Association between statin use and open-angle glaucoma: A nested case–control study using the Japanese claims database

doi:10.21203/rs.3.rs-2227992/v1

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Association between statin use and open-angle glaucoma: A nested case–control study using the Japanese claims database

https://doi.org/10.21203/rs.3.rs-2227992/v1

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Background: The association between statins and open-angle glaucoma (OAG) remains controversial.

Aim: This study investigated the relationship between statins and OAG in Japanese patients with dyslipidemia using the Japanese administrative claims database.

Methods: A nested case–control study using two models was conducted using the JMDC claims database (01/2005–01/2020). The onset of OAG: index date was defined as the diagnosis of glaucoma, prescription of anti-glaucoma drugs, or surgery of glaucoma. For each case, 10 age-, sex-, and calendar year/month-matched controls were randomly selected from the risk sets. The number of statin prescriptions during the exposure period, which was identified as the12-month (model 1) or 24-month (model 2) periods prior to the index date, was used as an indicator for statin exposure. Adjusted odds ratios (aORs) and 95% confidence interval (CI) were estimated using multivariable conditional logistic regression analyses.

Results: We identified 378,494 patients with newly diagnosed dyslipidemia. Of them, 6,281 cases and 62,804 controls (model 1) and 4,229 cases and 42,284 controls (model 2) were selected. Significant risk factors commonly identified in the two models were diabetes mellitus, myopia, and steroid use. Statin use was not identified as a significant risk factor for OAG (model 1: aOR 0.99, 95% CI 0.94–1.05, model 2: aOR 0.96, 95% CI 0.90–1.03).

Conclusion: Compared with non-exposure, short-term exposure (<2 years) to statins was not related to increased risk of OAG in Japanese working-age population with dyslipidemia.

Statin

Open-angle glaucoma

Risk factor

Claims database

Nested case–control study

Short-term exposure to statins was not significantly associated with the development of open-angle glaucoma in Japanese working-age population.
There was no significant difference in the risk of developing open-angle glaucoma between different statin types.
Since glaucoma is an insidious disease, further study about longer-term exposure to statins are warranted.

Glaucoma is a group of eye conditions that narrow the visual field due to optic neuropathy, and can cause visual impairment and irreversible blindness. The prevalence of glaucoma among Caucasians and Asian Americans is 2.8% and 3.5%, respectively [1]. When it comes to the Japanese population, the Tajimi study showed that the estimated prevalence of glaucoma in Japanese people older than 40 years was 3.9% [2], a percentage that should certainly not be overlooked. Once diagnosed with an optic nerve or visual field disorder, the patients’ quality of life is significantly impacted. Therefore, early detection of glaucoma is very essential in suppressing disease progression and optimizing the subsequent therapeutic treatment. However, early detection of this disease requires a better understanding of the risk factors that are associated with the development of glaucoma.

Primary glaucoma can be divided into open-angle glaucoma (OAG), which is the most common form of glaucoma, and angle-closure glaucoma. Older age, elevated intraocular pressure, and high myopia have been known as risk factors in the pathogenesis of primary OAG [3]; and these risk factors can be applicable to the Japanese people as well [4]. Recently, a population-based case–control study using a Taiwanese database of patients showed that a high dosage of HMG-CoA reductase inhibitors (statins) resulted in a 1.26-fold increased risk of OAG [5]. However, current studies provide conflicting evidence with respect to the possible association between statins and OAG [6, 7]. For instance, some studies have shown that statin use is not associated with an increased risk of OAG, compared with no use of statins [8, 9]. Furthermore, a recent study has reported that the risk of OAG does not vary with the type of statins in the Japanese working-age population [10]. Therefore, the true relationship between statins and OAG remains unknown.

Aim

In this study, we investigated the relationship between statins and OAG in Japanese working-age patients with dyslipidemia using a database from the Japan Medical Data Center (JMDC) administrative claims.

Ethics approval

This study was approved by the Ethics Committees of the Kindai University School of Pharmacy on April 14, 2018 (approval number, 18–128). The requirement for informed consent was waived due to the anonymous nature of the data.

Data source

The JMDC administrative claims database is a large and chronologically organized database (JMDC Inc., Tokyo, Japan) that uses standardized disease classification and anonymous record linkage [11]. This database includes information from employment-based health insurance claims regarding employees and their dependents who are less than 75 years old. In total, the data of approximately 14 million insured persons in Japan were registered in this database between January 2005 and November 2021. The registered data for each patient included the following: age, sex, dates of hospital or clinic visits, International Statistical Classification of Diseases and Related Health Problems-Tenth Revision (ICD-10) codes, and prescription of drugs categorized according to the Anatomical Therapeutic Chemical (ATC) classification of both the European Pharmaceutical Market Research Association and the World Health Organization. An encrypted personal identifier was used to link the claims data from various hospitals, clinics, and pharmacies.

Study design

We conducted a nested case–control study using the JMDC claims database (January 2005 to January 2020). Figure 1 shows the study design and matching. Our study used two model cohorts to evaluate the robustness of the results. First, we identified newly diagnosed patients with dyslipidemia (ICD-10 code: E78) who had already been included in the JMDC database for at least six months before the first diagnosis. Furthermore, we selected patients who had been diagnosed once or more within six months after the first diagnosis of dyslipidemia. We excluded patients under 20 years of age, patients whose onset of OAG (outcome) was before the first diagnosis of dyslipidemia, and patients who did not have a minimum of 12 months (model 1) or 24 months (model 2) of participation after the first diagnosis of dyslipidemia. Observation periods were defined as periods from the first diagnosis of dyslipidemia to onset of the treatment effect (index date) or the final diagnosis of dyslipidemia, whichever came first. Within these cohorts, we identified all cases with outcomes during the observation periods. Consequently, we constructed a risk set of possible controls for each case with at least an equal duration of observation periods at the case’s index date. Cases with outcomes were randomly matched to maximum 10 controls according to age (± 5 years), gender, and calendar year/month (± 3 months) of the date of dyslipidemia diagnosis through risk set sampling. The same index date was applied to the ten comparator controls.

Outcome

The onset of OAG in patients with dyslipidemia was defined as the outcome of this study, and the index date of case was defined as the onset of OAG. To define the onset of OAG, we used a composite outcome measure. In models 1 and 2, OAG was identified based on the diagnosis code of glaucoma (ICD-10: H401, H406, and H409), prescribed anti-glaucoma drugs (ATC code: S01E2), or a Japanese medical procedure code of glaucoma surgery (K268).

Exposure

Six HMG-CoA reductase inhibitors, including rosuvastatin, atorvastatin, pitavastatin, pravastatin, simvastatin, and fluvastatin were defined as statins in our study. We investigated exposure to statins during a 12- or 24-month period prior to the index date in models 1 and 2, respectively (Fig. 1). The number of statin prescriptions during the exposure period was totaled, and the number of the first (Q1) and second (Q2) quartiles was calculated by descriptive statistics. Patients who were prescribed for Q1 or more times (Q1 exposure) or Q2 or more times (Q2 exposure) were considered to have exposure to statins. A total of four patterns were analyzed for Q1 and Q2 exposures in the models 1 and 2.

Covariates

We examined age, sex, and comorbidities at the first diagnosis of dyslipidemia. Diseases with an ICD-10 code of two or more times within six months prior to the first diagnosis of dyslipidemia were defined as comorbidities. Identified comorbidities that could influence the outcomes of this study included diabetes mellitus (defined as ICD-10 code E11 and E14), hypertension (I10), hypotension (I95), hyperuricemia/gout (E790 and M10), arrhythmias/heart failure (I48, I49, and I50), ischemic heart disease (I20-I25), cerebrovascular disease (I60-I69), transient ischemic attack (G45), migraines (G439), obstructive sleep apnea (G473), and myopia (H521). Infectious or inflammatory conditions, including Behçet’s disease (M352), congenital rubella (P350), cytomegalovirus retinitis (B258 and B259), herpetic disease (B00, B01, and B02), human leukocyte antigen B27-related disease (M02, M45, and L405), juvenile idiopathic arthritis (M080), Lyme disease (A692), rheumatoid arthritis (M05 and M06), sarcoidosis (D86), syphilis (A50-A53), systemic lupus erythematosus (M32), toxocariasis (B830) or toxoplasmosis (B58), systemic beta-blocker use (defined as ATC code C07), and steroid use (defined as ATC code H02, S01B, S03B, and S03C), were also identified as potential confounders with reference to previous studies [1, 12].

Data analysis

Categorical variables were reported as number and percentage. Continuous variables were reported as median and interquartile range (IQR). Categorical or continuous variables were compared between cases and controls using Chi-square or Mann–Whitney tests, respectively. A multivariable conditional logistic regression analysis was conducted. The estimates of odds ratios were adjusted for all variables. Data management was performed using the Visual Mining Studio software (version 9.0; NTT DATA Mathematical Systems Inc., Tokyo, Japan) and SAS studio (version 3.8, Enterprise Edition; SAS Institute, Cary, NC). All statistical tests were two-sided, and a value of p < 0.05 was considered statistically significant using IBM SPSS 28 Statistics (IBM Inc., Armonk, NY).

Patient characteristics

Figure 2 illustrates the flow diagram of patient selection. We identified 1,276,513 patients who had a diagnosis code of dyslipidemia in the JMDC claims database. Patients were excluded if they had < 6 months run-in period (n = 632,593), no new diagnosis of dyslipidemia once or more within six months after the first diagnosis (n = 238,727), outcomes before the first diagnosis of dyslipidemia (n = 19,599), were younger than 20 years old (n = 7,100), and had an observation period of less than 12 months after the first diagnosis of dyslipidemia (n = 168,294). After applying these criteria, 6,281 cases and 62,804 controls were identified in model 1. Similarly, 4,229 cases and 42,284 controls were identified in model 2.

Table 1 shows the demographic and clinical characteristics of the cases and controls included in this study. Male patients predominated (approximately 60%), and the median age was 52 years. The median [IQR] observational period after the first diagnosis of dyslipidemia in models 1 or 2 were 33 [25–53] or 51 [45–56] months, respectively. Approximately half of the patients diagnosed with dyslipidemia were prescribed statins at least once. In model 1, the Q1 and Q2 of the number of statin prescriptions were 5 and 7, suggesting more than 75% and 50% patients who received statins within a 12-month period prior to the index date and received Q1 exposure and Q2 exposure, respectively. In model 2, the Q1 and Q2 of the number of statin prescription was 9 and 13, suggesting more than 75% and 50% patients who received statins within 24 months prior to the index date and received Q1 exposure and Q2 exposure, respectively.

Risk factors for OAG

Tables 2 and 3 show the risks associated with OAG in models 1 and 2, respectively. In model 1, the multivariable conditional logistic regression analysis revealed that four variables, namely diabetes mellitus (adjusted odds ratio [aOR]: 1.27, 95% confidence interval [CI]: 1.17–1.39), transient ischemic attack (aOR: 2.06, 95% CI: 1.27–3.33), myopia (aOR: 2.11, 95% CI: 1.81–2.47), and steroid use (aOR: 1.30, 95% CI: 1.20–1.41) were significantly associated with OAG (Table 2). In model 2, the multivariable logistic regression analysis showed that three variables, namely diabetes mellitus (aOR: 1.30, 95% CI: 1.18–1.44), myopia (aOR: 2.07, 95% CI: 1.71–2.51), and steroid use (aOR: 1.32, 95% CI: 1.19–1.45), were significantly associated with OAG (Table 3). Statin use (Q1 exposure) was not identified as a significant risk factor in these two models (model 1: aOR 0.99, 95% CI 0.94–1.05, model 2: aOR 0.96, 95% CI 0.90–1.03). Furthermore, Q2 exposure was examined in the same way, and statin use (Q2 exposure) was not identified as a significant risk factor in these two models (model 1: aOR 1.04, 95% CI 0.98–1.10, model 2: aOR 1.02, 95% CI 0.95–1.09) (Supplementary Table S1 and S2). Table 4 shows that the association between Q1 or Q2 exposure of each statin and the onset of OAG. No statins were identified as risk factors for OAG onset. Significant risk factors commonly identified among these models were diabetes mellitus, myopia, and steroid use.

This nested case–control study used the Japanese administrative claims database to investigate the association between statin use and onset of OAG. Since studies using databases have several limitations, we constructed a total of four patterns in the two-model design to increase the robustness of our results. Consequently, our findings revealed that there was no significant association between short-term statin use (< 2 years) and onset of OAG in Japanese working-age population with dyslipidemia, a finding that was consistent across all models used in this study.

The National Health and Nutrition Survey in Japan (2018), reported by the Ministry of Health, Labour and Welfare, showed that the frequency of high low-density lipoprotein cholesterol levels decreased after peaking in the 40s for men and in the 50s for women [13]. The proportion of males in our study was approximately 60%, with a median age of 52 years, indicating that the JMDC database can reflect the portion of the Japanese population with dyslipidemia. Approximately half of the patients diagnosed with dyslipidemia were prescribed statins once or more in both cases and controls. The number of patients prescribed statins once or more times was higher in model 2 (≥ 24 months of observation) compared to model 1 (≥ 12 months of observation), suggesting that a long-term prevalence of dyslipidemia may lead to an increased number of statin users.

One of the advantages of our study is that it confirms the use of a nested case–control design for estimating the hazard ratio. Our results can be interpreted in that a 12- or 24-month period administration of statins to young patients with dyslipidemia does not make a significant difference in the hazard ratio of OAG compared to patients without administration of statins. A nested case–control study targeting elderly Australians also showed that statin use was not associated with glaucoma onset [14]. However, their study showed an increased risk of glaucoma onset in participants with a longer duration (> 3 years) of statin use compared with a shorter duration (< 1 year). No consistent conclusions have been extracted regarding the relationship between the duration of statin administration and the risk of OAG, although some reports have suggested that long-term or short-term statin use can reduce the respective risk [7, 15]. Another large-scale study with a long follow-up duration of five or more years also showed that longer statin use was not associated with risk of OAG [8]. The twenty-four months period of exposure to statins was considered in our study; however, longer-term observations may need to be considered. Racial differences may influence the relationship between statin use and OAG. A cross-sectional study using big data in the United Kingdom showed that no evidence of a protective association between statin use and glaucoma was found [16]. In a study of the Korean population, dyslipidemia treatment was not found to be significantly associated with OAG [17]. Regardless of the racial or ethnic composition, statins are presumably not involved in the onset of OAG. No statin was found to be significantly associated with the onset of OAG in our results for the individual statin investigation. For simvastatin, the odds ratios in all the analyses were below 1.0, although not statistically significant. Simvastatin has been reported for its possible role in visual field stabilization in glaucoma patients [18]. Consideration with a larger sample size of simvastatin is desired.

Furthermore, several reports have examined the relationship between serum cholesterol levels and glaucoma/intraocular pressure [19, 20]. Serum HDL3 cholesterol level, which is involved in the promotion of cholesterol efflux and has a protective effect on vascular endothelium, was reported to be associated with glaucoma [9]. These reports indicate that dyslipidemia and blood lipid level are associated with glaucoma and high intraocular pressure. Regardless of the use or nonuse of statins in dyslipidemia, poor control of dyslipidemia may increase the risk of glaucoma depending on serum cholesterol level.

Comorbidities, such as diabetes, myopia, and the use of steroids, were identified as significant risk factors for OAG in our models, a finding which is consistent with current literature. Diabetes mellitus has already been suggested to increase the risk of OAG [21, 22], because higher serum glucose levels are associated with high intraocular pressure, which is thought to result in optic nerve damage [23]. Myopia, especially high myopia, has been reported as a major risk factor for OAG [24, 25]. This is further supported by the Tajimi study conducted in Japan, which also showed an association between myopia and glaucoma [4]. Finally, steroid use has been long known to increase intraocular pressure, resulting in steroid-induced glaucoma [26–28]. Patients with these factors are at a particularly increased risk of developing OAG, thus regular examinations are extremely essential.

Nonetheless, our study has several limitations. First, since the JMDC administrative claims database is based on social insurance enrollees in the Japanese working-age population, most of the people registered is under 65 years of age, and thus data for patients over 75 years old are not included. This is especially important considering that the incidence of glaucoma increases with age [29]. Therefore, this study may be underestimating events of OAG in patients over 65 years old. Second, OAG is associated with ocular features, such as intraocular pressure and family history. However, this information was not included in the JMDC claims database. Serum cholesterol level was also not included. Therefore, these factors could not be considered in our analysis. Furthermore, our definition might not accurately identify primary open angle glaucoma. Glaucoma is likely heterogenous (open-angle glaucoma, exfoliative glaucoma, some secondary glaucoma and some ocular hypertension). Therefore, there would have been misclassification of the outcome in this study. Third, the number of prescriptions for statins was used as an indicator of exposure. However, factors such as adherence to statins, daily dose, or dose intensity were not taken into consideration. Furthermore, modifiable environmental factors, such as lifestyle, exercise, and nutrition [30], could not be investigated.

In conclusion, our analysis demonstrated that no significant association was observed between short-term statin usage (< 2 years) and the onset of OAG among the Japanese working-age population with dyslipidemia.

Supplementary Information

Supplemental material for this article is available online.

Acknowledgements

The authors would like to Enago (www.enago.jp) for the English language review.

Funding

This work was supported by JSPS KAKENHI (Grant no. JP19K16461).

Conflicts of interest

The authors declare no conflicts of interest.

ACKNOWLEDGMENTS

Funding/Support: This work was supported by JSPS KAKENHI (Grant no. JP19K16461).

Financial Disclosures: The authors indicate no financial support or conflicts of interest. All authors attest that they meet the current ICMJE criteria for authorship.

Authors’ contributions

All authors contributed to the study’s conception and design. Material preparation and data collection and analysis were performed by Satoshi Yokoyama and Chihiro Nakagawa. Analysis and interpretation of the data collected were performed by Satoshi Yokoyama, Chihiro Nakagawa, and Kouichi Hosomi. The first draft of the manuscript was written by Satoshi Yokoyama, and all authors commented on previous versions of the manuscript. All authors have read and approved the final version of the manuscript.

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Table 1. Patients demographic and clinical characteristics

Table 2. Odds ratio for open-angle glaucoma in model 1

Table 3. Odds ratio for open-angle glaucoma in model 2

Table 4. Odds ratio of each statin for open-angle glaucoma

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Association between statin use and open-angle glaucoma: A nested case–control study using the Japanese claims database

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Abstract

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Introduction

Aim

Ethics approval

Method

Data source

Study design

Outcome

Exposure

Covariates

Data analysis

Results

Patient characteristics

Risk factors for OAG

Discussion

Conclusion

Declarations

References

Tables

Supplementary Files

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