Diabetes mellitus and incident glaucoma in Australia: a 10-year cohort study from the 45 and Up Study.

Background: Understanding the relationship between diabetes mellitus (DM) and the severity of glaucoma is important for the primary prevention of incident glaucoma. This paper aims to examine the association between DM and incident glaucoma.
Methods: The 45 and Up Study is a prospective cohort study where Australians aged ≥45 years old were enrolled. The incident of glaucoma at follow-up is the main outcome measured. Glaucoma incidence was identified as those with recorded glaucoma-related medication from the Pharmaceutical Benefits Scheme or surgery recorded in the Medicare Benefits Schedule. Patients with glaucoma were classified into the medical glaucoma group (with glaucoma-related medication but not surgery) and the surgical glaucoma group (with glaucoma-related surgery). A Cox regression model was used to calculate the hazard ratios (HRs) to examine the association between baseline DM and the risk of developing glaucoma during the follow-up period. The reference groups are as follows: (I) non-DM participant; (II) participant with DM, duration between 0 and 5 years; (III) participant uses insulin.
Results: A total of 255,547 eligible participants, with no glaucoma diagnosis at baseline, were included. During the follow-up period, 7,667 patients (3.0%) were identified as medical glaucoma only and 2,326 patients (0.9%) underwent glaucoma surgery. After controlling for confounders, baseline DM was associated with an increased risk of glaucoma in the medical glaucoma group only [hazard ratio (HR) =1.36, 95% confidence interval (CI) =1.07-1.72, P=0.002]. However, baseline DM (HR =0.97, 95% CI =0.57-1.65, P=0.979) was not associated with an increased risk of surgical glaucoma. Conclusions: DM was associated with an increased risk of medical glaucoma only, there was no association identified with surgical glaucoma in the Australian population recruited in the 45 and Up Study. 2022 Annals of Translational Medicine. All rights reserved.

Introduction

Glaucoma is one of the significant causes of irreversible blindness worldwide, with 79.6 million people affected in 2020, of those, 10% will be bilaterally blind (1). It is estimated to affect up to 13.48% of adults aged over 50 years in Australia (2). It is a progressive optic neuropathy characterized as glaucomatous optic neuropathy with possible glaucomatous visual field loss. Glaucoma does not show any symptoms in the early stages (3). Therefore, the identification of risk factors is of the utmost importance for glaucoma prevention.

Diabetes mellitus (DM) is a growing international public health issue due to lifestyle changes and an aging population. It is estimated that the global prevalence of DM has nearly doubled since 1980, rising from 4.7% to 8.5% in the adult population (4). DM causes many severe acute and chronic complications that negatively affect patients’ quality of life and survival, therefore imposing a huge burden on society.

Although many risk factors for glaucoma have been identified, such as older age, ethnicity, elevated intraocular pressure (IOP), family history (5,6), systemic hyper- and hypotension, smoking, alcohol consumption, low level of physical activity (PA) (7-9) and poor glycemic control (10), the association between DM and glaucoma is still contentious. Furthermore, evidence in Australia of this relationship is especially limited. Some studies have found that patients with DM had a greater risk of developing glaucoma (11-17). However, several studies have failed to observe any significant association between these two diseases (18-22). In addition, there is no evidence to date investigating the relationship between the severity of glaucoma and DM.

Therefore, we conducted this present study using data from the 45 and Up Study, to provide a more accurate estimate of the relationship between DM and the severity of glaucoma over 10 years in the general elderly Australian population. We present the following article in accordance with the STROBE reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-22-41/rc).

Methods

Data source and study population

Participants in this study were selected from the Sax Institute’s 45 and Up Study, a large Australian population-based prospective study established to explore health and aging. All participants are aged at least 45 years and live in the state of New South Wales (NSW). Participants are randomly selected from the Services Australia (formerly the Australian Government Department of Human Services) Medicare enrolment database. A total of 267,153 participants joined the study, with an overall response rate of 18%, corresponding to 10% of all NSW residents aged 45 years and above. All participants received a mailed invitation and gave a signed consent form for the baseline study questionnaire and linkage of their information to routinely collected health databases. The study methodology has been described in detail elsewhere (23). In brief, recruitment was undertaken between January 1st, 2006, and December 31st, 2009. Demographic details, socioeconomic status, chronic disease, medication history, family history and lifestyle behaviors, were collected in the baseline questionnaire survey, which is available online (http://www.saxinstitute.org.au/our-work/45-up-study/questionnaires/). All of the data was then linked to Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) records, which were available from 2004 to 2016.

The 45 and Up Study was approved by the University of New South Wales Human Research Ethics Committee (HREC). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The present study was approved by the Royal Victorian Eye and Ear Hospital Human Research Ethics Committee (No. 17/1330HS/20) and informed consent was taken from all the patients.

Inclusion and exclusion criteria

The present study included all patients from the 45 and Up Study at baseline, and the exclusion criteria of the study sample were as follows: (I) those who had been prescribed a topical glaucoma medication at least three times; (II) patients who had previously undergone a glaucoma-related surgery before the baseline examination; (III) those with missing data on confounding variables or implausible data (Logic error in the questionnaire of DM or not and DM duration).

Confounding variables

All confounding variables were derived from the self-reported baseline questionnaire. (http://www.saxinstitute.org.au/ourwork/45-up-study/questionnaires/). Demographic factors included age, sex, income level (AUD/year) and education level. Health-related factors included body mass index (BMI), insulin treatment, diabetes duration, family history of diabetes, history of hypertension and history of cardiovascular disease. Lifestyle factors included alcohol consumption, smoking status and PA.

DM was defined as subjects who gave an affirmative answer to the following question ‘Has a doctor EVER told you that you have diabetes or who had claims of any anti-hyperglycemic medications before the baseline examination. Identifying DM patients according to self-reported status has been proven to be a valid method of identification with high sensitivity and specificity in the 45 and Up Study (24).

Age was categorized into four groups: 45–54, 55–64, 65–74 and ≥75 years. Participant household income level was divided into four groups: more than $70,000, $40,000 to $70,000, $20,000 to $40,000 and <$20,000 (Australia dollars). The education level was categorized into three groups: No qualifications, certificate, diploma or trade and a university degree or higher. BMI was categorized into four groups: underweight (<18.4 kg/m2), normal (18.5 to <24.9 kg/m2), overweight (25.0 to <29.9 kg/m2) or obese (≥30.0 to 50 kg/m2). The PA level was divided into three categories: <5, ≥5–14 and ≥15 based on the metabolic equivalent intensity level (MET-adjusted) sessions per week (25). The MET-adjusted number of PA over the last week was calculated as W+M+2V, where the sum of the total number of times walking (W), undertaking moderate activity (M) and two times the number of times doing vigorous activities (V). Diabetes duration was divided into four groups: <10, ≥10–19, ≥20–29 and ≥30 years. Insulin treatment was defined as a record of insulin prescription. Family history of diabetes, history of hypertension, and history of cardiovascular disease, as well as smoking and drinking status, were analyzed as dichotomous variables.

Incident glaucoma

The incidence of glaucoma during the 10-year follow-up was defined based on the MBS and PBS databases. Patients with glaucoma were classified into two groups: the medical glaucoma group and the surgical glaucoma group. Patients in the medical glaucoma group were identified as patients who were solely treated with topical glaucoma medications (at least three claims through PBS for glaucoma-related prescriptions). Patients in the surgical glaucoma group were identified as those who underwent glaucoma-related surgery during the follow-up period. A total of 64 PBS item codes and 13 procedures codes in MBS were used to classify patients into the medical and surgical glaucoma group. The details of the codes are shown in Table S1.

Statistical analysis

Baseline characteristics of the study participants were described using descriptive statistics. Categorical variables were calculated and compared using a chi-square test. Three Cox proportional hazards models were fitted to calculate the hazard ratios (HRs) and the 95% confidence intervals (CIs), to estimate the effect of DM on the risk of developing glaucoma during the follow-up period. The crude model was adjusted for age and gender, and model 2 was further adjusted for income, education level, history of hypertension and CVD, family history of DM, smoking status, alcohol intake and PA. In addition, history of DM, diabetes duration and insulin dependence were added to model 3. Statistical significance was defined as a P value of <0.05. All statistical analysis was performed using SAS 9.4 (SAS Institute, Cary, North Carolina).

Results

Of the 267,153 participants, 11,606 (4.3%) were excluded and a total of 255,547 (95.7%) were included in our study. shows the brief flow chart of the selection process for the study population, including the specific inclusion/exclusion criteria and grouping steps. A total of 22,443 (8.8%) eligible participants had DM at baseline. During the follow-up period, a total of 9,993 (3.9%) participants were defined as having glaucoma, including 7,667 (76.7%) participants in the medical group and 2,326 (23.2%) participants in the surgical glaucoma group. The mean ± SD age of participants at baseline was 61.7±5.2 years, with 53.7% (n=137,232) being female.

Figure 1

Flow chart of the population selection process in this study.

Baseline characteristics of participants were stratified by glaucoma status during the follow-up period are presented in . The distribution of glaucoma status was significantly different in participants with different ages (P<0.001), gender (P<0.001), income (P<0.001), education level (P<0.001), BMI (P=0.026), history of hypertension (P<0.001), CVD history (P<0.001), diabetes duration (P<0.001), insulin-treated history (P=0.004), and diabetes status (P<0.001). The distribution of PA, smoking status, alcohol consumption and family history of diabetes were not statistically different among those without glaucoma, compared to those with medical glaucoma or surgical glaucoma. Compared to the medical glaucoma group, participants in the surgical glaucoma group were more likely to be younger than 65 years old, female, have a higher income and education, and consume alcohol, but were less likely to be obese, smoke, have DM, a longer diabetes duration, take insulin and perform PA.

Table 1

Comparison of baseline characteristics among participants without glaucoma, participants in medical and surgical glaucoma group during the follow-up period

Characteristics	All (N=255,547)	Glaucoma			P value*
		Without (N=245,554)	Medical (N=7,667)	Surgical (N=2,326)
Age (y)					<0.001
45–54	77,305 (30.3)	76,200 (31.0)	809 (10.6)	296 (12.7)
55–64	84,017 (32.9)	81,072 (33.0)	2,185 (28.5)	760 (32.7)
65–74	54,554 (21.3)	51,114 (20.8)	2,651 (34.6)	789 (33.9)
≥75	39,669 (15.5)	37,166 (15.1)	2,022 (26.4)	481 (20.7)
Missing value	2 (0.0)	2 (0.0)	0 (0.0)	0 (0.0)
Female	137,232 (53.7)	132,180 (53.8)	3,840 (50.1)	1,212 (52.1)	<0.001
Income (AUD/y)					<0.001
<20,000	49,089 (19.2)	46,614 (19.0)	1,948 (25.4)	527 (22.7)
20,000–40,000	44,362 (17.4)	42,397 (17.3)	1,526 (19.9)	439 (18.9)
40,000–70,000	45,749 (17.9)	44,199 (18.0)	1,155 (15.1)	395 (17.0)
>70,000	61,739 (24.2)	60,157 (24.5)	1,175 (15.3)	407 (17.5)
Missing value	54,608 (21.4)	52,187 (21.3)	1,863 (24.3)	558 (24.0)
Education					<0.001
No qualifications	29,373 (11.5)	27,991 (11.4)	1,097 (14.3)	285 (12.3)
Certificate/diploma/trade	162,302 (63.5)	155,952 (63.5)	4,854 (63.3)	1,496 (64.3)
University degree	59,695 (23.4)	57,645 (23.5)	1,550 (20.2)	500 (21.5)
Missing value	4,177 (1.6)	3,966 (1.6)	166 (2.2)	45 (1.9)
Body mass index					0.026
Underweight	3,200 (1.3)	3,073 (1.3)	93 (1.2)	34 (1.5)
Normal	86,737 (33.9)	83,315 (33.9)	2,572 (33.5)	850 (36.5)
Overweight	93,452 (36.6)	89,715 (36.5)	2,911 (38.0)	826 (35.5)
Obese	55,387 (21.7)	53,360 (21.7)	1,591 (20.8)	436 (18.7)
Missing value	16,771 (6.6)	16,091 (6.6)	500 (6.5)	180 (7.7)
Physical activity (sessions/week)					0.227
<5	48,128 (18.8)	46,328 (18.9)	1,421 (18.5)	379 (16.3)
5–14	13,4945 (52.8)	129,536 (52.8)	4,154 (54.2)	1,255 (54.0)
≥15	62,923 (24.6)	60,550 (24.7)	1,764 (23.0)	609 (26.2)
Missing value	9,551 (3.7)	9,140 (3.7)	328 (4.3)	83 (3.6)
Current smoker	18,932 (7.4)	18,486 (7.5)	349 (4.6)	97 (4.2)	0.999
Alcohol drinker	168,547 (66.0)	162,131 (66.0)	4,874 (63.6)	1,542 (66.3)	0.220
Hypertension	89,442 (35.0)	85,203 (34.7)	3,322 (43.3)	917 (39.4)	<0.001
Cardiovascular disease	34,487 (13.5)	32,776 (13.3)	1,366 (17.8)	345 (14.8)	<0.001
Family history of diabetes	58,570 (22.9)	56,306 (22.9)	1,743 (22.7)	521 (22.4)	0.454
Diabetes duration (y)					<0.001
<10	13,490 (5.3)	12,820 (5.2)	549 (7.2)	121 (5.2)
10–19	5,242 (2.1)	4,939 (2.0)	243 (3.2)	60 (2.6)
20–29	1,460 (0.6)	1,359 (0.6)	86 (1.1)	15 (0.7)
≥30	788 (0.3)	734 (0.3)	47 (0.6)	7 (0.3)
No diabetes or missing value	234,567 (91.8)	225,702 (91.9)	6,742 (87.9)	2,123 (91.3)
Insulin treated	2,954 (1.2)	2,795 (1.1)	133 (1.7)	26 (1.1)	0.004
Diabetes at baseline	22,433 (8.8)	21,216 (8.6)	999 (13.0)	218 (9.4)	<0.001

Data are expressed as numbers (%). *, P value for trend among the non-glaucoma group, medical glaucoma group and surgical glaucoma group. AUD, Australian dollar.

shows the baseline characteristics of participants stratified by diabetes. The prevalence of DM was 8.8% (n=22,433). The distribution of DM status varied significantly by age, gender, income, education level, BMI, PA, smoking, alcohol intake, history of hypertension, CVD history and family history of diabetes (all P<0.001). Patients with DM were more likely to be older, male, poorer lifestyle habits (smoking, drinking, less PA), and have more comorbidities (hypertension, CVD, family history of DM) compared to patients without diabetes.

Table 2

Comparison of baseline characteristics between participants with or without diabetes

Characteristics	All (N=255,547)	Diabetes		P value*
		With (N=22,433)	Without (N=233,114)
Age (y)				<0.001
45–54	77,305 (30.3)	3,307 (14.7)	73,998 (31.7)
55–64	84,017 (32.9)	6,801 (30.3)	77,216 (33.1)
65–74	54,554 (21.3)	6,924 (30.9)	47,630 (20.4)
≥75	39,669 (15.5)	5,401 (24.1)	34,268 (14.7)
Missing value	2 (0.0)	0 (0.0)	2 (0.0)
Female	137,232 (53.7)	9,426 (42.0)	127,806 (54.8)	<0.001
Income (AUD/y)				<0.001
<20,000	49,089 (19.2)	7,173 (32.0)	41,916 (18.0)
20,000–40,000	44,362 (17.4)	4,262 (19.0)	40,100 (17.2)
40,000–70,000	45,749 (17.9)	2,974 (13.3)	42,775 (18.3)
>70,000	61,739 (24.2)	2,797 (12.5)	58,942 (25.3)
Missing value	54,608 (21.4)	5,227 (23.3)	49,381 (21.2)
Education				<0.001
No qualifications	29,373 (11.5)	4,103 (18.3)	25,270 (10.8)
Certificate/diploma/trade	162,302 (63.5)	14,365 (64.0)	147,937 (63.5)
University degree	59,695 (23.4)	3,422 (15.3)	56,273 (24.1)
Missing value	4,177 (1.6)	543 (2.4)	3,634 (1.6)
Body mass index				<0.001
Underweight	3,200 (1.3)	150 (0.7)	3,050 (1.3)
Normal	86,737 (33.9)	4,003 (17.8)	82,734 (35.5)
Overweight	93,452 (36.6)	7,612 (33.9)	85,840 (36.8)
Obese	55,387 (21.7)	9,050 (40.3)	46,337 (19.9)
Missing value	16,771 (6.6)	1,618 (7.2)	15,153 (6.5)
Physical activity (sessions/week)				<0.001
<5	48,128 (18.8)	5,854 (26.1)	42,274 (18.1)
5–14	13,4945 (52.8)	11,251 (50.2)	123,694 (53.1)
≥15	62,923 (24.6)	4,107 (18.3)	58,816 (25.2)
Missing value	9,551 (3.7)	1,221 (5.4)	8,330 (3.6)
Current smoker	18,932 (7.4)	1,601 (7.1)	17,331 (7.4)	<0.001
Alcohol drinker	168,547 (66.0)	157,068 (67.4)	11,479 (51.2)	<0.001
Hypertension	89,442 (35.0)	13,571 (60.5)	75,871 (32.5)	<0.001
Cardiovascular disease	34,487 (13.5)	6,190 (27.6)	28,297 (12.1)	<0.001
Family history of diabetes	58,570 (22.9)	10,322 (46.0)	48,248 (20.7)	<0.001
Diabetes duration (y)
<10	13,490 (5.3)	13,490 (60.1)	–	–
10–19	5,242 (2.1)	5,242 (23.4)	–	–
20–29	1,460 (0.6)	1,460 (6.5)	–	–
≥30	788 (0.3)	788 (3.5)	–	–
No diabetes or missing value	234,567 (91.8)	1,453 (6.5)	233,114 (100.0)
Insulin treated	2,954 (1.2)	2,888 (12.9)	–	–

Data are expressed as numbers (%). *, P value for trend between with diabetes group and non-diabetes group. AUD, Australian dollar.

reports the associations between diabetes-related factors and different glaucoma statuses in 45 and Up Study participants. In any multiple Cox regression model, DM at baseline (Model 1: HR =1.25, 95% CI =1.17–1.34, P<0.001; Model 2: HR =1.22, 95% CI =1.14–1.32, P<0.001; Model 3: HR =1.36, 95% CI =1.07–1.72, P=0.002) and not using insulin (Model 1: HR =1.42; 95% CI =1.19–1.69; P<0.001; Model 2: HR =1.37; 95% CI =1.15–1.64; P<0.001; Model 3: HR =1.16; 95% CI =1.09–1.41; P=0.025) had significantly higher risk of medical glaucoma. Meanwhile, longer DM duration (P trend >0.05) was not associated with an increased risk of glaucoma in the medical glaucoma group. However, baseline DM (P>0.05), DM duration (P>0.05) and those who were not treated with insulin (P>0.05) were not associated with an increased risk of surgical glaucoma except for increased age.

Table 3

Adjusted Cox regression analysis of associations between diabetes mellitus, diabetes duration, taken insulin and glaucoma in the medical and surgical group

Variable	Multiple regression analysis
	Medical glaucoma									Surgical glaucoma
	Model 1*			Model 2#			Model 3^			Model 1*			Model 2#			Model 3^
	HR (95% CI)	P value		HR (95% CI)	P value		HR (95% CI)	P value		HR (95% CI)	P value		HR (95% CI)	P value		HR (95% CI)	P value
Age (y)
45–54	Ref.	<0.001		Ref.	<0.001		Ref.	<0.001		Ref.	0.001		Ref.	0.004		Ref.	<0.001
55–64	2.54 (2.34–2.75)			2.47 (2.27–2.68)			2.46 (2.26–2.67)			2.37 (2.07–2.71)			2.37 (2.07–2.72)			2.37 (2.07–2.72)
65–74	4.78 (4.42–5.18)			4.52 (4.15–4.92)			4.48 (4.11–4.88)			3.79 (3.31–4.34)			3.83 (3.31–4.43)			3.83 (3.32–4.44)
≥75	5.04 (4.63–5.48)			4.77 (4.35–5.23)			4.72 (4.31–5.18)			3.13 (2.70–3.63)			3.21 (2.73–3.79)			3.22 (2.73–3.79)
Diabetes
No	Ref.			Ref.			Ref.			Ref.			Ref.			Ref.
Yes	1.25 (1.17–1.34)	<0.001		1.22 (1.14–1.32)	<0.001		1.36 (1.07–1.72)	0.002		0.90 (0.78–1.04)	0.159		0.95 (0.82–1.11)	0.546		0.97 (0.57–1.65)	0.979
Diabetes duration (y)
0–5	Ref.	0.005		Ref.	0.002		Ref.	0.006		Ref.	0.333		Ref.	0.280		Ref.	0.274
5–10	1.07 (0.91–1.25)			1.08 (0.92–1.27)			1.07 (0.91–1.25)			1.30 (0.95–1.79)			1.32 (0.96–1.82)			1.33 (0.96–1.83)
10–20	1.37 (1.08–1.73)			1.40 (1.11–1.78)			1.37 (1.08–1.74)			1.14 (0.65–1.99)			1.19 (0.68–2.09)			1.20 (0.68–2.13)
>20	1.35 (1.01–1.84)			1.41 (1.02–1.92)			1.36 (0.98–1.87)			1.09 (0.51–2.34)			1.10 (0.51–2.37)			1.11 (0.51–2.43)
Taken insulin
Yes	Ref.			Ref.			Ref.			Ref.			Ref.			Ref.
No	1.42 (1.19–1.69)	<0.001		1.37 (1.15–1.64)	<0.001		1.16 (1.09–1.41)	0.025		0.98 (0.66–1.45)	0.912		1.06 (0.71–1.58)	0.769		1.08 (0.70–1.66)	0.621

*, Model 1 adjusted for age and gender; #, Model 2 adjusted for age, gender, income, education level, history of hypertension and cardiovascular disease, family history of diabetes mellitus, smoking, alcohol drinking and physical activity; ^, Model 3 adjusted for age, gender, income, education level, BMI, history of hypertension and CVD, family history of DM, smoking, alcohol drinking, physical activity, history of diabetes, diabetes duration and insulin dependence. HR, hazard ratios; CI, confidence interval; DM, diabetes mellitus; CVD, cardiovascular disease.

Discussion

This study aimed to explore the association between DM and incident glaucoma in a cohort of patients living in NSW, Australia. The strengths of this population-based study are its prospective design, long-term follow-up, national claims data on PBS and MBS and the ability to adjust for a broad array of covariates. To the best of our knowledge, our study is the largest cohort study with the longest follow-up to explore the relationship between DM and incident glaucoma. In this study, we classified incident glaucoma into two groups: the medical glaucoma group as drug-controlled glaucoma and surgical glaucoma as drug-uncontrolled glaucoma. In our study, we found that DM was associated with an increased risk of medical glaucoma but not surgical glaucoma.

There are no previous studies that have explored the association between DM and different degrees of glaucoma. Studies have instead combined the early stage and advanced stage of glaucoma. In our study, we classified glaucoma into two groups, the medical glaucoma group represents drug-controlled glaucoma, and the surgical glaucoma group represents severe glaucoma which may be drug-uncontrolled glaucoma. After adjustment for the related confounders (in model 3), DM was shown to be an independent risk factor for medical glaucoma but not surgical glaucoma. Even in the univariate analysis, no association was found between DM, DM duration, those taking oral anti-hyperglycemic medications and surgical glaucoma. Our study makes the novel point that DM was not associated with surgical glaucoma. This negative association may explain the contrasting results from previous studies (18-20). Meanwhile, no association was found between smoking and alcohol consumption and medical or surgical glaucoma. The most probable cause was the different study populations, aged 45 years and up.

Results from the previous case-control (11,26) and cross-sectional (27-29) studies investigating the association between DM and glaucoma were controversial. Previous cross-sectional studies examining the relationship between diabetes and glaucoma have the limitation of possible reverse causation, especially in studies where glaucoma cases included those whose glaucoma had occurred before a diagnosis of diabetes. However, it is worth mentioning that the association was more reliable in longitudinal studies, which had less selection bias than case-control or cross-sectional studies.

Overall, our results were consistent with several previous longitudinal studies (12-14,30,31), but not all (18-21). The two meta-analyses suggested that patients with DM were at a significantly increased risk of glaucoma incidence (32,33). But in a recent review, Grzybowski et al. demonstrated conflicting results (34). The discrepancy may be attributed to differences in demographic characteristics, duration of follow-up, glaucoma definition and the statistical analysis conducted. For example, one study included American young people aged 20 and above (14), compared to another study that only included women (13). In addition, the comorbidities, which were used for adjustment in the analyses, and the methods used to identify the presence or absence of DM and glaucoma were different. In a cohort study with a similarly aged population in Australia (19), DM was not identified as a risk for glaucoma. Compared to this study, which only included 3,271 Australian resident participants and a follow-up period of 5 years, our results are more convincing given the larger sample size and longer follow-up period. (More details of longitudinal studies are shown in Table S2).

As discussed, diabetes is a known risk factor for many ocular diseases, such as cataracts and diabetic retinopathy (DR). Given this increased risk, patients with DM may be more motivated to visit an ophthalmologist and receive ocular examinations. This may inflate the association between DM and medical glaucoma. In addition, it is also possible that different stages of DR could result in visual field loss and could lead to overdiagnosis of glaucoma (11,12,35). In addition, the classification method we used simply divided glaucoma into mild and severe diseases. There may be a small number of inaccuracies in our classification of glaucoma grouping. These include drug-uncontrolled cases who have not had surgery yet and some mild glaucoma patients who have been treated with surgery instead of topical glaucoma medications. We have been able to establish the relationship between DM and mild glaucoma, but this was not the case with severe glaucoma. It is possible due to the false negative relationship caused by the sample size in the surgical glaucoma group. In the future, more large cohort studies will be needed to classify glaucoma into different severities, and confirm the relationship between glaucoma severity and DM.

Although the mechanism of the association between DM and glaucoma, especially primary open-angle glaucoma (POAG), is still uncertain, there are several mainstream possible mechanisms, explaining the association between DM and medical glaucoma in our study. DM is associated with an increased IOP which occurs by interrupting the trabecular meshwork function and changing an osmotic gradient, which draws excess aqueous humor into the anterior chamber and remodels the connective tissue of the optic nerve (34). Another explanation is vascular mechanisms. DM causes microvascular damage, which affects the diffusion of oxygen, and may affect vascular autoregulation of the retina and optic nerve (29). A third theory proposes that chronic hyperglycemia and lipid disorders could cause retinal vascular endothelial cell dysfunction and increase the risk of neuronal stress damage (36). In the surgical glaucoma group of our study, the majority of patients were classified as having severe glaucoma and their primary risk factors were age and family history. Poor function of trabecular meshwork and relatively abnormal structure may be the main reason behind this association. If this is the case it provides the opportunity for ophthalmologists to promote blood glucose control in medically-controlled glaucoma patients to reduce the risk of progression. Clinicians may need to shift from purely focusing on diabetes and instead focus on other risk factors including IOP, among the surgical glaucoma patients.

Limitations

Several potential limitations should also be considered in our study. Firstly, the definitions of DM, BMI, smoking, and glaucoma were based on questionnaires, PBS and MBS records, this raises the potential that there may be undiagnosed DM or glaucoma patients which may contribute to bias. We were unable to differentiate between DM type (type 1 diabetes vs. type 2 diabetes) and glaucoma type (primary open-angle vs. angle closure). Despite this, previous reports from a longitudinal Australian population-based study suggest that around 1 in 20 Australians have diabetes, that the majority (89.7%) have type 2 diabetes (37), and the rate of primary angle-closure glaucoma (PACG) is negligible (0.1%) (38,39). Secondly, we are unable to exclude patients who had undergone glaucoma medical treatment or glaucoma-related surgeries before 2004 as records before that time were unavailable. While this may have led to selection bias, the prevalence of pooled glaucoma (estimated by MBS and PBS records) during the follow-up period in our study of 3.9% is comparable with that of the previous study (3.5%) (40) in Australian population. Thirdly, claims data do not include information on relevant clinical parameters such as anti-hyperglycemic medication, ophthalmic examination data (i.e., IOP) laboratory values (i.e., glycosylated hemoglobin), which, ideally, we would have liked to account for. However, in a recent meta-analysis, the association between DM and IOP elevation was weak, suggesting that the association between DM and glaucoma in part may be independent of IOP elevation (35). Fourthly, only the participants treated in private hospitals could be tracked by MBS records. Our analysis did not include glaucoma-related surgeries performed in public hospitals. But the bias caused by patients who received glaucoma-related surgeries in public hospitals might be limited, as private hospitals accounted for 73% of eye procedures (41).

Conclusions

Our study demonstrated that patients with DM had a significantly greater risk of medical glaucoma. We were also able to show that DM was not associated with surgical glaucoma, which might represent severe glaucoma. This positive association between medical glaucoma and DM in the Australian population from the 45 and Up Study, provides an opportunity for improving primary prevention of incident glaucoma.

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