eTable. Diagnosis Groups and Their Definitions
Leuschen J, Mortensen EM, Frei CR, Mansi EA, Panday V, Mansi I. Association of Statin Use With CataractsA Propensity Score–Matched Analysis. JAMA Ophthalmol. 2013;131(11):1427-1434. doi:10.1001/jamaophthalmol.2013.4575
Copyright 2013 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.
Cataracts are a main cause of low vision; with the growing elderly population, the incidence of cataracts is likely to increase. Investigators have previously hypothesized that statin antioxidant effects may slow the natural aging process of the lens.
To compare the risks for development of cataracts between statin users and nonusers.
A propensity score–matched cohort analysis using retrospective data from October 1, 2003, to March 1, 2010. A propensity score–matched cohort of statin users and nonusers was created using 44 variables.
Database of a military health care system.
Based on medication fills during fiscal year 2005, patients were divided into 2 groups: (1) statin users (received at least a 90-day supply of statin) and (2) nonusers (never received a statin throughout the study). Among 46 249 patients meeting study criteria, we identified 13 626 statin users and 32 623 nonusers.
Use of statin therapy for more than 90 days.
Main Outcomes and Measures
Primary analysis examined the risks for cataract in the propensity score–matched cohort. Secondary analyses examined the risks for cataract in patients with no comorbidities according to the Charlson Comorbidity Index (patients with no Charlson comorbidity). A sensitivity analysis was conducted to repeat the secondary analysis in patients taking statins for durations of 2, 4, and 6 years.
For our primary analysis, we matched 6972 pairs of statin users and nonusers. The risk for cataract was higher among statin users in comparison with nonusers in the propensity score–matched cohort (odds ratio, 1.09; 95% CI, 1.02-1.17). In secondary analyses, after adjusting for identified confounders, the incidence of cataract was higher in statin users in comparison with nonusers (odds ratio, 1.27; 95% CI, 1.15-1.40). Sensitivity analysis confirmed this relationship.
Conclusions and Relevance
The risk for cataract is increased among statin users as compared with nonusers. The risk-benefit ratio of statin use, specifically for primary prevention, should be carefully weighed, and further studies are warranted.
Age-related lens opacities (cataracts) are a main cause of low vision and blindness.1 In addition to being a financial burden that amount to an annual cost of $4.7 billion in the United States,2 cataracts affect quality of life.3 With the growing elderly population, the incidence of cataracts is likely to increase. Therefore, understanding and optimizing the modifiable risk factors for developing lens opacities must be a public health priority.4
Hydroxymethyl glutaryl coenzyme A reductase inhibitors (statins) are commonly prescribed for prevention of cardiovascular disease. Investigators have previously hypothesized that statins’ antioxidant and anti-inflammatory effects on the lens may slow the aging process of the lens nucleus and epithelium.5 Observational studies have reported conflicting results; some studies have demonstrated increased risk for cataract in association with statin use,6,7 while others have demonstrated decreased risk.8,9 The wider use of statins in primary prevention is heavily debated.10,11
The objective of this study was to compare the risks for cataract development between statin users and nonusers within a military health care system where patients have equal access and standards of health care.
This study was approved by the institutional review board at the Brooke Army Medical Center. This study was exempt from obtaining informed consent from patients because it was an observational study on preexisting data.
This was a retrospective cohort study of all adult patients enrolled in the San Antonio Military Multi-Market Area as Tricare Prime or Plus. Using the Military Health System Management Analysis and Reporting Tool, we retrieved medical encounters data and medication fill histories. The Military Health System Management Analysis and Reporting Tool is a powerful tool that has been used in health care administration,12- 14 use,12,15,16 and outcomes research.17,18 The data include the full spectrum of clinical care regardless of point-of-service location or affiliation12,14,19:
Outpatient electronic medical record system, which contains all outpatient service activities. Health care providers document outpatient encounter details and close encounters by determining visit codes and billing level.
Inpatient electronic medical record system, which is used to document all inpatient service activities. Professional coders record the diagnosis and procedure codes based on notes and discharge summaries.
Medical benefit claims data, which contain services and medications from health care providers outside the military facilities.
Laboratory data, which include all laboratory results performed within the military system.
Pharmacy Data Transaction Service, which includes the medication issue date, strength, dosage form, and days of supply for all medications dispensed at or outside of military facilities. Although it is possible that medications may be purchased outside of Tricare, this is unlikely since those costs would be unnecessarily out-of-pocket expenses for Tricare beneficiaries.
Patient selection and inclusion and exclusion criteria were published in detail previously.20 All study subjects were enrolled in the system throughout the study. The study duration was divided into 2 periods: (1) the baseline period was used to identify patients’ baseline characteristics (October 1, 2003, to September 30, 2005) and (2) the follow-up period was used to identify the occurrence of outcome events (October 1, 2005, to March 1, 2010).
We identified 2 groups of patients: (1) statin users were patients who received and filled a statin medication prescription for at least 90 days in the period from October 1, 2004, to September 30, 2005 (fiscal year 2005). Patients who received statins for fewer than 90 days were excluded from the study. And (2) nonusers were patients who did not receive a statin at any time throughout the study from October 1, 2003, to March 1, 2010.
The study included all patients who met the following criteria: (1) were aged 30 to 85 years old, (2) were enrolled in Tricare Prime or Plus in the San Antonio Multi-Market Area, and (3) had at least 1 outpatient visit during the baseline period and 1 outpatient visit during the follow-up period.
Exclusion criteria included body trauma and burn patients (based on International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes): codes for burn patients were those identified by the Agency for Healthcare Research and Quality–Clinical Classifications Software,21 category 240; trauma codes were compiled from the ICD-9 manual and previous publications.22,23 Also excluded were patients who newly started treatment with statins after September 30, 2005 (end of baseline; the purpose of this exclusion was to allow for creating 2 treatment groups with equal periods of follow-up) and patients who did not receive at least 1 prescription medication during the study baseline.
An event was defined as the occurrence of an ICD-9-CM code during follow-up in either the inpatient or outpatient setting consistent with a cataract diagnosis. Cataracts were defined using ICD-9-CM codes for cataracts as identified by Agency for Healthcare Research and Quality–Clinical Classifications Software, category 86 (eTable in Supplement).21 We also used the following prespecified diagnosis subgroups for cataracts: (1) the cataract 1 subgroup included presenile, senile, and traumatic cataracts (eTable in Supplement) and (2) the cataract 2 subgroup included cataracts secondary to ocular disorders, systemic diseases, diabetes mellitus, radiation, and others (eTable in Supplement).
Details of all patients who met the study criteria were previously published.20,24 We described patients’ comorbidities using the Deyo et al25 adaptation method of the Charlson Comorbidity Index (CCI). A propensity score–matched cohort of statin users and nonusers was created using 44 variables that were expected to increase the likelihood for receiving statins (eg, diabetes mellitus, ischemic heart disease, cerebrovascular diseases, peripheral vascular diseases, and smoking26) and increase the risk for cataract (eg, excessive alcohol use, obesity, smoking, glaucoma, severe refractory disorders, and corticosteroid administration27). The variables used to create the propensity score were age, sex, 17 comorbid conditions as defined by the Deyo method (Table 1), total CCI score,25 obesity, alcohol dependence/abuse, illicit drug use, cigarette smoking, glaucoma (eTable in Supplement), vision defects/blindness (eTable in Supplement), health care use (number of outpatient visits and inpatient admissions during baseline and follow-up), and the use of 14 medication groups (Table 1).28
We used logistic regression analysis to create the propensity score and test the balance of covariates in our models using the routines developed by Becker and Ichino.29 We then used the routine developed by Leuven and Sianesi30 to perform nearest-number matching with a caliper of 0.001.
In this analysis, we estimated the risk for cataract in relation to statin use in the propensity score–matched cohort.
In the secondary analysis, we created a prespecified subgroup of patients in which patients with any CCI comorbidity according to the Deyo et al25 method were excluded (patients with no Charlson comorbidities). Hence, all statin users and nonusers in secondary analysis had a CCI score of 0.
We then used logistic regression analysis to examine the risk for outcome. Covariates included in each secondary analysis were statin use, age, sex, obesity, smoking, alcohol use, illicit drug use, glaucoma, vision defects/blindness, number of all admissions, number of all outpatients visits, and use of different classes of medications as listed in Table 1 in the baseline period.
In this analysis of patients with no Charlson comorbidities, we restricted statin users’ entry in our logistic regression model to patients who used statins for at least 2, 4, or 6 years successively. For each outcome measure, we included the same covariates used in the secondary analysis.
Baseline characteristics of statin users and nonusers were compared using χ2 test for categorical variables and t test for continuous variables. Comparisons were considered statistically significant if the calculated P value was less than an alpha level of 0.05.
Statistical analyses were performed using Stata version 12 (StataCorp) and SPSS statistical software version 19 (IBM Corp).
After excluding 516 patients who received statins for fewer than 90 days, 60 891 patients were identified; 14 642 were excluded (2124 were trauma patients; 10 476 received statins after September 30, 2005; and 2042 did not receive a medication during baseline). There were 13 626 statin users and 32 623 nonusers.
During the study, 73.5% of statin prescriptions were for simvastatin, 17.4% for atorvastatin, 7% for pravastatin, 1.7% for rosuvastatin, and 0.24% for fluvastatin or lovastatin. Approximately 33.7% of statin users received maximal statin doses defined as 80 mg of simvastatin, 80 mg of pravastatin, 80 mg of atorvastatin, and 40 mg of rosuvastatin. Because patients used different doses and different types of statins throughout the study, it was not possible to categorize patients based on statin type or dose. However, we calculated the cumulative simvastatin years, which consisted of the cumulative product of years of statin use and simvastatin-equivalent doses based on statins’ relative potency in lowering low-density lipoprotein (LDL) cholesterol, as previously reported.28,31
Among patients who satisfied the study selection criteria, statin users were older, more likely to be male, more likely to be obese, used tobacco more frequently, had higher CCI total score, used more medications, and used health care more frequently. For the primary analyses, we created a propensity score–matched cohort of 6972 pairs of statin users and nonusers. There were no significant differences in baseline characteristics between statin users and nonusers after matching (Table 1). The mean (standard deviation [SD]) of cumulative duration of statin use among statin users was 1593 (696) days. The mean (SD) of cumulative simvastatin years was 141.8 (133) mg years.
In the propensity score–matched cohort, statin use was associated with higher risk for cataract (odds ratio [OR], 1.09; 95% CI, 1.02-1.17) (Table 2). To examine the interaction of cataract risk with duration and dose of statins, we repeated the analysis, sequentially substituting statin use with each of those parameters. Cumulative simvastatin years was significantly related to increased cataract risk (OR, 1.001; P < .001), but not to the maximum dose of statin used in simvastatin-equivalent doses (OR, 0.99; P = .33).
For our secondary analysis, the cohort of patients with no Charlson comorbidity included 33 513 patients (6113 statin users and 27 400 nonusers). Table 3 describes patient baseline characteristics in this cohort.
After adjusting for the identified confounders, statin use was associated with higher adjusted OR for any cataract and cataract 1 (presenile, senile, and traumatic cataract), but not for cataract 2 (cataract secondary to ocular disorders, systemic diseases, diabetes mellitus, radiation, etc) (Table 4). We repeated the logistic regression analysis using a forward stepwise technique to maximize the value of R2. The final model identified statin use as an independent predictor of cataract (adjusted OR, 1.43; 95% CI, 1.33-1.53; Nagelkerke R2 = 0.52). We also repeated the analysis using backward stepwise elimination; statin use continued to be an independent predictor of cataract (adjusted OR, 1.42; 95% CI, 1.32-1.52; Nagelkerke R2 = 0.52). Cumulative simvastatin years was significantly related to increased cataract risk (adjusted OR, 1.001; P = .03), but not to the maximum dose of statin used in simvastatin-equivalent doses (adjusted OR, 0.99; P = .46).
We examined the relationship between cataract and both LDL cholesterol and high-density lipoprotein cholesterol by restricting the cohort to statin nonusers and introducing both parameters into our regression model. Mean LDL cholesterol level was inversely related to risk for cataract (adjusted OR, 0.997; P = .009); mean high-density lipoprotein cholesterol was not (adjusted OR, 1.002; P = .16). Introducing mean LDL cholesterol in a logistic regression model of patients with no Charlson comorbidities continued to demonstrate that statin use was independently associated with an increased adjusted OR for cataract (Table 4). Sensitivity analysis demonstrated consistent results in all subgroups of statin users for 2, 4, or 6 years (Table 4).
In this study, statin use was associated with a higher incidence of cataract diagnosis in the propensity score–matched cohort and in all the secondary and sensitivity analyses.
Cataract development may be induced by oxidative stress27; statins’ bidirectional effects on oxidation processes, including a possible mitochondrial effect, can potentially increase risk for cataract.32 Previous studies hypothesized that the inhibition of cholesterol biosynthesis by statin medications prevents proper epithelial cell development within the crystalline lens, which requires high cholesterol to maintain its transparency.8 Increased rates of cataract among animals and humans with hereditary cholesterol deficiency have been noted.33 Administration of atorvastatin was noted to induce cataract in the lens structure of Wistar rats.34
Several observational studies investigating the association of statin therapy with cataracts have produced conflicting results, with some reporting a protective effect,6,9,35,36 a negative effect,37 no effect,38 and an inconsistent effect.9
Recently, several studies found that statin use was associated with increased risk for cataract. In a prospective cohort study, the outcomes of 225 922 new statin users were compared with 1 778 770 nonusers. The adjusted hazard ratio (HR) for cataract in statin users compared with nonusers was 1.32 (95% CI, 1.26-1.37) in men. This adverse effect was similar across various types of statins.7 In another study that included 19 622 patients with cataract, statin users had a higher risk for cataract (HR, 1.27 for females and 1.24 for males; P < .001).39 In another retrospective study of 6336 patients, statin use was associated with nuclear sclerosis and posterior subcapsular cataract.37
Conversely, in a prospective, observational study including 1299 persons, the OR of nuclear cataract was lower in statin users (0.40; 95% CI, 0.18-0.90), after excluding smokers and diabetics and adjusting for potential confounders.36 In another population-based cohort study including 3654 elderly participants, statin use was protective for any cataract (adjusted HR, 0.52; 95% CI, 0.29-0.93).35
Furthermore, several studies have found no effect or inconsistent effect of statins on the cataract development. In a case-control analysis involving 13 982 patients who underwent cataract extraction and 34 049 control subjects, long-term statin use (>5 years) was protective against cataract surgery, but short-term statin use (<5 years) was associated with an increased rate of cataract extraction.9 In another case-control analysis, 7405 cases and 28 327 control subjects were matched by age, sex, practice type, calendar time, and duration of medical history in the database.40 Long-term use of statins was not associated with an increased cataract risk (adjusted OR, 0.9; 95% CI, 0.5-1.6), but concomitant use of simvastatin and erythromycin was associated with an increased risk for cataract.40 Moreover, a controlled, double-blind study randomized 621 individuals to receive simvastatin or matching placebo. After 18 months, there were no significant differences between the treatment groups in the refractive condition of the eye or in the incidence of cataract.41
An important consideration in observational studies is the presence of baseline confounders that may mask an actual relationship or falsely demonstrate the presence of relationship. Adherence to statins may be a marker for a healthy user bias that may result in false association of statin use with better outcomes. In a large prospective cohort study, statin-adherent patients had a lower adjusted risk ratio for motor vehicle and workplace accidents in comparison to nonadherent patients.42 Several risk factors for cardiovascular disease (eg, older age, diabetes mellitus, and smoking), which constitute indications for statin therapy, are also risk factors for development of cataract. Hence, adequate description of baseline characteristics and adjustment for these potential confounders is necessary.
To our knowledge, ours is the first study to use a propensity score–matched analysis to adjust for baseline confounders in statin users and nonusers. The propensity score–matched cohorts were equally balanced between the 2 treatment groups. This study also is one of the largest studies in the literature, comprising more than 45 000 patients followed up longitudinally within the same health care system. Additionally, all patients in this study received health care in a relatively homogenous health care system (military health care), with similar insurance coverage (Tricare Prime or Plus) and similar access to care and medication coverage. This consistency partially minimizes bias resulting from differences because of health care accessibility and use.
Limitations of this study included its retrospective observational design, such as the presence of unidentified confounders and the difficulty in adjusting for these confounders. Propensity score matching offers a strong tool to adjust for confounders, and we were successful in creating balanced cohorts. However, the presence of yet unidentified baseline confounders cannot be absolutely ascertained. Measurement inadequacies for covariates, residual confounding, omitted variable bias, and potential for interaction effects are all factors that could continue to confound results. Moreover, successful propensity score matching of individual baseline characteristics does not guarantee that the combined effect of individual differences has no impact on the outcome of interest. Conducting a retrospective administrative database search (ICD-9-CM code based) for diagnosis of cataract does not provide information on the visual significance of the cataract. Detection of cataract by slitlamp examination is quite sensitive, and the increased diagnoses of cataract among statin users may be owing to ascertainment bias. However, to mitigate this risk, we included the number of medical encounters in both inpatient and outpatient settings as one of the potential confounders. Using pharmacy data to identify statin use is another limitation because it captures statin prescribing and not necessarily statin intake. However, most statin users received statin prescriptions for prolonged periods (mean cumulative use of 1695 days), which suggests actual compliance with their statin prescription.
In conclusion, this study found statin use to be associated with an increased risk for cataract. Efforts to curtail preventable causes of cataracts entail further studies, including prospective observational studies/registries or randomized clinical trials, to confirm or refute these findings. Such studies should include regular ophthalmologic examinations and objective assessment tools rather than relying on patient surveys or administrative data. Weighing the benefit-risk ratio of statin use, specifically for primary prevention, should be carefully considered.
Corresponding Author: Ishak Mansi, MD, VA North Texas Health System, University of Texas Southwestern, 4500 S Lancaster Rd, Dallas, TX 75216 (firstname.lastname@example.org).
Submitted for Publication: January 3, 2013; final revision received March 20, 2013; accepted March 22, 2013.
Published Online: September 19, 2013. doi:10.1001/jamaophthalmol.2013.4575.
Author Contributions: Dr I. Mansi had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Mortensen, Frei, E. A. Mansi, Panday, I. Mansi.
Acquisition of data: I. Mansi.
Analysis and interpretation of data: Leuschen, Mortensen, Frei, E. A. Mansi, I. Mansi.
Drafting of the manuscript: Leuschen, Panday, I. Mansi.
Critical revision of the manuscript for important intellectual content: Mortensen, Frei, E. A. Mansi, Panday, I. Mansi.
Statistical analysis: Leuschen, Mortensen, Frei, I. Mansi.
Administrative, technical, or material support: Mortensen, Frei, E. A. Mansi, Panday, I. Mansi.
Study supervision: Mortensen, Panday, I. Mansi.
Conflict of Interest Disclosures: Dr Frei has received research grants and/or served as a scientific consultant/advisor for AstraZeneca, Bristol-Myers Squibb, Elan, Forest, Ortho-McNeil Janssen, and Pfizer. No other disclosures were reported.
Funding/Support: Dr Frei’s work is supported by the National Institutes of Health (NIH) in the form of a NIH/KL2 career development award (RR025766).
Role of the Sponsor: The sponsor had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Disclaimer: The views expressed herein are those of the authors and do not reflect the official policy or position of the Department of the Army, Department of Defense, Department of Veterans Affairs, or the US government. The authors are employees of the US government. This work was prepared as part of their official duties and, as such, there is no copyright to be transferred.
Correction: This article was corrected online October 9, 2013, for inaccurate information in reference 13.