Central Corneal Thickness in a Korean Population: The Namil Study

Abstract

Purpose.: We investigated the distribution of central corneal thickness (CCT), and its association with age, sex, intraocular pressure (IOP), anterior chamber depth (ACD), axial length (AL), and the presence of systemic hypertension and diabetes in a Korean population.

Methods.: Our study is a population-based glaucoma prevalence study of residents aged ≥40 years in Namil-meon area, located in central South Korea. All subjects underwent a complete ophthalmic examination that included CCT measurement with an ultrasonic pachymeter, ACD and AL measurements by optical biometry, and Goldmann applanation tonometry. The right eye of all subjects was analyzed.

Results.: The mean (SD) CCT of the 1259 right eyes was 530.9 (31.5) μm. In univariate analysis, a thicker CCT was associated with a higher IOP (P < 0.001), a longer AL (P = 0.003), and a younger age (P < 0.001). ACD was not correlated significantly with CCT (P = 0.087). Men had a 5.7 μm higher CCT than women (age adjusted, P = 0.001). Subjects with hypertension had a 4.1 μm lower CCT than those without hypertension (age, sex-adjusted, P = 0.027), and the presence of diabetes was not associated significantly with CCT (age, sex-adjusted, P = 0.892). In multivariate analysis, a higher CCT was associated with a higher IOP (P < 0.001), younger age (P = 0.001), male sex (P = 0.005), and the absence of hypertension (P = 0.018).

Conclusions.: The mean CCT of a Korean population was 530.9 μm. CCT was associated with IOP, age, sex, and hypertension.

Introduction

Given that applanation tonometry estimates the intraocular pressure (IOP) by measuring the force required to flatten an area of the cornea, a thinner cornea may lead to underestimation of the true IOP and a thicker cornea may lead to overestimation of the true IOP. ^1–3 In addition, a thinner central corneal measurement is associated with the development of glaucoma, ⁴ increased risk of visual field progression, ⁵ and more advanced glaucoma damage. ⁶ Therefore, central corneal thickness (CCT) measurement has emerged as an important component in glaucoma assessment. ^1–3  

Various factors, including ocular (IOP, refractive error, corneal curvature, anterior chamber depth [ACD], and axial length [AL]) and systemic (age, sex, ethnicity, height, weight, and diabetes) factors, have been reported to affect CCT in the general population. ^7–21 Although several cross-sectional, population-based studies have investigated the distribution of CCT in Asian populations, ^7–21 no such study has been done in Korea to our knowledge. The Namil Study was designed to estimate the prevalence of glaucoma and its associated factors in a large population of Korea. ^22–24 Our study was performed to investigate the distribution of CCT and its association with various ocular and systemic factors in a Korean population, and to compare these results to those of other Asian populations. 

Methods

Our study was performed as a part of the Namil Study, a population-based glaucoma prevalence study of 2027 residents aged ≥40 years in Namil-meon area, a rural agricultural location with an area of 47.14 km² located in central South Korea. ^22–24 Our study was conducted in compliance with the tenets of the Declaration of Helsinki for the use of human subjects in biomedical research. The study was approved by the Institutional Review Board of Chungnam National University Hospital, and written informed consent was obtained from all participants. The details of the Namil Study have been reported previously. ^22–24 In brief, subject screening was performed between November 2007 and February 2008. Notices addressed to all residents in the Namil-meon area encouraged participation in the study. A schedule of 2 screening visits to the screening center was announced to residents. Subjects who did not appear at a designated time were visited at home by a public official and a member of the screening team, who offered a car ride to the screening center. At the end of the screening period, the number of residents was 1928. Of these, 1532 participated in the screening examination, for a response rate of 79.5%. 

Visual acuity was measured using a Landolt broken ring visual acuity chart with correction at a distance of 5 m, and the refractive status was measured by an auto-refractometer (KR-8100; Topcon, Tokyo, Japan). ACD and AL were measured by optical biometry (IOL Master; Carl Zeiss Meditec, Oberkochen, Germany). CCT measurements were obtained from each eye with ultrasound contact-type pachymetry (IOPac; Heidelberg Engineering, Heidelberg, Germany). CCT was measured after calibration following the manufacturer's manual by 2 well-trained examiners. During CCT measurement, subjects were in the sitting position while fixating on a distant target. The average of 3 measurements was recorded. The intra-observer variability as presented by intraclass correlation coefficient was 0.997, which suggests good measurement repeatability. Glaucoma specialists performed slit-lamp biomicroscopy examinations, including van Herick measurements of the ACD, gonioscopy using a Goldmann-type contact lens, and measurement of the IOP with a Goldmann applanation tonometer under topical anesthesia. Binocular optic disc evaluation was performed with a 90-diopter lens on the slit-lamp, and fundus photography was performed with a retinal camera (TRC-NW200; Topcon). The screening visual field test was performed by frequency doubling technology (FDT; N30-1 screening; Humphrey Matrix; Carl Zeiss Meditec, Inc., Dublin, CA). All subjects completed a questionnaire regarding their medical history, including past systemic illnesses, ocular disease, and medication history (i.e., systemic hypertension and diabetes). 

Subjects were suspected of having glaucoma if the FDT screening examination indicated abnormal findings or low test reliability, the Goldmann applanation tonometry revealed an IOP ≥20 mm Hg, and/or optic disc abnormalities were detected (including a vertical cup-to-disc ratio ≥0.6 or a difference in the cup-to-disc ratio between the 2 eyes of ≥0.2) and were referred for a definitive examination to confirm the diagnosis of glaucoma. This analysis consisted of visual field tests with the Humphrey Field Analyzer SITA Standard 30-2 program (HFA II 720i; Carl Zeiss Meditec, Inc.), retinal nerve fiber layer analysis with optical coherence tomography (Stratus OCT; Carl Zeiss Meditec, Inc.), and scanning laser polarimetry (GDx VCC; Carl Zeiss Meditec, Inc.). 

Subjects were diagnosed with primary open angle glaucoma if a glaucomatous visual field defect occurred together with 1 or more of the following in the same baseline phase: a vertical cup-to-disc ratio ≥0.6, a difference in the cup-to-disc ratio ≥0.2 between both eyes, and an IOP >21 mm Hg. In addition, the subject also was required to have open and normal anterior chamber angles without any other abnormalities that could explain the visual field defect. A subject was diagnosed with ocular hypertension if the IOP exceeded 21 mm Hg, there were no glaucomatous visual field defects, and the cup-to-disc ratio was >0.5. A patient with angle-closure was classified into 1 of 3 clinical subtypes, including primary angle-closure suspect, primary angle-closure, and primary angle-closure glaucoma using the definitions reported by the International Society of Geographical and Epidemiological Ophthalmology as described in a previous study. ²³ Subjects were diagnosed with secondary glaucoma or suspected to have glaucoma if they had a history of significant ocular trauma, iridocyclitis, and the presence of new vessels in the iris or chamber angle, or showed other ocular findings that could cause a glaucomatous optic disc or visual field changes. 

The association between primary open angle glaucoma, ocular hypertension, angle-closure, and CCT in our study population has been described previously. ^22,23 Therefore, eyes with glaucoma or ocular hypertension were excluded from our study. In addition, to minimize the effect of intraocular surgery on CCT, eyes with a history of intraocular surgery or corneal refractive surgery also were excluded. Because CCT in the right and left eyes was highly correlated (correlation coefficient = 0.946, P < 0.001), only the right eyes were used for the analysis. 

Statistical Analysis

Linear regression analysis was performed to assess the effect of continuous variables (age, IOP, ACD, and AL) on CCT, using CCT as the dependent variable. Refractive error and AL were correlated significantly (correlation coefficient = −0.600, P < 0.001); therefore, only AL was considered as an independent variable in our study. To identify the effect of binary variables on CCT, differences in CCT between sex and the presence of systemic disease (hypertension and diabetes) were evaluated using the t-test. Analysis of covariance was performed additionally for the adjustment of age and sex. Multivariate analysis was performed for variables that were associated significantly with CCT in univariate analysis. A P value < 0.05 was considered significant. All statistical analyses were performed using SPSS software version 12.0 (SPSS Inc., Chicago, IL). 

Results

Among the 1928 subjects ≥40 years of age in the Namil-meon area who participated in the Namil study, both eyes of 1532 subjects were examined. Of the 1532 right eyes examined, CCT measurements were available for 1504 eyes. In addition, eyes with glaucoma or ocular hypertension and/or history of intraocular surgery or corneal refractive surgery were excluded. Therefore, the remaining 1259 non-glaucomatous right eyes were included in our analysis. The mean (SD) age, refractive error, and IOP of the 1259 subjects was 62.4 (11.4) years (range 40–99), 0.22 (2.00) diopters (range −22.38–7.13), and 13.5 (2.7) mm Hg (range 5–21), respectively. 

The distribution of the CCT values is shown in Figure 1. The mean (SD), median, skewness, and kurtosis of CCT distribution was 530.9 (31.5) μm (range 380–627), 529.0 μm, 0.08, and 0.32, respectively, which suggest a normal distribution. The distributions of CCT for age categories are presented in Table 1. In univariate analysis, a thicker CCT was correlated significantly with a younger age (P < 0.001), higher IOP (P < 0.001), and longer AL (P = 0.003); however, CCT was not associated significantly with ACD (P = 0.087, Table 2). According to the regression line, CCT decreased by 4.0 μm for every decade of life (95% confidence intervals [CI] 2.4–5.5 μm), increased by 2.73 μm for every 1 mm Hg increase in the IOP (95% CI 2.10–3.36 mm Hg), and increased by 2.84 μm for every mm increase in the AL (95% CI 0.98–4.71 μm). When CCTs of male and female subjects were compared, the average CCT in men was 5.8 μm more than that in women (P = 0.001, Table 3). The average CCT in subjects with hypertension was 5.5 μm less than that in those without hypertension (P = 0.007), whereas the presence of diabetes was not associated significantly with CCT (P = 0.493, Table 3). When age and sex were adjusted using the analysis of covariance, the average CCT in men was 5.7 μm more than that in women (age-adjusted, P = 0.001), the average CCT in subjects with hypertension was 4.1 μm less than that in those without hypertension (age and sex-adjusted, P = 0.027), and the presence of diabetes was not associated significantly with CCT (age- and sex-adjusted, P = 0.892, Table 3). 

Figure 1. 

 

Histogram showing the distribution of CCT.

Table 1. 

 

Age- and Sex-Specific CCT, μm

Age Groups, y	Male		Female		Entire Study
	No.	Mean (SD)	No.	Mean (SD)	No.	Mean (SD)
40 to 49	98	537.8 (28.1)	108	542.1 (31.9)	206	540.0 (30.2)
50 to 59	146	537.8 (29.6)	150	530.2 (31.2)	296	534.0 (30.6)
60 to 69	152	531.9 (31.4)	218	527.2 (30.8)	370	529.1 (31.1)
≥70	160	530.8 (32.3)	227	521.5 (31.1)	387	525.3 (31.9)
All age groups	556	534.1 (30.7)	703	528.3 (31.8)	1259	530.9 (31.5)

Table 2. 

 

Univariate and Multivariate Linear Regression Analysis with CCT as a Dependent Variable and Other Ocular and Systemic Factors as Independent Variables

	Univariate		Final Model*
	β	P	β	P	Standardized β
Age	−0.40	< 0.001	−0.26	0.001	−0.09
Sex	5.89	0.001	4.95	0.005	0.08
IOP	2.73	< 0.001	2.56	< 0.001	0.22
ACD	4.37	0.087	−	−	−
AL	2.84	0.003	1.54	0.116	0.05
Hypertension	−5.57	0.007	−4.91	0.018	−0.07
Diabetes	2.17	0.493	−	−	−

*  Adjusted R ² = 0.072, P < 0.001.

Table 3. 

 

Distribution of the CCT according to Sex, and the Presence of Hypertension and Diabetes

	Unadjusted	P Value*	Adjusted	P Value†
Sex
Male, n = 556	534.1 (30.7)	0.001	534.1 (1.3)‡	0.001
Female, n = 703	528.3 (31.8)		528.4 (1.2)‡
Hypertension
Yes, n = 310	526.7 (30.0)	0.007	527.9 (1.6)§	0.027
No, n = 949	532.2 (31.8)		532.0 (1.0)§
Diabetes
Yes, n = 108	532.8 (30.7)	0.493	530.5 (2.7)§	0.892
No, n = 1151	530.7 (31.5)		530.9 (0.9)§

 Values are presented as mean (SD) for unadjusted values and mean (SE) for adjusted values, in μm.

*   t-test.

†  Analysis of covariance.

‡  Adjusted for age.

§  Adjusted for age and sex.

In multivariate analysis, when age, IOP, AL, sex, and the presence of hypertension were considered as independent variables and CCT was considered as a dependent variable, a higher CCT was associated with a higher IOP (P < 0.001, 2.56-μm increase in CCT for every 1 mm Hg increase in the IOP [95% CI 1.92–3.20 μm]), younger age (P = 0.001, 2.6-μm decrease in the CCT for every decade of life [95% CI 1.0–4.1 μm]), male sex (P = 0.005), and the absence of hypertension (P = 0.018, Table 2). 

When the effect of CCT on IOP was analyzed separately, IOP increased by 2.0 mm Hg for every 100-μm increase in the CCT (P < 0.001, 95% CI 1.5–2.5 mm Hg, Fig. 2). When a multivariate analysis was performed additionally to identify the effect of CCT on IOP after adjustment for age, sex, and the presence of hypertension, ²⁴ IOP increased by 1.9 mm Hg for every 100-μm increase in the CCT (P < 0.001, 95% CI 1.4–2.3 mm Hg). 

Figure 2. 

 

Scattergram showing the relationship between CCT and IOP (IOP = 2.77 + 0.02 mm Hg × CCT, r ² = 0.06, P < 0.001).

Discussion

In our study, the mean CCT measured by ultrasound pachymetry in a Korean population was 530.9 μm, and the skewness and kurtosis of CCT distribution was 0.08 and 0.32, respectively, which suggest a normal distribution. According to previous population-based studies, the mean CCT of an Asian population ranged from 485.7 to 561.4 μm, as summarized in Table 4. ^7–21 When only studies using ultrasound pachymetry were considered, the mean CCT was 539.1 μm for Nepalese, ⁸ 521.9 μm for Burmese, ⁹ 541.2 μm for Singaporean Malay, ¹¹ 541.5 μm for Singaporean, ¹² 541.5 μm for southern Chinese, ¹⁴ 527.6 μm for northern Chinese, ¹⁵ 511.4 μm for southern Indian, ¹⁸ and 514 μm for central Indian ²⁰ populations. Although the comparison of CCTs among studies is confounded by the variation in the characteristics of each study population and frequency of the ultrasound pachymetry, these results suggest that Nepalese, Singaporean Malay, Singaporean, and southern Chinese populations may have, on an average, thicker central corneas than Koreans, whereas Burmese, northern Chinese, and Indians may have, on an average, thinner central corneas than Koreans. Differences in ocular and systemic factors among various studies may account for the variations in reported CCT among the studies. 

Table 4. 

 

Mean (SD, μm) Values and Associated Factors with CCT in Various Population-Based Studies in Asian Populations

Study	Ethnicity	Age	Method	Mean (SD)	Age	Sex	Diabetes	Hypertension	IOP	ACD	AL
Mongolian7	Mongolian	50+	Optical	485.7	+	−	NA	NA	+	NA	NA
Bhaktapur Glaucoma Study8	Nepalese	40+	Ultrasound	539.1 (33.7)	+	−	NA	NA	+	NA	NA
Meiktila Eye Study9	Burmese	40+	Ultrasound	521.9 (33.3)	−	−	NA	NA	+	NA	NA
Tanjong Pagar10	Singaporean Chinese	40+	Optical	539 (32)	+	−	NA	NA	+	NA	NA
Singapore Malay Eye Study11	Singaporean Malay	40+	Ultrasound	541.2 (38.1)	+	−	+	NA	+	NA	+
SiMES Study12	Singaporean	40+	Ultrasound	541.5	NA	NA	NA	NA	+	NA	NA
Beijing Eye Study13	Chinese	40+	AS-OCT	556.2 (33.1)	−	+	NA	NA	+	NA	NA
Liwan Eye Study14	Southern Chinese	50+	Ultrasound	541.5 (31.4)	+	−	+	−	+	NA	−
Liwan Eye Study14	Southern Chinese	50+	Optical	511.6 (29.0)	+	−	−	−	+	NA	−
Northern China15	Northern Chinese	40+	Ultrasound	527.6 (29.5)	NA	NA	NA	NA	NA	NA	NA
Tajimi Study16	Japanese	40+	Specular	517.5 (29.8)	−	+	NA	NA	+	NA	NA
Kumejima Study17	Japanese	40+	Specular	510 (34)	NA	NA	NA	NA	+	NA	NA
Funagata Study21	Japanese	35+	Specular	544.7 (34.6)	−	−	+	−	+	NA	NA
Chennai Glaucoma Study18	Southern Indian	40+	Ultrasound	511.4 (33.5)	+	+	NA	NA	+	NA	NA
Singapore Indian Eye Study19	Singaporean Indian	40+	AS-OCT	561.4 (34.1)	NA	NA	NA	NA	NA	NA	NA
Central India Eye and Medical Study20	Central Indian	30+	Ultrasound	514 (33)	+	+	NA	NA	+	+	−
Namil Study (present study)	Korean	40+	Ultrasound	530.9 (31.5)	+	+	−	+	+	−	−

 AS-OCT, anterior segment optical coherence tomography; NA, not applicable; +, significant association; −, non-significant association.

Goldmann applanation tonometry, which is the current gold standard IOP measurement technique, estimates IOP by measuring the force required to flatten an area of the cornea. Therefore, corneal characteristics, including its thickness ^1–3 or hysteresis, ^25,26 can affect IOP measurement. With regard to the correlation between IOP and CCT, all previous studies reported a higher IOP with a greater CCT, ^{7–14,16–18,20,21} which confirms the importance of CCT for the proper assessment of IOP (Table 4). In previous studies in Asian populations, the IOP increase ranged from 1.0–3.6 mm Hg for each 100-μm CCT increase, ^{7–10,12–14,17,18,20,21} which is in agreement with the results of our study (1.9 mm Hg IOP change per 100 μm CCT change after adjustment for age, sex, and the presence of hypertension). 

Older age was associated with a thinner central cornea in our study population, which is in line with previous studies in Asian populations (Table 4). ^{7,8,10,11,14,18,20} A 2.6-μm/decade decrease in CCT with age was estimated using the regression line in our study after adjusting for sex, IOP, AL, and the presence of hypertension; the amount of CCT decrease ranged from 2.4 to 6.8 μm/decade in other studies with Asian populations. ^{7,8,10,11,14,18} Possible explanations for the thinning of the central cornea with aging include a decline in the density of keratocytes and a breakdown of corneal collagen fibers with age, as well as longer exposure to the environment. ^27,28 Another possible explanation is the effect of nutrition on CCT. Older populations of our time experienced world wars and suffered nutritional deficiencies, and this probably is similar to a cohort effect and may not be a physiologic thinning with age. Our study was cross-sectionally performed; to investigate the effect of age on CCT, longitudinal studies are needed. 

Regarding the effect of sex on CCT, previous studies reported that men have thicker central corneas than women; ^13,16,18,20 other studies did not find such a difference (Table 4). ^7–11,14,21 In our study, the CCT of male subjects was significantly thicker than that of female subjects. Given that CCT is correlated with body mass index ^11,14,20,21 or weight, ¹⁶ greater body mass index or weight in men than in women may contribute partly to this finding. However, body height and weight were not analyzed in our study; therefore, the cause of the discrepancy in CCT between men and women remains unclear in our study population. 

Another interesting finding of our study is the association between the presence of systemic hypertension and a thinner central cornea. In contrast to these results, other Asian population-based studies showed no significant correlation between CCT and blood pressure ^11,16 or the presence of hypertension (Table 4). ^14,21 The underlying mechanism for the correlation between hypertension and CCT remains unclear. Our findings between hypertension and thinner central corneas were consistent after age and sex adjustment, which suggests that hypertension per se or blood pressure-lowering medication may affect CCT. In a previous study with the same study population used in the present report, the presence of hypertension was associated significantly with a higher IOP. ²⁴ A thinner CCT and a higher IOP in subjects with hypertension may suggest a possible role of hypertension as a real risk factor rather than a confounder for ocular hypertension or glaucoma. 

In previous studies, the presence of abnormal glaucoma metabolism and/or diabetes was associated with a thicker central cornea (Table 4). ^11,14,21 This may be due to an osmotic gradient drawing fluid into the corneal stroma or a tendency among diabetics for corneal collagen to develop disulphide cross-linking. ²⁹ In our study, no significant difference was found in CCT between subjects with and without diabetes. Difference in distribution and severity of diabetes among study populations may account for this discrepancy. 

It has been reported that a thicker central cornea is associated with a greater radius of corneal curvature, which in turn is correlated with a greater AL. ^11,16,30 Therefore, an eye with a greater AL may have a thicker central cornea. ¹¹ In our study, a greater AL was associated significantly with a thicker central cornea in a univariate analysis, whereas the association was no longer significant in a multivariate analysis; this is in agreement with previous study results. ^14,20 Corneal curvature was not investigated in our study; therefore, the cause of this discrepancy remains unclear. 

There are some limitations of our study, including its cross-sectional study design, identifying systemic disease based on the information provided by the subjects without measuring blood pressure and blood glucose level, and the lack of analysis of other confounding factors, such as corneal curvature, body height, and weight. In our study, although age, sex, IOP, hypertension, and CCT were correlated significantly, the associations were not strong, which is in agreement with previous study results. ^7–21 Therefore, it remains unclear how these associations will affect glaucoma evaluation in a clinical setting. 

In conclusion, the mean CCT of a Korean population was 530.9 μm, and was associated with IOP, age, sex, and hypertension. These findings would be helpful for the evaluation of glaucoma in this population. 

Footnotes

 Supported by Alcon Korea, Merck Korea, Pfizer Korea, Taejoon Pharmaceutical, Zeiss Korea, and the Korean Ophthalmological Society. The sponsors did not participate in the design and conduct of the study; in the collection, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript. At least 1 author who is independent of any commercial funder had full access to all data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors have no financial or proprietary interest in any of the materials or methods mentioned. The authors alone are responsible for the content and writing of the paper.

Footnotes

²  These authors contributed equally to the work presented here and should therefore be regarded as equivalent authors.

Footnotes

³  See the Appendix for the members of the Namil Study Group, Korean Glaucoma Society.

Footnotes

 Disclosure: Y.H. Hwang, None; H.K. Kim, None; Y.H. Sohn, None

Appendix

The Namil Study Group, Korean Glaucoma Society

Byung-Heon Ahn, Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine 

Myung Douk Ahn, Department of Ophthalmology, College of Medicine, The Catholic University of Korea 

Nam Ho Baek, Saevit Eye Hospital 

Kyu-Ryong Choi, Department of Ophthalmology, Ewha Womans University School of Medicine 

Seung-Joo Ha, Department of Ophthalmology, Soonchunghyang University College of Medicine 

Gyu-Heon Han, Doctor Lee's Eye Clinic 

Young Jae Hong, Nune Eye Hospital 

Ja-Heon Kang, Department of Ophthalmology, Kyung Hee University College of Medicine 

Changwon Kee, Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University 

School of Medicine 

Hong-Seok Kee, Leeyeon Eye Clinic 

Chang-Sik Kim, Department of Ophthalmology, College of Medicine, Chungnam National University 

Chan Yun Kim, Department of Ophthalmology, Yonsei University College of Medicine 

Hwang-Ki Kim, Department of Ophthalmology, Konyang University College of Medicine, Kim's Eye Hospital 

Joon-Mo Kim, Department of Ophthalmology, Sungkyunkwan University School of Medicine, Kangbuk Samsung Hospital 

Seok-Hwan Kim, Department of Ophthalmology, Seoul National University College of Medicine 

Tae-Woo Kim, Department of Ophthalmology, Seoul National University College of Medicine 

Yong Yeon Kim, Department of Ophthalmology, Korea University College of Medicine 

Michel Scott Kook, Department of Ophthalmology, University of Ulsan, College of Medicine, Asan Medical Center 

Joo-Hwa Lee, Department of Ophthalmology, Sanggye-Paik Hospital, Inje University Medical College 

Kyung-Wha Lee, Department of Ophthalmology, Hallym University College of Medicine 

Seung-Hyuck Lee, Yonsei Plus Eye Center 

Jung-Il Moon, Department of Ophthalmology, College of Medicine, The Catholic University of Korea 

Chan Kee Park, Department of Ophthalmology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea 

Hyun Joon Park, Merit Eye Clinic 

Ki Ho Park, Department of Ophthalmology, Seoul National University College of Medicine 

Gong Je Seong, Department of Ophthalmology, Yonsei University College of Medicine 

Yong Ho Sohn, Department of Ophthalmology, Konyang University College of Medicine, Kim's Eye Hospital 

Ki-Bang Uhm, Department of Ophthalmology, Hanyang University College of Medicine