October 2014
Volume 55, Issue 10
Free
Clinical and Epidemiologic Research  |   October 2014
A Longitudinal Study of Age-Related Changes in Intraocular Pressure: The Kangbuk Samsung Health Study
Author Affiliations & Notes
  • Di Zhao
    Department of Epidemiology and Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, United States
  • Myung Hun Kim
    Department of Epidemiology, Graduate School of Public Health, Seoul National University, Seoul, Korea
  • Roberto Pastor-Barriuso
    National Center for Epidemiology, Carlos III Institute of Health and Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • Yoosoo Chang
    Department of Epidemiology and Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, United States
    Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea
    Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea
  • Seungho Ryu
    Department of Epidemiology and Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, United States
    Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea
    Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea
  • Yiyi Zhang
    Department of Epidemiology and Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, United States
  • Sanjay Rampal
    Department of Epidemiology and Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, United States
    Department of Social and Preventive Medicine, Julius Centre University of Malaya, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
  • Hocheol Shin
    Department of Family Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea
  • Joon Mo Kim
    Department of Ophthalmology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea
  • David S. Friedman
    The Wilmer Eye Institute, Johns Hopkins Hospital, Baltimore, Maryland, United States
  • Eliseo Guallar
    Department of Epidemiology and Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, United States
  • Juhee Cho
    Department of Epidemiology and Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, United States
    Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea
    Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
    Biostatistics and Clinical Epidemiology Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
  • Correspondence: Juhee Cho, Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, 50 Irwon-dong, Gangnam-gu, Seoul 135-710, South Korea; jcho@skku.edu.  
  • Joon Mo Kim, Department of Ophthalmology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 108 Pyoung-dong, Jongro-gu, Seoul 110-746, South Korea; kjoonmo@skku.edu
Investigative Ophthalmology & Visual Science October 2014, Vol.55, 6244-6250. doi:10.1167/iovs.14-14151
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      Di Zhao, Myung Hun Kim, Roberto Pastor-Barriuso, Yoosoo Chang, Seungho Ryu, Yiyi Zhang, Sanjay Rampal, Hocheol Shin, Joon Mo Kim, David S. Friedman, Eliseo Guallar, Juhee Cho; A Longitudinal Study of Age-Related Changes in Intraocular Pressure: The Kangbuk Samsung Health Study. Invest. Ophthalmol. Vis. Sci. 2014;55(10):6244-6250. doi: 10.1167/iovs.14-14151.

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Abstract

Purpose.: To examine the longitudinal association between age and intraocular pressure (IOP) in a large sample of Korean men and women.

Methods.: We conducted a prospective cohort study of 274,064 young and middle-aged Korean adults with normal fundoscopic findings, following them from January 1, 2002, to February 28, 2010. Health exams were scheduled annually or biennially. At each visit, IOP was measured in both eyes using automated noncontact tonometers. The longitudinal change in IOP with age was evaluated using three-level mixed models for longitudinal paired-eye data, accounting for correlations between paired eyes and repeated measurements over time.

Results.: In fully adjusted models, the average longitudinal change in IOP per 1-year increase in age was −0.065 mm Hg (95% confidence interval [CI] −0.068 to −0.063), with marked sex differences (P < 0.001). In men, the average annual IOP change was −0.093 mm Hg (95% CI −0.096 to −0.091) throughout follow-up. In women, the average annual IOP change was −0.006 mm Hg (95% CI −0.010 to −0.003), with a relatively flat association in the age range of 30 to 59 years and more marked annual decreases at younger and older ages.

Conclusions.: Intraocular pressure was inversely associated with age in a large cohort of Korean adults attending health-screening visits. For men, this inverse association was observed throughout the entire age range, while for women it was evident only in younger (<30 years of age) and older (≥60 years of age) women, with no association in women aged 30 to 59. Further research is needed to better understand the underlying mechanisms and to reconsider cutoffs for defining high IOP by age and sex groups in Asian populations.

Introduction
Elevated intraocular pressure (IOP) is a major risk factor for the development of primary open-angle glaucoma, 1 and even in normal tension glaucoma the reduction of IOP may slow the progression of visual field loss. 2 Population-based studies of prevalence and incidence of glaucoma consistently show a steady increase with age. 39 Consistent with this finding, cross-sectional and longitudinal studies have shown that IOP increases with age in Western populations. 2,1014  
In the 1980s, Shiose and Kawase reported an inverse association between age and IOP in a Japanese population 15,16 and suggested that these results reflect ethnic differences or environmental effects. Several cross-sectional studies have confirmed these findings in Japanese 17,18 and in other East Asian populations, 1921 but the only two longitudinal prospective studies have been inconsistent. Nomura et al. 22 reported that IOP significantly increased with age in Japanese men and women, while Nakano et al. 23 reported that IOP decreased with age in young and middle-aged Japanese men. The inconsistencies of these two studies could be because of the difference in study populations. Nomura et al. 22 examined the association in 69,643 Japanese office workers and their family members, while Nakano et al. 23 selected participants from 2987 Japanese male aircraft crew members. The inconsistencies also may be due to the methodological issues related to the analysis of the longitudinal IOP trajectories as Nomura et al. 22 used a mixed-effects model for longitudinal analysis and Nakano et al. 23 individually calculated coefficients for 11 measurement points using linear regression. 
Since cross-sectional studies do not provide estimates of within-subject IOP trajectories, we conducted a longitudinal cohort study to evaluate the influence of age on IOP in a large sample of healthy Korean men and women attending regular health-screening visits. 
Methods
Study Design and Population
The Kangbuk Samsung Health Study is a longitudinal cohort study of 281,238 adult Korean men and women who underwent comprehensive screening health examinations at the two Kangbuk Samsung Hospital Health Screening Centers in Seoul and Suwon, South Korea, from January 1, 2002, to February 28, 2010. Over 80% of the participants were employees of various companies or local government organizations who took employer-paid annual or biennial health-screening exams required by the Korean Industrial Safety and Health Law and the employees' spouses. The remaining participants voluntarily purchased screening exams at the health exam center. 
The present analysis included 280,911 study participants with valid IOP readings in at least one screening visit between January 1, 2002, and February 28, 2010 (the total number of visits was 604,416). We excluded the following: 9225 visits after participants developed an absolute difference in IOP between both eyes greater than 6 mm Hg, as this is a marker of high risk of glaucoma 24 ; 1652 visits with a missing fundus photograph; 15,458 visits after participants developed abnormal findings in fundus photographs; and 100 visits for participants with missing IOP measurements at all visits. Thus, the final sample included 274,064 participants (119,723 women and 154,341 men) free of eye disease with a total of 577,981 screening visits. 
This study was approved by the Institutional Ethics Committee of the Kangbuk Samsung Hospital. The Ethics Committee waived the requirement of informed consent as we only used deidentified data routinely collected during health-screening visits. 
Measurements
Health exams were scheduled every 2 years for participants younger than 40 years of age and every year for participants 40 years of age or older. At each visit, IOP was measured in both eyes with automated noncontact tonometers (2002–2004: TX-10; Canon, Tokyo, Japan; 2005–2008: TX-F; Topcon, Itabashi, Tokyo, Japan; 2009 onward: CT-80; Topcon). Extreme IOP readings below 5 mm Hg (0.02%) or above 30 mm Hg (0.16%) were discarded because of the potential for measurement error. The time of registration at the exam center was within 2 hours of IOP measurement and hence was used as the approximate IOP measurement time (classified into morning or afternoon). Fundus photographs were taken with a nonmydriatic fundus camera (CR6-45NM; Canon). 
Data for demographic characteristics, smoking status, alcohol consumption, physical activity, medical history, and medication use were collected by a standardized self-administered questionnaire. Smoking status was categorized into never, former, or current smoking; frequency of current alcohol consumption was categorized into <1, 1 to 3, or >3 d/wk; and frequency of vigorous physical activity was categorized into none, 1 to 3, or >3 times/wk. Height and weight were measured with the participants wearing a lightweight hospital gown and no shoes. Body mass index was calculated as weight in kilograms divided by height in meters squared. Sitting blood pressure and heart rate were measured by trained nurses. Hypertension was defined as a systolic blood pressure ≥ 140 mm Hg, a diastolic blood pressure ≥ 90 mm Hg, a self-reported history of hypertension, or current use of antihypertensive medications. 
Serum glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides were measured in fasting blood samples collected after at least 12 hours of fasting. Diabetes mellitus was defined as a fasting serum glucose ≥ 126 mg/dL, a self-reported history of diabetes, or current use of antidiabetic medications. Dyslipidemia was defined as total cholesterol ≥ 240 mg/dL, HDL cholesterol < 40 mg/dL in men and < 50 mg/dL in women, serum triglycerides ≥ 150 mg/dL, report of a previous diagnosis, or current use of lipid-lowering medications. 
Statistical Analysis
The primary objective of this study was to assess the longitudinal change in IOP with age. To account for correlations in IOP measurements arising from both paired eyes and repeated measurements over time in the same participant, the main analyses consisted of three-level linear mixed models for longitudinal paired-eye data. 25,26 Details of the models are provided in the supplementary statistical material. Briefly, we modeled linear trajectories in IOP with age for each eye at the first level, variations in IOP trajectories between both eyes of the same participant at the second level, and variations in IOP trajectories across participants at the third level. These mixed models provided the average longitudinal change in IOP per 1-year increase in age, while they allowed for random variations in longitudinal changes among participants and between participants' eyes according to normal distributions with unstructured variance-covariance matrices. For comparison with previous studies, we also evaluated the cross-sectional association between age and IOP by using random-intercept linear models for paired-eye data from the baseline visit, which estimated the average cross-sectional difference in baseline IOP per 1-year increase in baseline age. Details of cross-sectional analyses are also provided in the supplementary material. 
To adjust for confounding and to evaluate potential mediating factors, we used three models with increasing degrees of adjustment. The first model was crude. The second model adjusted for sex, study center (Seoul or Suwon), and height (continuous) as time-constant covariates, as well as for IOP measurement time (morning or afternoon), smoking status (never, former, or current), alcohol drinking (<1, 1–3, or >3 d/wk), and physical activity (none, 1–3, or >3 times/wk) as time-varying covariates. The third model further included potential mediators of the effect of age as time-varying covariates, including heart rate (continuous), body mass index (continuous), hypertension (no or yes), diabetes (no or yes), and dyslipidemia (no or yes). 
We accommodated distinct linear IOP trajectories in age intervals < 30, 30 to 39, 40 to 49, 50 to 59, and ≥60 years by extending the above mixed models with fixed-effects linear spline terms for age at follow-up with knots at 30, 40, 50, and 60 years. 27 Smooth longitudinal trends in IOP with age were also obtained by adding fixed-effects restricted quadratic spline terms for age with the same knots described above. To evaluate potential heterogeneity of IOP trajectories by sex, interactions of sex and age were included as fixed effects in the corresponding mixed models. We conducted sensitivity analyses without excluding participants with abnormal fundoscopy findings or with between-eye differences in IOP greater than 6 mm Hg (280,911 participants with 604,416 visits). In addition, we performed a sensitivity analysis restricted to participants with two or more screening visits (130,991 participants with 435,262 visits). All reported P values were two-sided, and the significance level was set at 0.05. Statistical analyses were undertaken using Stata (version 12; Stata Corp., College Station, TX, USA). 
Results
The mean (SD) age and IOP of study participants at baseline were 40.2 (9.9) years and 13.6 (2.5) mm Hg, respectively (Table 1). Overall, women had lower mean baseline IOP levels than men, although the difference in baseline IOP levels between sexes decreased among participants older than 50 years of age. Compared with men, women were less likely to smoke, drink alcohol, and exercise and to have hypertension, diabetes, and dyslipidemia at baseline. Women also had lower mean baseline body mass index and higher heart rate. Participants with two or more screening visits (n = 133,651) were generally younger; more educated; healthier; less likely to have diabetes, hypertension, or dyslipidemia; and more likely to have a higher IOP compared with participants with only a single screening visit. 
Table 1
 
Participants' Characteristics at Baseline*
Table 1
 
Participants' Characteristics at Baseline*
Characteristic Overall Female Male P Value
Participants 274,064 119,723 (43.7) 154,341 (56.3)
Age, y 40.2 (9.9) 40.5 (10.3) 39.8 (9.5) <0.001
Study center <0.001
 Seoul 184,957 (67.5) 81,423 (68.0) 103,534 (67.1)
 Suwon 89,107 (32.5) 38,300 (32.0) 50,807 (32.9)
Height, cm 166.2 (8.5) 159.1 (5.5) 171.8 (5.9) <0.001
Smoking status <0.001
 Never 147,039 (54.6) 105,391 (90.8) 41,648 (27.2)
 Former 45,506 (16.9) 4,147 (3.6) 41,359 (27.0)
 Current 76,582 (28.5) 6,497 (5.6) 70,085 (45.8)
Alcohol drinking, d/wk <0.001
  <1 172,819 (64.0) 101,902 (86.8) 70,917 (46.4)
 1–3 71,097 (26.3) 12,732 (10.8) 58,365 (38.2)
  >3 26,319 (9.7) 2,824 (2.4) 23,495 (15.4)
Physical activity, times/wk <0.001
 0 147,688 (54.6) 74,984 (63.5) 72,704 (47.6)
 1–3 77,994 (28.8) 23,647 (20.0) 54,347 (35.6)
 >3 45,005 (16.6) 19,427 (16.5) 25,578 (16.8)
Heart rate, beats/min 67.2 (9.3) 68.3 (9.2) 66.4 (9.3) <0.001
Body mass index, kg/m2 23.5 (3.1) 22.4 (3.1) 24.4 (2.9) <0.001
Hypertension 47,671 (17.4) 14,501 (12.1) 33,170 (21.5) <0.001
Diabetes 10,763 (3.9) 3,416 (2.9) 7,347 (4.8) <0.001
Dyslipidemia 115,731 (42.2) 41,852 (35.0) 73,879 (47.9) <0.001
Intraocular pressure, mm Hg‡ 13.58 (2.66) 13.10 (2.65) 13.95 (2.61) <0.001
 <30 y 13.55 (2.71) 13.10 (2.71) 13.99 (2.64) <0.001
 30–39 y 13.58 (2.70) 12.94 (2.66) 14.05 (2.64) <0.001
 40–49 y 13.62 (2.60) 13.13 (2.58) 13.97 (2.55) <0.001
 50–59 y 13.59 (2.57) 13.52 (2.63) 13.66 (2.51) <0.001
 ≥60 y 13.34 (2.62) 13.37 (2.67) 13.30 (2.57) 0.13
In longitudinal analyses, age showed a significant inverse association with IOP (Table 2). In fully adjusted models, the average longitudinal decrease in IOP for each 1-year increase in age was −0.065 mm Hg (95% confidence interval [CI] −0.068 to −0.063 mm Hg). The longitudinal downward trend in IOP with increasing age, however, was markedly different between sexes and across age intervals (all P values < 0.001). For men, the average annual IOP change was −0.093 mm Hg (95% CI −0.096 to −0.091 mm Hg) over the entire age range, and it varied from −0.142 to −0.080 mm Hg across the different age intervals. For women, the average annual IOP change was −0.006 mm Hg (95% CI −0.010 to −0.003 mm Hg) over the entire age range, with a relatively flat association between 30 and 59 years of age and more marked annual decreases of −0.167 and −0.076 mm Hg at younger and older ages, respectively. The longitudinal decrease in IOP with age and the differences between sexes were also evident in restricted quadratic spline models, which confirmed the homogeneous linear decline in men and the weaker nonlinear association in women (Fig.). Sensitivity analyses restricting the study population to participants with at least two screening visits did not materially affect the result. Additional analyses without excluding participants with abnormal fundoscopy findings or with between-eye differences in IOP greater than 6 mm Hg also yielded similar results (data not shown). 
Table 2
 
Longitudinal Changes in Intraocular Pressure per 1-Year Increase in Age, Overall and by Age Interval*
Table 2
 
Longitudinal Changes in Intraocular Pressure per 1-Year Increase in Age, Overall and by Age Interval*
Overall Age Interval, y P Value
<30 30–39 40–49 50–59 ≥60
No. subjects/visits
 Overall 274,064/577,785 23,021/26,033 147,849/268,588 92,650/204,678 36,318/56,144 16,583/22,342
 Female 119,723/224,085 11,282/13,073 61,806/105,754 36,796/71,273 17,292/23,717 8,106/10,268
 Male 154,341/353,700 11,739/12,960 86,043/162,834 55,854/133,405 19,026/32,427 8,477/12,074
Model 1,‡ mm Hg/y
 Overall −0.058
(−0.060 to −0.056)
−0.128
(−0.141 to −0.115)
−0.043
(−0.046 to −0.041)
−0.073
(−0.076 to −0.069)
−0.065
(−0.071 to −0.060)
−0.104
(−0.112 to −0.095)
<0.001
 Female 0.000
(−0.003 to 0.004)
−0.160
(−0.177 to −0.144)
0.005
(0.001 to 0.010)
0.004
(−0.001 to 0.009)
0.009
(0.000 to 0.017)
−0.060
(−0.073 to −0.048)
<0.001
 Male −0.088
(−0.091 to −0.086)
−0.118
(−0.138 to −0.098)
−0.070
(−0.074 to −0.067)
−0.105
(−0.109 to −0.101)
−0.103
(−0.110 to −0.096)
−0.124
(−0.135 to −0.112)
<0.001
 P value§ <0.001 0.001 <0.001 <0.001 <0.001 <0.001
Model 2‖, mm Hg/y
 Overall −0.059
(−0.061 to −0.057)
−0.161
(−0.173 to −0.148)
−0.046
(−0.049 to −0.043)
−0.070
(−0.073 to −0.067)
−0.061
(−0.067 to −0.056)
−0.108
(−0.117 to −0.099)
0.001
 Female 0.001
(−0.003 to 0.005)
−0.170
(−0.186 to −0.153)
0.007
(0.002 to 0.011)
0.006
(0.001 to 0.012)
0.007
(−0.001 to 0.016)
−0.065
(−0.079 to −0.052)
<0.001
 Male −0.087
(−0.090 to −0.085)
−0.127
(−0.148 to −0.107)
−0.071
(−0.074 to −0.067)
−0.103
(−0.106 to −0.099)
−0.103
(−0.110 to −0.095)
−0.120
(−0.132 to −0.109)
<0.001
 P value§ <0.001 0.002 <0.001 <0.001 <0.001 <0.001
Model 3,¶ mm Hg/y
 Overall −0.065
(−0.068 to −0.063)
−0.166
(−0.179 to −0.154)
−0.054
(−0.056 to −0.051)
−0.074
(−0.077 to −0.071)
−0.073
(−0.078 to −0.067)
−0.112
(−0.121 to −0.103)
<0.001
 Female −0.006
(−0.010 to −0.003)
−0.167
(−0.184 to −0.151)
0.001
(−0.004 to 0.005)
−0.001
(−0.007 to 0.004)
−0.012
(−0.021 to −0.004)
−0.076
(−0.089 to −0.063)
<0.001
 Male −0.093
(−0.096 to −0.091)
−0.142
(−0.162 to −0.122)
−0.080
(−0.083 to −0.076)
−0.105
(−0.109 to −0.101)
−0.108
(−0.115 to −0.101)
−0.121
(−0.133 to −0.110)
<0.001
 P value§ <0.001 0.06 <0.001 <0.001 <0.001 <0.001
Figure
 
Longitudinal trend in intraocular pressure by age at follow-up. Curves represent adjusted average intraocular pressures (solid lines) and their 95% confidence intervals (dashed lines) based on restricted quadratic splines with knots at 30, 40, 50, and 60 years of age. Results were obtained from linear mixed models with interactions between spline terms and sex and random variations in age trends among participants and between eyes within participants, and they were adjusted for study center (Seoul or Suwon), height (continuous), and time-varying changes in intraocular pressure measurement time (morning or afternoon), smoking status (never, former, or current), alcohol drinking (<1, 1–3, or >3 d/wk), physical activity (none, 1–3, or >3 times/wk), heart rate (continuous), body mass index (continuous), hypertension (no or yes), diabetes (no or yes), and dyslipidemia (no or yes). The histogram represents the age distribution of person-visits among males (shaded bars) and females (white bars).
Figure
 
Longitudinal trend in intraocular pressure by age at follow-up. Curves represent adjusted average intraocular pressures (solid lines) and their 95% confidence intervals (dashed lines) based on restricted quadratic splines with knots at 30, 40, 50, and 60 years of age. Results were obtained from linear mixed models with interactions between spline terms and sex and random variations in age trends among participants and between eyes within participants, and they were adjusted for study center (Seoul or Suwon), height (continuous), and time-varying changes in intraocular pressure measurement time (morning or afternoon), smoking status (never, former, or current), alcohol drinking (<1, 1–3, or >3 d/wk), physical activity (none, 1–3, or >3 times/wk), heart rate (continuous), body mass index (continuous), hypertension (no or yes), diabetes (no or yes), and dyslipidemia (no or yes). The histogram represents the age distribution of person-visits among males (shaded bars) and females (white bars).
For comparison with previous studies, we also estimated the cross-sectional association between age and IOP at baseline. In cross-sectional analyses, age was also significantly inversely associated with IOP, although this baseline association was weaker than the longitudinal relationship (Supplementary Table S1; Supplementary Fig. S1). The average cross-sectional differences in baseline IOP per 1-year increase in baseline age were −0.023 mm Hg (95% CI −0.024 to −0.022 mm Hg) overall, −0.036 mm Hg (95% CI −0.037 to −0.034 mm Hg) in men, and −0.008 mm Hg (95% CI −0.010 to −0.007 mm Hg) in women. 
Discussion
In this large cohort of Korean adults, IOP decreased with age, but the decline was stronger in men compared with women and in participants < 30 years of age compared with older participants. Cross-sectional associations between age and IOP followed a similar pattern, but underestimated the magnitude of the longitudinal association. The large sample size, the wide age range, the availability of repeated IOP measurements in both eyes in study participants, and the use of longitudinal analyses that consider the trajectories of individual eyes in each participant add to the strength of our findings. 
The inverse longitudinal and cross-sectional associations between age and IOP in our study are compatible with other cross-sectional 1619,21,28,29 and longitudinal studies 23 conducted in Asian populations, with the exception of a longitudinal study in Japan. 22 On the contrary, most cross-sectional 2,10,3033 and longitudinal studies 3436 in Western populations showed a positive association between age and IOP, although some studies showed no 11,37,38 or inverse associations. 39,40 Aging is associated both with reduced production of aqueous humor, 41 which leads to a reduction of IOP, and with structural changes in the trabecular meshwork, which increase the resistance to aqueous humor outflow, increasing IOP. 42 The net change in IOP may be determined by the balance between these processes, which may differ in Western and Asian populations. 
The mechanisms for the differences in the association of age and IOP between Western and Asian populations are unclear. Lifestyle factors and environmental exposures have been proposed, 22,29,35 but no single responsible factor has been clearly identified. Anatomic eye features linked to IOP (iris color, central corneal thickness, anterior chamber depth, etc.) could have different effects in Asian compared with Western subjects, 23,31,39 but additional research is needed to understand the role of anatomic eye differences in IOP trajectories. Finally, the decrease in IOP with age in Asian populations has been ascribed to methodological factors such as selection bias due to nonparticipation in cross-sectional studies, or drop out in longitudinal studies of elderly subjects with higher IOP and higher comorbidities, 22 or to cohort effects with younger individuals adopting Western lifestyles. In our study, however, we observed inverse associations between age and IOP even among participants < 30 years of age who are unlikely to be subject to selection bias due to major comorbidities. In addition, the decrease in IOP with advancing age was observed across all age groups in the longitudinal analyses, with no clear cohort effects. 
Sex-related differences in the distribution of IOP and its changes with age have also been inconsistent across studies. In our study, women had a lower IOP compared with men, a pattern also reported in the Egna-Neumarkt 2 and the Gutenberg Health 39 studies. In contrast, in the Barbados Eye study, 30 the Rotterdam study, 43 the Los Angeles Latino Eye Study, 44 and the Beaver Dam Eye Study, 11 men had lower IOP, while the Framingham Eye study 33 and the Health and Nutrition Examination Survey 45 reported no association between sex and IOP. It has been hypothesized that the higher IOP in men could be due to a higher prevalence of cardiovascular risk factors in men. 39,44 However, adjusting for cardiovascular risk factors in our study did not materially change the association of IOP with age and sex. 
Hormonal differences and the effect of menopause may also explain some sex differences in IOP. 46 Estrogen may affect the inflow of aqueous humor, the ciliary body, and the trabecular meshwork. 47 Indeed, an Indian study showed that the IOP in postmenopausal women was higher compared with premenopausal women and attributed this difference to the higher levels of testosterone and the decrease in estrogen and progesterone levels with the onset of menopause. 48 Use of hormone replacement therapy has also been associated with a lower IOP. 49,50 Further research is needed to understand sex-related differences in IOP. 
Our study has several strengths. To our knowledge, the Kangbuk Samsung Health Study is by far the largest population-based cohort study evaluating the association between age and IOP. The longitudinal nature allowed us to evaluate within-person trajectories in IOP, avoiding many biases in cross-sectional studies. The dropout rate was modest (over 77% of participants recruited in the first year of the study had at least one additional follow-up visit), and we were able to incorporate multiple potential confounders and intermediate factors. Finally, we used a statistical approach based on a three-level hierarchical approach to appropriately account for correlations between eyes and between visits for each participant. 
Some limitations of our study also need to be considered. First, we used noncontact tonometers to measure IOP instead of applanation tonometers, the use of which is considered the gold standard. This may have resulted in measurement error that may have underestimated study associations. Second, our study included preferentially young and middle-aged adults, and only 6% of study participants were 60 years of age or older at baseline. Additional studies with follow-up of older participants are needed to better understand the relationship of age with IOP beyond the sixth decade of age in Asian populations. Finally, our study population consisted of middle-aged Korean men and women attending health-screening visits, which may limit the generalizability of our findings to other populations. 
In conclusion, we found that IOP was inversely associated with age in a large cohort of Korean adults attending health-screening visits. For men, this inverse association was observed throughout the entire age range, while for women it was evident only in younger (<30 years of age) and older (≥60 years of age) women, with no association in women aged 30 to 59. Further research is needed to better understand the underlying mechanisms and to reconsider cutoffs for defining high IOP by age and sex groups in Asian populations. 
Acknowledgments
The authors alone are responsible for the content and writing of the paper. 
Disclosure: D. Zhao, None; M.H. Kim, None; R. Pastor-Barriuso, None; Y. Chang, None; S. Ryu, None; Y. Zhang, None; S. Rampal, None; H. Shin, None; J.M. Kim, None; D.S. Friedman, None; E. Guallar, None; J. Cho, None 
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Figure
 
Longitudinal trend in intraocular pressure by age at follow-up. Curves represent adjusted average intraocular pressures (solid lines) and their 95% confidence intervals (dashed lines) based on restricted quadratic splines with knots at 30, 40, 50, and 60 years of age. Results were obtained from linear mixed models with interactions between spline terms and sex and random variations in age trends among participants and between eyes within participants, and they were adjusted for study center (Seoul or Suwon), height (continuous), and time-varying changes in intraocular pressure measurement time (morning or afternoon), smoking status (never, former, or current), alcohol drinking (<1, 1–3, or >3 d/wk), physical activity (none, 1–3, or >3 times/wk), heart rate (continuous), body mass index (continuous), hypertension (no or yes), diabetes (no or yes), and dyslipidemia (no or yes). The histogram represents the age distribution of person-visits among males (shaded bars) and females (white bars).
Figure
 
Longitudinal trend in intraocular pressure by age at follow-up. Curves represent adjusted average intraocular pressures (solid lines) and their 95% confidence intervals (dashed lines) based on restricted quadratic splines with knots at 30, 40, 50, and 60 years of age. Results were obtained from linear mixed models with interactions between spline terms and sex and random variations in age trends among participants and between eyes within participants, and they were adjusted for study center (Seoul or Suwon), height (continuous), and time-varying changes in intraocular pressure measurement time (morning or afternoon), smoking status (never, former, or current), alcohol drinking (<1, 1–3, or >3 d/wk), physical activity (none, 1–3, or >3 times/wk), heart rate (continuous), body mass index (continuous), hypertension (no or yes), diabetes (no or yes), and dyslipidemia (no or yes). The histogram represents the age distribution of person-visits among males (shaded bars) and females (white bars).
Table 1
 
Participants' Characteristics at Baseline*
Table 1
 
Participants' Characteristics at Baseline*
Characteristic Overall Female Male P Value
Participants 274,064 119,723 (43.7) 154,341 (56.3)
Age, y 40.2 (9.9) 40.5 (10.3) 39.8 (9.5) <0.001
Study center <0.001
 Seoul 184,957 (67.5) 81,423 (68.0) 103,534 (67.1)
 Suwon 89,107 (32.5) 38,300 (32.0) 50,807 (32.9)
Height, cm 166.2 (8.5) 159.1 (5.5) 171.8 (5.9) <0.001
Smoking status <0.001
 Never 147,039 (54.6) 105,391 (90.8) 41,648 (27.2)
 Former 45,506 (16.9) 4,147 (3.6) 41,359 (27.0)
 Current 76,582 (28.5) 6,497 (5.6) 70,085 (45.8)
Alcohol drinking, d/wk <0.001
  <1 172,819 (64.0) 101,902 (86.8) 70,917 (46.4)
 1–3 71,097 (26.3) 12,732 (10.8) 58,365 (38.2)
  >3 26,319 (9.7) 2,824 (2.4) 23,495 (15.4)
Physical activity, times/wk <0.001
 0 147,688 (54.6) 74,984 (63.5) 72,704 (47.6)
 1–3 77,994 (28.8) 23,647 (20.0) 54,347 (35.6)
 >3 45,005 (16.6) 19,427 (16.5) 25,578 (16.8)
Heart rate, beats/min 67.2 (9.3) 68.3 (9.2) 66.4 (9.3) <0.001
Body mass index, kg/m2 23.5 (3.1) 22.4 (3.1) 24.4 (2.9) <0.001
Hypertension 47,671 (17.4) 14,501 (12.1) 33,170 (21.5) <0.001
Diabetes 10,763 (3.9) 3,416 (2.9) 7,347 (4.8) <0.001
Dyslipidemia 115,731 (42.2) 41,852 (35.0) 73,879 (47.9) <0.001
Intraocular pressure, mm Hg‡ 13.58 (2.66) 13.10 (2.65) 13.95 (2.61) <0.001
 <30 y 13.55 (2.71) 13.10 (2.71) 13.99 (2.64) <0.001
 30–39 y 13.58 (2.70) 12.94 (2.66) 14.05 (2.64) <0.001
 40–49 y 13.62 (2.60) 13.13 (2.58) 13.97 (2.55) <0.001
 50–59 y 13.59 (2.57) 13.52 (2.63) 13.66 (2.51) <0.001
 ≥60 y 13.34 (2.62) 13.37 (2.67) 13.30 (2.57) 0.13
Table 2
 
Longitudinal Changes in Intraocular Pressure per 1-Year Increase in Age, Overall and by Age Interval*
Table 2
 
Longitudinal Changes in Intraocular Pressure per 1-Year Increase in Age, Overall and by Age Interval*
Overall Age Interval, y P Value
<30 30–39 40–49 50–59 ≥60
No. subjects/visits
 Overall 274,064/577,785 23,021/26,033 147,849/268,588 92,650/204,678 36,318/56,144 16,583/22,342
 Female 119,723/224,085 11,282/13,073 61,806/105,754 36,796/71,273 17,292/23,717 8,106/10,268
 Male 154,341/353,700 11,739/12,960 86,043/162,834 55,854/133,405 19,026/32,427 8,477/12,074
Model 1,‡ mm Hg/y
 Overall −0.058
(−0.060 to −0.056)
−0.128
(−0.141 to −0.115)
−0.043
(−0.046 to −0.041)
−0.073
(−0.076 to −0.069)
−0.065
(−0.071 to −0.060)
−0.104
(−0.112 to −0.095)
<0.001
 Female 0.000
(−0.003 to 0.004)
−0.160
(−0.177 to −0.144)
0.005
(0.001 to 0.010)
0.004
(−0.001 to 0.009)
0.009
(0.000 to 0.017)
−0.060
(−0.073 to −0.048)
<0.001
 Male −0.088
(−0.091 to −0.086)
−0.118
(−0.138 to −0.098)
−0.070
(−0.074 to −0.067)
−0.105
(−0.109 to −0.101)
−0.103
(−0.110 to −0.096)
−0.124
(−0.135 to −0.112)
<0.001
 P value§ <0.001 0.001 <0.001 <0.001 <0.001 <0.001
Model 2‖, mm Hg/y
 Overall −0.059
(−0.061 to −0.057)
−0.161
(−0.173 to −0.148)
−0.046
(−0.049 to −0.043)
−0.070
(−0.073 to −0.067)
−0.061
(−0.067 to −0.056)
−0.108
(−0.117 to −0.099)
0.001
 Female 0.001
(−0.003 to 0.005)
−0.170
(−0.186 to −0.153)
0.007
(0.002 to 0.011)
0.006
(0.001 to 0.012)
0.007
(−0.001 to 0.016)
−0.065
(−0.079 to −0.052)
<0.001
 Male −0.087
(−0.090 to −0.085)
−0.127
(−0.148 to −0.107)
−0.071
(−0.074 to −0.067)
−0.103
(−0.106 to −0.099)
−0.103
(−0.110 to −0.095)
−0.120
(−0.132 to −0.109)
<0.001
 P value§ <0.001 0.002 <0.001 <0.001 <0.001 <0.001
Model 3,¶ mm Hg/y
 Overall −0.065
(−0.068 to −0.063)
−0.166
(−0.179 to −0.154)
−0.054
(−0.056 to −0.051)
−0.074
(−0.077 to −0.071)
−0.073
(−0.078 to −0.067)
−0.112
(−0.121 to −0.103)
<0.001
 Female −0.006
(−0.010 to −0.003)
−0.167
(−0.184 to −0.151)
0.001
(−0.004 to 0.005)
−0.001
(−0.007 to 0.004)
−0.012
(−0.021 to −0.004)
−0.076
(−0.089 to −0.063)
<0.001
 Male −0.093
(−0.096 to −0.091)
−0.142
(−0.162 to −0.122)
−0.080
(−0.083 to −0.076)
−0.105
(−0.109 to −0.101)
−0.108
(−0.115 to −0.101)
−0.121
(−0.133 to −0.110)
<0.001
 P value§ <0.001 0.06 <0.001 <0.001 <0.001 <0.001
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