dirks AJ, Jones KM. Statin-induced apoptosis and skeletal myopathy.
Am J Physiol Cell Physiol 2006;291:C1208-12.
11. Forfar JC, Brown GJ, Cull RE. Proximal myopathy during beta- 3. Cheong HI, Johnson J, Cormier M, et al. In vitro cytotoxicity of eight beta-blockers in human corneal epithelial and retinal pigment 12. Sobie EA, Guatimosim S, Song LS, et al. The challenge of molecular epithelial cell lines: comparison with epidermal keratinocytes and medicine: complexity versus Occam's razor. J Clin Invest 2003;111: dermal fibroblasts. Toxicol In Vitro 2008;22:1070-6.


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