Abstract
Objective
To determine the prevalence and risk of systemic comorbidities in primary angle-closure glaucoma in Taiwan population.
Methods
We included 3322 primary angle-closure glaucoma (PACG) subjects and randomly selected patients without PACG from the Taiwan National Health Insurance Research Database and frequency matched four of them (n=13 288) to each PACG patient, based on age and sex. The univariable and multivariable unconditional logistic regression models were used to estimate the effect of comorbidities on the risk of PACG as indicated by the OR with 95% CI.
Results
The mean age of the PACG group was 65.2±12.7 years, and 61.1% of the patients were female. The risk of PACG was greater for patients with the comorbidities of hyperlipidaemia (ORs: 1.11), headaches (ORs: 1.13), liver diseases (ORs: 1.14), peptic ulcers (ORs: 1.10) and cataract (ORs: 3.80). For the male group, diabetes (ORs: 1.19), liver diseases (ORs: 1.29) and cataract (ORs: 4.30) were significantly associated with increasing PACG risk. For the female group, hyperlipidaemia (ORs: 1.13), headaches (ORs: 1.15), peptic ulcers (ORs: 1.14) and cataract (ORs: 3.54) were significantly associated with increasing PACG risk. For the age group of 64 years and younger, patients with comorbidity of hyperlipidaemia (ORs: 1.20), peptic ulcers (ORs: 1.21) and cataract (ORs: 5.91) were significantly associated with increasing PACG risk. For the age group of 65 years and older, patients with cataract were significantly associated with increasing PACG risk (ORs: 5.07).
Conclusions
Clinicians should be aware of slightly increased PACG risk in the subjects with the medical comorbidities of hyperlipidaemia, headaches, liver diseases and peptic ulcers. However, cataract is the strongest risk factor of PACG.
Keywords: primary angle-closure glaucoma, medical comorbidity, cataract, Taiwan
Strengths and limitations of this study.
This is the first original study on the association between medical comorbidity and primary angle-closure glaucoma (PACG).
A strength of this study is the large sample size.
Clinicians should be aware of slightly increased PACG risk in the subjects with hyperlipidaemia, headaches, liver diseases and peptic ulcers.
Cataract is the strongest risk factor of PACG in any age group and gender.
This study has inherent limitations from the claims database, including miscoding and selection bias; the findings are thus not generalisable to all populations.
Introduction
Primary angle-closure glaucoma (PACG) is a leading cause of blindness worldwide; it is especially common in Asian countries.1–3 A recent meta-analysis study shows that PACG affects approximately 0.75% of adult Asians, and this percentage doubles every decade; 60% of cases are in females.4 The proposed mechanism of PACG is pupillary block, with anterior lens movement as a strong contributing factor, often due to ageing-induced cataract formation.4 5 Risk factors for PACG are ageing, female gender, shallow anterior chamber and short axial length in hyperopic eye.4 5 Contrary to primary open angle glaucoma (POAG)—which has been associated with systemic diseases, including cardiovascular, metabolic, neurodegenerative, psychological diseases and others6–13 few studies have evaluated medical illness among PACG subjects. Age is the main factor contributing to the coexisting of systemic comorbidities and cataract formation. Therefore, it is quite meaningful to understand if age-related medical illness would be associated with PACG, which is also a very important issue in our population because of very high prevalence of this type of glaucoma in Taiwan.
Here, we use a nationwide dataset from Taiwan to determine the prevalence of some common medical comorbidities in the PACG population. We also study whether these comorbidities are associated with the increased risk of PACG compared with controls. This is the first original study using a large claims database to evaluate this important topic.
Materials and methods
Patient and public involvement statement
This work is a retrospective longitudinal case–control study from a claims database. Patients were not involved in the recruitment or conduct of the study.
Data source
We conducted a nationwide population-based retrospective cohort study using data from the Longitudinal Health Insurance Database 2000 (LHID 2000). The LHID 2000 contains the enrolment and claims information of 1 million randomly sampled enrollees of the National Health Insurance (NHI) programme in 2000. The NHI programme provides mandatory universal health insurance to Taiwan’s 23.75 million citizens and residents, with an enrolment rate of approximately 99%.14The LHID 2000 includes all ambulatory care, inpatient services, prescription drugs, traditional Chinese Medicine and dental services claims data. The study was approved by the Institutional Review Board of China Medical University and Hospital (CMUH-104-REC2-115). Diseases are coded according to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), 2001 edition.
Sampled participants
From the LHID 2000, we identified patients aged more than 20 years with a diagnosis of PACG (ICD-9-CM code 365.2) between January 1, 2005, and December 31, 2011 as the case group. The diagnosis of PACG was based on definitions agreed on by the World Glaucoma Association.15 The date of diagnosis of PACG was defined as the index date. We excluded patients with a history of POAG (ICD-9-CM code 365.1) diagnosed before the index date. Secondary, juvenile, and congenital glaucoma were also excluded. For each PACG case, four insured beneficiaries with no history of glaucoma (ICD-9-CM code 365) were assigned to a non-PACG control group, frequency matched to the patients in the PACG case group according to age (every 5 years), sex and index year of PACG diagnosis; the same exclusion criteria used for the PACG case group were applied.
Common medical comorbidity
The comorbidities were hypertension (ICD-9-CM codes 401–405), ischaemic heart disease (ICD-9-CM codes 410–414), hyperlipidaemia (ICD-9-CM code 272), congestive heart failure (ICD-9-CM code 428), cardiac arrhythmias (ICD-9-CM codes 426 and 427), peripheral vascular disorders (ICD-9-CM codes 440.2, 440.3, 440.8, 440.9, 443, 444.22, 444.8, 447.8 and 447.9), stroke (ICD-9-CM codes 430–438), headaches (ICD-9-CM code 784.0), migraine (ICD-9-CM code 346), epilepsy (ICD-9-CM code 345), dementia (ICD-9-CM code 290, 294.1 and 331.0), rheumatoid arthritis (ICD-9-CM code 714), systemic lupus erythematosus (ICD-9-CM code 710.0), chronic obstructive pulmonary disease (ICD-9-CM codes 491, 492 and 496), asthma (ICD-9-CM code 493), pulmonary circulation disorders (ICD-9-CM codes 415–417), diabetes (ICD-9-CM code 250), hypothyroidism (ICD-9-CM codes 243 and 244), renal failure (ICD-9-CM codes 584–586), liver diseases (ICD-9-CM codes 570–573), peptic ulcers (ICD-9-CM codes 531–533), hepatitis B (ICD-9-CM codes V02.61, 070.20, 070.22, 070.30 and 070.32), tuberculosis (ICD-9-CM codes 011–018), deficiency anaemias (ICD-9-CM codes 280, and 281), depression (ICD-9-CM codes 296.2, 296.3, 300.4, and 311), psychosis (ICD-9-CM codes 295–299), metastatic cancer (ICD-9-CM codes 196–198) and solid tumour (ICD-9-CM codes 140–195).
Cataract (ICD-9-CM code 366) was also evaluated because of higher prevalence in the elderly population.
Statistical analysis
The baseline characteristics and comorbidities of the PACG case group and non-PACG control group were compared. Χ2 test and t-test were used to evaluate the difference of categorical and continuous variables, respectively, between the two groups. Univariable and multivariable unconditional logistic regression models were used to estimate the effect of comorbidities on the risk of PACG as indicated by the OR with 95% CI. All analyses were performed using SAS V.9.4 (SAS Institute), and the significance level was set at 0.05 for the two-tailed tests.
Results
A total of 3322 PACG cases met the study criteria, and 13 288 subjects were matched according to sex and age to form the control group (table 1). The PACG group comprised 61.1% women, and 57.6% were older than 65 years. The mean age was 65.2±12.7 years in the PACG group and 64.8±13.0 years in the control group. Compared with the controls, PACG patients have significantly higher prevalence of hypertension, ischaemic heart disease, hyperlipidaemia, cardiac arrhythmias, peripheral vascular disorders, headaches, chronic obstructive pulmonary disease, asthma, diabetes, renal failure, liver diseases, peptic ulcers, hepatitis B, depression, solid tumour and cataract (p<0.05).
Table 1.
Demographic comparison between primary angle-closure glaucoma (PACG) cases and controls
| PACG cases n=3322 |
Controls n=13 288 |
P value | |||
| n | (%) | n | (%) | ||
| Sex | 0.999 | ||||
| Female | 2031 | 61.1 | 8124 | 61.1 | |
| Male | 5164 | 38.9 | 1291 | 38.9 | |
| Age group (years) | 0.999 | ||||
| 20–49 | 398 | 12.0 | 1592 | 12.0 | |
| 50–64 | 1011 | 30.4 | 4044 | 30.4 | |
| ≥65 | 1913 | 57.6 | 7652 | 57.6 | |
| Age (year), mean (SD)* | 65.2 (12.7) | 64.8 (13.0) | 0.100 | ||
| Comorbidity | |||||
| Hypertension | 2025 | 60.6 | 6896 | 51.9 | <0.001 |
| Ischaemic heart disease | 1097 | 33.0 | 3561 | 26.8 | <0.001 |
| Hyperlipidaemia | 1389 | 41.8 | 4399 | 33.1 | <0.001 |
| Congestive heart failure | 213 | 6.41 | 849 | 6.39 | 0.962 |
| Cardiac arrhythmias | 540 | 16.3 | 1826 | 13.7 | <0.001 |
| Peripheral vascular disorders | 201 | 6.05 | 571 | 4.30 | <0.001 |
| Stroke | 246 | 7.41 | 994 | 7.48 | 0.883 |
| Headaches | 1407 | 42.4 | 4772 | 35.9 | <0.001 |
| Migraine | 125 | 3.76 | 456 | 3.43 | 0.353 |
| Epilepsy | 30 | 0.90 | 144 | 1.08 | 0.360 |
| Dementia | 110 | 3.31 | 448 | 3.37 | 0.863 |
| Rheumatoid arthritis | 11 | 0.33 | 45 | 0.34 | 0.957 |
| Systemic lupus erythematosus | 3 | 0.09 | 8 | 0.06 | 0.546 |
| Chronic obstructive pulmonary disease | 675 | 20.3 | 2343 | 17.6 | <0.001 |
| Asthma | 418 | 12.6 | 1455 | 11.0 | 0.008 |
| Pulmonary circulation disorders | 26 | 0.78 | 85 | 0.64 | 0.366 |
| Diabetes | 710 | 21.4 | 2148 | 16.2 | <0.001 |
| Hypothyroidism | 36 | 1.08 | 110 | 0.83 | 0.158 |
| Renal failure | 448 | 13.5 | 1435 | 10.8 | <0.001 |
| Liver diseases | 898 | 27.0 | 2775 | 20.9 | <0.001 |
| Peptic ulcers | 1409 | 42.4 | 4503 | 33.9 | <0.001 |
| Hepatitis B | 182 | 5.48 | 610 | 4.59 | 0.032 |
| Tuberculosis | 86 | 2.59 | 294 | 2.21 | 0.194 |
| Deficiency anaemia | 114 | 3.43 | 381 | 2.87 | 0.087 |
| Depression | 328 | 9.87 | 922 | 6.94 | <0.001 |
| Psychosis | 153 | 4.61 | 518 | 3.90 | 0.064 |
| Metastatic cancer | 1 | 0.03 | 2 | 0.02 | 0.564 |
| Solid tumour | 190 | 5.72 | 630 | 4.74 | 0.020 |
| Cataract | 2088 | 62.9 | 4077 | 30.7 | <0.001 |
Data are presented as the number of subjects in each group, with percentages given in parentheses.
χ2 test; *t-test.
The crude and adjusted ORs for the model were fitted to examine the association between medical comorbidities and the risk of PACG (table 2). Hyperlipidaemia increased the risk of PACG by 1.11-fold (95% CI: 1.01 to 1.21). Headaches increased the risk of PACG by 1.13-fold (95% CI: 1.04 to 1.23). Liver diseases inrcreased the risk of PACG by 1.14-fold (95% CI: 1.03 to 1.25). Peptic ulcers increased the risk of PACG by 1.10- fold (95% CI: 1.01 to 1.20). Cataract increased the risk of PACG by 3.80-fold (95% CI: 3.49 to 4.14).
Table 2.
Factors associated with risk of primary angle-closure glaucoma
| Variable | Crude | Adjusted† |
| OR (95% CI) | OR (95% CI) | |
| Comorbidity | ||
| Hypertension | 1.45 (1.34 to 1.56)*** | 0.97 (0.88 to 1.07) |
| Ischaemic heart disease | 1.35 (1.24 to 1.46)*** | 0.92 (0.83 to 1.01) |
| Hyperlipidaemia | 1.45 (1.34 to 1.57)*** | 1.11 (1.01 to 1.21)* |
| Congestive heart failure | 1.00 (0.86 to 1.17) | – |
| Cardiac arrhythmias | 1.22 (1.10 to 1.35)*** | 0.91 (0.81 to 1.02) |
| Peripheral vascular disorders | 1.44 (1.22 to 1.69)*** | 1.02 (0.86 to 1.21) |
| Stroke | 0.98 (0.86 to 1.14) | – |
| Headaches | 1.31 (1.21 to 1.42)*** | 1.13 (1.04 to 1.23)*** |
| Migraine | 1.10 (0.90 to 1.35) | – |
| Epilepsy | 0.83 (0.56 to 1.24) | – |
| Dementia | 0.98 (0.79 to 1.21) | – |
| Rheumatoid arthritis | 0.98 (0.51 to 1.89) | – |
| Systemic lupus erythematosus | 1.50 (0.40 to 5.66) | – |
| Chronic obstructive pulmonary disease | 1.19 (1.08 to 1.31)*** | 0.88 (0.79 to 1.00) |
| Asthma | 1.17 (1.04 to 1.32)*** | 0.98 (0.86 to 1.11) |
| Pulmonary circulation disorders | 1.23 (0.79 to 1.90) | – |
| Diabetes | 1.41 (1.28 to 1.55)*** | 1.03 (0.93 to 1.15) |
| Hypothyroidism | 1.31 (0.90 to 1.92) | – |
| Renal failure | 1.29 (1.15 to 1.44)*** | 0.93 (0.82 to 1.05) |
| Liver diseases | 1.40 (1.29 to 1.53)*** | 1.14 (1.03 to 1.25)* |
| Peptic ulcers | 1.44 (1.33 to 1.55)*** | 1.10 (1.01 to 1.20)* |
| Hepatitis B | 1.21 (1.02 to 1.43)* | 1.09 (0.91 to 1.31) |
| Tuberculosis | 1.18 (0.92 to 1.50) | – |
| Deficiency anaemia | 1.20 (0.97 to 1.49) | – |
| Depression | 1.47 (1.29 to 1.68)*** | 1.12 (0.98 to 1.29) |
| Psychosis | 1.19 (0.99 to 1.43) | – |
| Metastatic cancer | 2.01 (0.18 to 22.1) | – |
| Solid tumour | 1.22 (1.03 to 1.44)* | 1.01 (0.85 to 1.20) |
| Cataract | 3.82 (3.53 to 4.14)*** | 3.80 (3.49 to 4.14)*** |
*p<0.05; **p<0.01; ***p<0.001.
†Covariables which were significantly associated with risk of PACG in univariable unconditional logistic regression model were further analysed by multivariable unconditional logistic regression model.
For the male group, diabetes (ORs: 1.19, 95% CI: 1.00 to 1.40), liver diseases (ORs: 1.29, 95% CI: 1.11 to 1.50), and cataract (ORs: 4.30, 95% CI: 3.74 to 4.94) were significantly associated with increasing PACG risk (table 3). For the female group, hyperlipidaemia (ORs: 1.13, 95% CI: 1.00 to 1.26), headaches (ORs: 1.15, 95% CI: 1.04 to 1.28), peptic ulcers (ORs: 1.14, 95% CI: 1.02 to 1.28) and cataract (ORs: 3.54, 95% CI: 3.18 to 3.95) were significantly associated with increasing PACG risk.
Table 3.
Factors affecting the risk of primary angle-closure glaucoma according to sex
| Variable | Male | Female | ||
| Crude | Adjusted† | Crude | Adjusted† | |
| OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
|---|---|---|---|---|
| Comorbidity | ||||
| Hypertension | 1.60 (1.41 to 1.81)*** | 1.01 (0.87 to 1.18) | 1.36 (1.23 to 1.50)*** | 0.94 (0.83 to 1.06) |
| Ischaemic heart disease | 1.43 (1.25 to 1.63)*** | 0.92 (0.78 to 1.08) | 1.30 (1.17 to 1.44)*** | 0.90 (0.79 to 1.02) |
| Hyperlipidaemia | 1.54 (1.35 to 1.75)*** | 1.12 (0.96 to 1.30) | 1.41 (1.28 to 1.56)*** | 1.13 (1.00, 1.26)* |
| Congestive heart failure | 1.15 (0.91 to 1.46) | – | 0.91 (0.74 to 1.12) | – |
| Cardiac arrhythmias | 1.24 (1.05 to 1.48)*** | 0.86 (0.71 to 1.05) | 1.20 (1.06 to 1.37)*** | 0.93 (0.80 to 1.07) |
| Peripheral vascular disorders | 1.52 (1.17 to 1.98)*** | 0.97 (0.73 to 1.29) | 1.38 (1.12 to 1.71)*** | 1.04 (0.84 to 1.31) |
| Stroke | 1.10 (0.89 to 1.36) | – | 0.90 (0.74 to 1.10) | – |
| Headaches | 1.35 (1.19 to 1.55)*** | 1.15 (1.00 to 1.33) | 1.30 (1.18 to 1.44)*** | 1.15 (1.04, 1.28)** |
| Migraine | 1.09 (0.70 to 1.70) | – | 1.10 (0.88 to 1.39) | – |
| Epilepsy | 0.91 (0.50 to 1.67) | – | 0.78 (0.46 to 1.31) | – |
| Dementia | 1.04 (0.74 to 1.45) | – | 0.95 (0.72 to 1.25) | – |
| Rheumatoid arthritis | 2.01 (0.37 to 11.0) | – | 0.88 (0.43 to 1.81) | – |
| Systemic lupus erythematosus | 4.00 (0.25 to 64.0) | – | 1.15 (0.24 to 5.52) | – |
| Chronic obstructive pulmonary disease | 1.34 (1.17 to 1.54)*** | 0.89 (0.75 to 1.05) | 1.07 (0.94 to 1.23) | – |
| Asthma | 1.30 (1.08 to 1.56)*** | 0.99 (0.80 to 1.23) | 1.10 (0.95 to 1.28) | – |
| Pulmonary circulation disorders | 1.07 (0.49 to 2.34) | – | 1.31 (0.77 to 2.24) | – |
| Diabetes | 1.67 (1.44 to 1.94)*** | 1.19 (1.00 to 1.40)* | 1.26 (1.11 to 1.42)*** | 0.93 (0.81 to 1.07) |
| Hypothyroidism | 1.18 (0.43 to 3.20) | – | 1.34 (0.89 to 2. 01) | – |
| Renal failure | 1.46 (1.23 to 1.73)*** | 0.96 (0.80 to 1.16) | 1.17 (1.00 to 1.36)* | 0.87 (0.74 to 1.03) |
| Liver diseases | 1.57 (1.37 to 1.80)*** | 1.29 (1.11 to 1.50)** | 1.30 (1.16 to 1.46)*** | 1.05 (0.92 to 1.19) |
| Peptic ulcers | 1.40 (1.24 to 1.59)*** | 1.01 (0.87 to 1.16) | 1.46 (1.32 to 1.61)*** | 1.14 (1.02, 1.28)* |
| Hepatitis B | 1.25 (0.97 to 1.61) | – | 1.17 (0.93 to 1.47) | – |
| Tuberculosis | 1.29 (0.95 to 1.75) | – | 1.02 (0.68 to 1.52) | – |
| Deficiency anaemia | 1.48 (0.99 to 2.20) | – | 1.12 (0.87 to 1.44) | – |
| Depression | 1.67 (1.31 to 2.13)*** | 1.20 (0.93 to 1.57) | 1.40 (1.20 to 1.64)*** | 1.11 (0.94 to 1.31) |
| Psychosis | 1.13 (0.81 to 1.59) | – | 1.22 (0.98 to 1.52) | – |
| Metastatic cancer | – | – | – | – |
| Solid tumour | 1.23 (0.93 to 1.61) | – | 1.22 (0.98 to 1.50) | – |
| Cataract | 4.37 (3.84 to 4.96)*** | 4.30 (3.74 to 4.94)*** | 3.54 (3.20 to 3.92)*** | 3.54 (3.18 to 3.95)*** |
*p<0.05; **p<0.01; ****p<0.001.
†Covariables which were significantly associated with risk of PACG in univariable unconditional logistic regression model were further analysed by multivariable unconditional logistic regression model.
For the age group of 64 years and younger, patients with comorbidity of hyperlipidaemia (ORs: 1.20, 95% CI: 1.03 to 1.40), peptic ulcers (ORs: 1.21, 95% CI: 1.05 to 1.40), and cataract (ORs: 5.91, 95% CI: 5.07 to 6.90) were significantly associated with increasing PACG risk (table 4). For the age group of 65 years and older, patients with cataract were significantly associated with increasing PACG risk (ORs: 5.07, 95% CI: 4.46 to 5.77).
Table 4.
Factors affecting the risk of primary angle-closure glaucoma according to the age
| Variable | Age ≤64 | Age ≥65 | ||
| Crude | Adjusted† | Crude | Adjusted† | |
| OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
|---|---|---|---|---|
| Comorbidity | ||||
| Hypertension | 1.77 (1.57 to 2.00)*** | 1.15 (0.99 to 1.34) | 1.35 (1.20 to 1.51)*** | 1.10 (0.97 to 1.25) |
| Ischaemic heart disease | 1.79 (1.54 to 2.08)*** | 1.00 (0.83 to 1.21) | 1.23 (0.11 to 1.36)*** | 0.95 (0.84 to 1.07) |
| Hyperlipidaemia | 1.81 (1.60 to 2.06)*** | 1.20 (1.03 to 1.40)* | 1.28 (1.15 to 1.41)*** | 1.04 (0.92 to 1.16) |
| Congestive heart failure | 1.75 (1.24 to 2.48)*** | 0.96 (0.64 to 1.44) | 0.89 (0.74 to 1.06) | – |
| Cardiac arrhythmias | 1.49 (1.22 to 1.83)*** | 1.01 (0.80 to 1.28) | 1.14 (1.01 to 1.29)* | 0.92 (0.80 to 1.06) |
| Peripheral vascular disorders | 1.65 (1.14 to 2.40)*** | 0.84 (0.55 to 1.28) | 1.40 (1.16 to 1.68)*** | 1.13 (0.93 to 1.38) |
| Stroke | 1.40 (0.99 to 1.96) | – | 0.92 (0.78 to 1.08) | – |
| Headaches | 1.48 (1.31 to 1.67)*** | 1.14 (1.00 to 1.30) | 1.20 (1.09 to 1.33)*** | 1.04 (0.93 to 1.16) |
| Migraine | 1.13 (0.83 to 1.52) | – | 1.08 (0.83 to 1.42) | – |
| Epilepsy | 1.17 (0.61 to 2.24) | – | 0.70 (0.42 to 1.15) | – |
| Dementia | 2.46 (1.16 to 5.21)*** | 1.31 (0.57 to 3.05) | 0.92 (0.73 to 1.15) | – |
| Rheumatoid arthritis | 1.26 (0.50 to 3.17) | – | 0.77 (0.30 to 2.00) | – |
| Systemic lupus erythematosus | 2.01 (0.18 to 22.1) | – | 1.33 (0.27 to 6.61) | – |
| Chronic obstructive pulmonary disease | 1.60 (1.33 to 1.93)*** | 1.08 (0.87 to 1.34) | 1.09 (0.97 to 1.22) | – |
| Asthma | 1.42 (1.15 to 1.76)*** | 1.00 (0.78 to 1.28) | 1.08 (0.94 to 1.25) | – |
| Pulmonary circulation disorders | 1.72 (0.66 to 4.48) | – | 1.13 (0.69 to 1.86) | – |
| Diabetes | 1.92 (1.63 to 2.25)*** | 1.08 (0.89 to 1.31) | 1.21 (1.08 to 1.37)** | 0.96 (0.84 to 1.09) |
| Hypothyroidism | 1.28 (0.71 to 2.30) | – | 1.34 (0.81 to 2.19) | – |
| Renal failure | 1.82 (1.48 to 2.24)*** | 1.08 (0.85 to 1.37) | 1.13 (0.99 to 1.30) | – |
| Liver diseases | 1.64 (1.43 to 1.87)*** | 1.12 (0.96 to 1.31) | 1.26 (1.13 to 1.42)*** | 1.02 (0.90 to 1.16) |
| Peptic ulcers | 1.70 (1.50 to 1.92)*** | 1.21 (1.05 to 1.40)** | 1.32 (1.19 to 1.45)*** | 1.06 (0.95 to 1.19) |
| Hepatitis B | 1.06 (0.83 to 1.35) | – | 1.37 (1.08 to 1.73)*** | 1.20 (0.93 to 1.54) |
| Tuberculosis | 1.08 (0.64 to 1.82) | – | 1.21 (0.92 to 1.59) | – |
| Deficiency anaemia | 1.36 (0.95 to 1.92) | – | 1.13 (0.86 to 1.47) | – |
| Depression | 1.78 (1.45 to 2.20)*** | 1.18 (0.93 to 1.50) | 1.31 (1.10 to 1.55)** | 1.01 (0.85 to 1.21) |
| Psychosis | 1.27 (0.95 to 1.69) | – | 1.14 (0.90 to 1.45) | – |
| Metastatic cancer | – | – | – | – |
| Solid tumour | 1.16 (0.83 to 1.60) | – | 1.25 (1.02 to 1.51)* | 1.15 (0.94 to 1.41) |
| Cataract | 6.95 (6.00 to 8.05)*** | 5.91 (5.07 to 6.90)*** | 5.18 (4.56 to 5.87)*** | 5.07 (4.46 to 5.77)*** |
*p<0.05; **p<0.01; ****p<0.001.
†Covariables which were significantly associated with risk of PACG in univariable unconditional logistic regression model were further analysed by multivariable unconditional logistic regression model.
Discussion
Among the 3322 PACG patients, 41.8% had hyperlipidaemia, 42.4% had headache and peptic ulcer and 62.9% had cataract. The risk of PACG was greater for patients with the comorbidities of hyperlipidaemia, headaches, liver diseases, peptic ulcers, and cataract. For the male group, diabetes, liver diseases and cataract were significantly associated with increasing PACG risk. For the female group, hyperlipidaemia, headaches, peptic ulcers and cataract were significantly associated with increasing PACG risk. For both the genders, cataract was the same and strongest risk factor for PACG development (ORs: 4.30 for the male group; ORs: 3.54 for the female group).
Regarding the effect of age on the risk of PACG, we subclassified the study groups into two. Interesting results were obtained; patients with comorbidity of hyperlipidaemia, peptic ulcers and cataract were associated with increasing PACG risk in the age group of 64 years and younger. However, for the age group of 65 years and older, cataract was the only factor for the increased risk of PACG. Cataract was the same and strongest risk factor for PACG onset for both the age groups (ORs: 5.91 for the age group younger than 65 years; ORs: 5.07 for the age group older than 65 years).
Our study is the first one that discussed the medical comorbidity in a large PACG cohort using large claims database. Potential explanations about the strong relationship between some medical illness and the risk of PACG should be mentioned as below.
Pathogenetic mechanisms of PACG and association between cataract and PACG
Our study reveals that cataract is the strongest risk factor for PACG in any age group and gender compared to other medical comorbidity. PACG has its characteristic anatomy features and unique pathological process, including a crowded anterior segment and narrow anterior chamber angle.15 The lens is considered to play a crucial role in the pathogenesis of PACG either because of an increase in its thickness or a more anterior position resulting in angle crowding and a greater predisposition to pupillary block.5 6 15 16 Furthermore, the lens thickness increases with age and makes the narrow anterior chamber angle even more crowded, which might be why most PACG occurs in patients older than 40 years.15 16 Our study result supports that ocular anatomical factor plays a more important role in the pathogenesis of PACG than any other medical comorbidities in Taiwan Chinese population.
Association between hyperlipidaemia and diabetes and PACG
In one Korean epidemiological study, hypercholesterolemia, hypertension and diabetes mellitus were independent risk factors for the development of any cataract.17 Moreover, in one study, the authors demonstrated that metabolic syndrome and its components are associated with age-related cataract only among Korean women.18 We believe that the potential reasons for diabetes and hyperlipidaemia in the risk of PACG from our result could be attributed to the increased risk of cataract. Further, longitudinal observational study is needed to address this issue.
Association between liver disease and PACG
One recent study from Taiwan reported that hepatitis C infection, even without the complication of cirrhosis, is associated with an increased risk of cataract.19 Another study from Korean reported that hepatitis B and hepatitis C infection were significantly associated with cataract.20 The strong association between liver disease and the risk of PACG might increase the risk of cataract in liver disease patients. However, further study is needed to elucidate this interesting result.
Association between headache and PACG
PACG patients complain of headache caused by increased intraocular pressure.21 22 PACG patients seek medical help due to headache before the diagnosis of PACG. Our results indicate that headache is associated with higher risk for PACG. Headache may be a symptom of PACG missed by the physician. Therefore, clinicians should consider the possibility of PACG in patients with headache.
Association between peptic ulcers and PACG
No previous study has reported the presence or absence of an association between peptic ulcers and PACG. We speculate that Histamine 2 receptor antagonist that was widely used in peptic ulcer treatment might induce or precipitate PACG.23 Further longitudinal study is mandatory in this interesting topic.
Despite these promising results, our study had certain limitations. First, glaucoma and medical comorbidity were defined entirely on the basis of claims data (ICD-9-CM codes assigned by clinicians).21 This approach is less accurate than diagnosing personally through a standardised procedure.21 The second limitation is selection bias.21 Because the NHI database only comprises data of patients who have received treatment, patients who have received no treatment for glaucoma or any of these medial disease might have been recruited in the comparison cohort. Third, despite the large sample, the study cohort comprised Taiwanese patients. Therefore, these findings cannot be generalised to other populations. Nevertheless, our study has the following strengths. First, the strength of the database is excellent because of the large sample randomization.21 We could follow patient cases over time to assess the relationship between medical illness and the subsequent onset of PACG. Second, the database includes data of people with diverse sociodemographic profiles, unlike some smaller studies that recruited patients from specific regions and thus lack in representativeness.
In conclusion, our population-based study using the NHIRD revealed that the PACG risk is strongest in cataract patients and is slightly higher in patients with medical comorbidities of hyperlipidaemia, headaches, liver diseases, and peptic ulcers. Clinicians should be aware of these findings when encountering patients with these diseases.
Supplementary Material
Footnotes
Contributors: H-YC: conception and design, acquisition of data, analysis and interpretation of data, drafting of the manuscript, critical revision of the manuscript for important intellectual content, obtaining funding; C-LL: statistical expertise, conception and design, critical revision of the manuscript for important intellectual content.
Funding: This work was supported by grants from the Ministry of Health and Welfare, Taiwan (MOHW107-TDU-B-212-123004), China Medical University Hospital, Academia Sinica Stroke Biosignature Project (BM10701010021), MOST Clinical Trial Consortium for Stroke (MOST 107-2321-B-039 -004-), Tseng-Lien Lin Foundation, Taichung, Taiwan, and Katsuzo and Kiyo Aoshima Memorial Funds, Japan.
Competing interests: None declared.
Ethics approval: The study was approved by the Institutional Review Board (IRB) of China Medical University and Hospital (CMUH-104-REC2-115).
Provenance and peer review: Not commissioned; externally peer reviewed.
Data sharing statement: No additional data are available.
Patient consent for publication: Not required.
References
- 1. Chen HY, Huang ML, Tsai YY, et al. . Comparing glaucomatous optic neuropathy in primary open angle and primary angle closure glaucoma eyes by scanning laser polarimetry-variable corneal compensation. J Glaucoma 2008;17:105–10. 10.1097/IJG.0b013e31814b9971 [DOI] [PubMed] [Google Scholar]
- 2. Chan EW, Li X, Tham YC, et al. . Glaucoma in Asia: regional prevalence variations and future projections. Br J Ophthalmol 2016;100:78–85. 10.1136/bjophthalmol-2014-306102 [DOI] [PubMed] [Google Scholar]
- 3. Vijaya L, George R, Arvind H, et al. . Prevalence of primary angle-closure disease in an urban south Indian population and comparison with a rural population. The Chennai Glaucoma Study. Ophthalmology 2008;115:655–60. 10.1016/j.ophtha.2007.05.034 [DOI] [PubMed] [Google Scholar]
- 4. Cheng JW, Zong Y, Zeng YY, et al. . The prevalence of primary angle closure glaucoma in adult Asians: a systematic review and meta-analysis. PLoS One 2014;9:e103222 10.1371/journal.pone.0103222 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Wright C, Tawfik MA, Waisbourd M, et al. . Primary angle-closure glaucoma: an update. Acta Ophthalmol 2016;94 10.1111/aos.12784 [DOI] [PubMed] [Google Scholar]
- 6. Trikha S, Perera SA, Husain R, et al. . The role of lens extraction in the current management of primary angle-closure glaucoma. Curr Opin Ophthalmol 2015;26:128–34. 10.1097/ICU.0000000000000128 [DOI] [PubMed] [Google Scholar]
- 7. Pache M, Flammer J. A sick eye in a sick body? Systemic findings in patients with primary open-angle glaucoma. Surv Ophthalmol 2006;51:179–212. 10.1016/j.survophthal.2006.02.008 [DOI] [PubMed] [Google Scholar]
- 8. Bayer AU, Ferrari F, Erb C. High occurrence rate of glaucoma among patients with Alzheimer’s disease. Eur Neurol 2002;47:165–8. 10.1159/000047976 [DOI] [PubMed] [Google Scholar]
- 9. Skalicky S, Goldberg I. Depression and quality of life in patients with glaucoma: a cross-sectional analysis using the Geriatric Depression Scale-15, assessment of function related to vision, and the Glaucoma Quality of Life-15. J Glaucoma 2008;17:546–51. 10.1097/IJG.0b013e318163bdd1 [DOI] [PubMed] [Google Scholar]
- 10. Chen HY, Chang YC, Lin CC, et al. . Obstructive sleep apnea patients having surgery are less associated with glaucoma. J Ophthalmol 2014;2014:1–6. 10.1155/2014/838912 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Lin HC, Chien CW, Hu CC, et al. . Comparison of comorbid conditions between open-angle glaucoma patients and a control cohort: a case-control study. Ophthalmology 2010;117:2088–95. 10.1016/j.ophtha.2010.03.003 [DOI] [PubMed] [Google Scholar]
- 12. Gordon MO, Beiser JA, Brandt JD, et al. . Ocular Hypertension Treatment Study Group. The Ocular Hypertension Treatment Study: baseline factors that predict the onset of primary open angle glaucoma. Arch Ophthalmol 2002;120:714–20. [DOI] [PubMed] [Google Scholar]
- 13. Newman-Casey PA, Talwar N, Nan B, et al. . The relationship between components of metabolic syndrome and open-angle glaucoma. Ophthalmology 2011;118:1318–26. 10.1016/j.ophtha.2010.11.022 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Database NHIR. Taiwan. 2015. http://nhird.nhri.org.tw/en/Background.html
- 15. Foster P, He M, Liebmann J. Epidemiology, classification and mechanism : Weinreb R, Friedman D, Angle Closure and Angle Closure Glaucoma. The Hague, The Netherlands: Kugler, 2006:1–20. [Google Scholar]
- 16. Sun X, Dai Y, Chen Y, et al. . Primary angle closure glaucoma: What we know and what we don’t know. Prog Retin Eye Res 2017;57:26–45. 10.1016/j.preteyeres.2016.12.003 [DOI] [PubMed] [Google Scholar]
- 17. Rim TH, Kim MH, Kim WC, et al. . Cataract subtype risk factors identified from the Korea National Health and Nutrition Examination survey 2008-2010. BMC Ophthalmol 2014;14:4 10.1186/1471-2415-14-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Park YH, Shin JA, Han K, et al. . Gender difference in the association of metabolic syndrome and its components with age-related cataract: the Korea National Health and Nutrition Examination Survey 2008-2010. PLoS One 2014;9:e85068 10.1371/journal.pone.0085068 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Lin SY, Lin CL, Ju SW, et al. . Increasing risk of cataract in HCV patients receiving anti-HCV therapy: a nationwide cohort study. PLoS One 2017;12:e0173125 10.1371/journal.pone.0173125 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Park S, Choi NK. Hepatitis virus infection and age-related cataract. Sci Rep 2017;7:13089 10.1038/s41598-017-13283-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Nesher R, Mimouni MD, Khoury S, et al. . Delayed diagnosis of subacute angle closure glaucoma in patients presenting with headaches. Acta Neurol Belg 2014;114:269–72. 10.1007/s13760-014-0290-2 [DOI] [PubMed] [Google Scholar]
- 22. Nesher R, Epstein E, Stern Y, et al. . Headaches as the main presenting symptom of subacute angle closure glaucoma. Headache 2005;45:172–6. 10.1111/j.1526-4610.2005.05034_4.x [DOI] [PubMed] [Google Scholar]
- 23. Tripathi RC, Tripathi BJ, Haggerty C. Drug-induced glaucomas: mechanism and management. Drug Saf 2003;26:749–67. 10.2165/00002018-200326110-00002 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.