Relation between intraocular pressure and systemic health parameters in Taiwan.

Objective: The aim of the study was to evaluate the association between intraocular pressure (IOP) and several systemic health parameters in Taiwan.

Methods: We retrospectively reviewed the medical records from all participants who had been examined in China Medical University Hospital. A total of 1,271 subjects (mean age, 50.0 [+ or -] 12.0 years; age range, 21 to 85) who had not undergone any medical treatment for ocular hypertension or glaucoma were included in this study. Systemic factors (age, sex, blood pressure, total cholesterol, triglycerides, fasting glucose, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, body mass index, waist circumference) were measured. One-way analysis of variance, Pearson correlation coefficients, and multivariate linear regression were used.

Results: There were 674 (53.0%) males and 597 (47.0%) females in this study. After controlling for the other covariates, the multivariate linear regression analysis showed that in males and females, systolic blood pressure was positively associated with IOP. That is, a change in systolic blood pressure by 1 mm Hg would alter the measured IOP by 0.024 mm Hg in males (95% CI, 0.002, 0.046; P = 0.031) and 0.030 mm Hg in females (95% CI, 0.006, 0.053; P = 0.014). In males, age was inversely associated with IOP. That is, a change in age by 1 year would alter the measured IOP by -0.039 mm Hg (95% CI, -0.059, -0.020; P = 0.0001).

Conclusions: IOP was found to have a strong positive association with systolic blood pressure in males and females. IOP was inversely associated with age in males. This study may provide basic information for further studies on IOP in Taiwan.

Key Words: age, blood pressure, body mass index, intraocular pressure, sex

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Intraocular pressure (IOP) is the ocular parameter commonly associated with glaucoma. It is important to identify factors that may influence the level of IOP. (1-5) Some epidemiologic studies showed a positive relation between IOP and systemic health parameters, such as age, sex, blood pressure and obesity, in general populations across age groups and in both sexes. (5-8) However, there is a negative association between IOP and age in the Japanese (9-11) and Korean populations. (12) There are still few studies evaluating the relation between IOP and different systemic health parameters in Taiwan. Therefore, in this study, we attempted to identify the relation between IOP and several systemic health parameters, using the cross-sectional data from the private health examination in Taiwan residents.

Materials and Methods

From January 2001 to December 2002, a hospital-based, cross-sectional study was carried out. We retrospectively analyzed the medical records of all participants undergoing private health examination with scheduled items at the Department of Family Medicine of China Medical University Hospital in Taichung City, Taiwan. The participants who had not undergone any medical treatment for ocular hypertension or glaucoma were selected as the subjects of this study. A total of 1,271 healthy subjects were included in this study.

Each patient received IOP measurement with the same noncontact tonometer (NT-2000, Nidek, Gamagori, Aichi, Japan). IOP was determined by the mean value of 3 successive readings of both eyes. Because there was a high correlation between left and right eyes for bilateral IOP (r = 0.798, P < 0.0001) in our subjects, IOP value from the right eye was used for further analysis. Each subject received IOP measurement only between 9 and 11 AM to minimize the effect of diurnal variation, Monday through Friday. To avoid interexaminer and intertonometer variances, all IOP measurements were taken by the same trained paramedical assistant without applying topical anesthetic.

Blood pressure for the average of both arms was measured by using a mercury sphygmomanometer in the sitting position. Body mass index (BMI, kg/[m.sup.2]) was measured as follows: weight (kg) divided by height (m). (2) Waist circumference (WC, cm) was measured as the minimum circumference with the tape positioned between xiphoid process and the umbilicus at the end of a normal expiration. (13)

Venous blood samples were obtained in the morning after a 12-hour overnight fast. A number of biochemical markers, such as total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL), and fasting glucose were measured by a biochemical autoanalyzer (Hitachi 736-15, Tokyo, Japan) at the Department of Clinical Laboratory of China Medical University Hospital within 4 hours of collection. If triglyceride level was less than 400 mg/dL, low-density lipoprotein cholesterol (LDL) was calculated by the formula suggested by Friedewald et al (14): LD = TC - (HDL + TG/5).

Statistical analysis was performed by SPSS (Chinese Version 10.0, Sinter Information Corp, Taiwan). The 1-way analysis of variance test distinguished the differences in age, IOP, BMI, WC, systolic blood pressure, diastolic blood pressure, TC, TG, HDL, LDL, and fasting glucose between males and females. Pearson correlation coefficients demonstrated the correlations among age, IOP, BMI, WC, systolic blood pressure, diastolic blood pressure, TC, TG, HDL, LDL, and fasting glucose in this study population. The multivariate linear regression identified the correlates of IOP. A P value less than 0.05 was considered statistically significant.

Results

There were 674 (53.0%) males and 597 (47.0%) females in this study. The mean age was 50.0 years (SD = 12.0; age range, 21 to 85 years). The mean values for age, IOP, BMI, WC, systolic blood pressure, diastolic blood pressure, TC, TG, HDL, LDL, and fasting glucose are shown by sex in Table 1.

Table 2 shows the mean value of IOP specific for age and sex. No significant association between IOP and age was detectable in males and females (P = 0.272 and P = 0.407, respectively), though IOP decreased with age in men.

Table 3 gives the Pearson correlation coefficients for IOP and the covariates. There was a strong positive association between IOP and systolic blood pressure, diastolic blood pressure, and TG in males. There was also a strong positive association between IOP and systolic blood pressure, diastolic blood pressure, and fasting glucose in women.

Table 4 shows the multiple linear regression analysis for IOP and its covariates. After controlling for the other covariates, in both sexes, systolic blood pressures were positively associated with IOP. That is, a change in systolic blood pressure by 1 mm Hg would correlate with a change in the measured IOP by 0.024 mm Hg (95% CI, 0.002, 0.046; P = 0.031) in males and 0.030 mm Hg in females (95% CI, 0.006, 0.053; P = 0.014). In males, age was inversely associated with IOP. That is, a change in age by 1 year would correlate with a change in the measured IOP by -0.039 mm Hg (95% CI, -0.059, -0.020; P = 0.0001).

Discussion

In our study, the mean value of IOP measured by a noncontact tonometer in all subjects was 13.7 [+ or -] 2.9 mm Hg for men and 13.5 [+ or -] 2.9 mm Hg for women. No significant association between IOP and age was detectable in men and women (P = 0.272 and P = 0.407, respectively), though IOP decreased with age in males. IOP was inversely associated with age in males by multiple linear regression analysis. There were many studies mentioned about the association between IOP and age in Western and Asian countries. The effect of age on IOP does appear to be inconsistent across the literature. For example, Shiose (10) and Shiose and Kawase (11) indicated a negative correlation for IOP with age in Japan. Lee et al (12) also reported that IOP decreased with age in a Korean population. Conversely, positive correlation between IOP and age has been reported in some cross-sectional studies in the United States and Europe. (15-20) However, there were still some studies suggesting that there was no relation between IOP and age when considered with other variables (eg, blood pressure, BMI, refractive error, heart rate, and serum cholesterol), such as the Beaver Dam Eye study, Blue Mountain Eye Study, and others. (21-23) Our result seemed to be more similar to the results in Japan and Korea. We found that there was some trend indicating that IOP has a negative correlation with age in Asian countries but a positive correlation in Western countries. We considered that these differences might be caused by ethnic or environmental effects. Therefore, the association between IOP and age has still been controversial.

Many studies have reported a positive association of the IOP with systolic blood pressure. (23-27) In our study, we found that IOP was strongly correlated with systolic blood pressure. The result is similar to those in Western and Asian populations. According to the reports on the mechanism of the association between IOP and blood pressure, (10,20,24) it is suggested that high blood pressure may elevate IOP by increasing the ultrafiltration of aqueous humor through elevation of ciliary artery pressure.

There are many systemic health parameters that will influence IOP, such as obesity. It is presumed that obesity may also exert a direct effect on IOP because of an excess in intraorbital fat tissue and increase in episcleral venous pressure, an increase in blood viscosity, and a consequent decrease in outflow facility. (10,11) Further, several factors such as sympathetic tone, hormones, or sclerotic changes may also be important in the relation between obesity and IOP. (10,11,24) Nearly all studies indicate significant positive association between IOP and BMI. (10-12,21) However, in the Blue Mountain Eye study, (21) no independent influence on IOP from BMI was found after controlling for blood pressure. In our study, we did not find any significant association between IOP and BMI or WC by multivariate linear regression analysis. Another possible explanation for our study comes from the lower BMI cutoffs for Taiwanese noted in recent studies, (28) which means that the traditional standard for obesity used in the previous studies is not acceptable for our Taiwanese population. (29)

A positive correlation between IOP and hypercholesterolemia was also reported previously. (16) We could not find any significant association between IOP and fasting glucose, cholesterol, or triglycerides. One possible reason for this result is that most patients with hyperglycemia, hypercholesterolemia, and hypertriglyceridemia will visit the general physician clinics or special metabolic clinics for further care. Therefore, it would be difficult to uncover a significant correlation between IOP and hyperglycemia, hypercholesterolemia, or hypertriglyceridemia among our study's population.

In this study, IOP was measured by the noncontact tonometer (NCT), which is less accurate than the Goldmann applanation tonometer. The NCT used the fundamental principles of applanation tonometry. (30) In most studies, comparisons with the Goldmann applanation tonometer indicate that the noncontact tonometer is reliable within the normal IOP range. (31) However, the variation obtained with the noncontact tonometer was greater than that with the Goldmann applanation tonometer, (32) so that the sensitivity of the study would have been greater had applanation tonometry been used. (9) Another interesting fact is that the tonometry used in the previous Japanese and Korean studies is NCT. However, the tonometer used in most Western epidemiologic studies is Goldman tonometry. The differences among these studies may be partially due to the different methods for IOP measurement. Besides, some cross-sectional studies showed that there was a highly significant decrease in central cornea thickness with age in both sexes, and that a significant positive correlation was identified between IOP and central corneal thickness. (33,34) In most of these studies evaluating IOP and other risk factors, the parameter of central cornea thickness is seldom considered. However, in a new Japanese study, it was shown that IOP still decreases with age in both sexes, even when adjusted for central cornea thickness. (35) We regret that central cornea thickness is not available in our retrospective data.

We think that the inconsistency among these studies may be due to different study designs, study populations, and statistical methods. The results of this study should not be directly compared with other studies for many reasons. First, the database in our cross-sectional study is from the private health examination, which is not suitable for representation of the general population in Taiwan. Therefore, the distribution of IOP in this study may be somewhat skewed, as compared with that of the general population.

Conclusion

Taiwan has a fast-growing economy and a versatile culture. Dietary habits have shifted toward a high-cholesterol content in recent years. The impact of obesity, hyperglycemia, hypercholesterolemia, and hypertriglyceridemia has also become a critical public health issue in Taiwan. (36,37) To date, there are still very few studies evaluating IOP with systemic health parameters in Taiwan. Our study result also served as the background data for further evaluation on IOP. We believe that the addition of this paper to the literature is important, especially considering the unique Taiwanese population.

Table 1. Basic data of study subjects (a)

                                 Men          Women          Total
Variates                      Mean   SD     Mean    SD     Mean     SD

Age (y) (b)                   49.0   11.6   51.1    12.3   50.0     12.0
IOP (mm Hg)                   13.7    2.9   13.5     2.9   13.6      2.9
BMI (kg/[m.sup.2]) (b)        24.8    3.3   24.2     3.8   24.5      3.5
WC (cm) (d)                   88.3    8.1   82.7     9.9   85.7      9.4
SBP (mm Hg) (d)              128.6   16.5  122.1    20.2  125.5     18.6
DBP (mm Hg) (d)               81.4   10.4   76.6    11.2   79.1     11.1
Glucose (mg/dL)              103.7   38.5  100.3    29.5  102.1     34.6
TC (mg/dL)                   199.8   39.9  198.1    37.2  199.0     38.7
TG (mg/dL) (d)               122.0   67.0   93.7    53.1  108.7     62.5
HDL (mg/dL) (d)               45.3   11.7   54.5    14.3   49.6     13.8
LDL (mg/dL) (c)              130.1   35.8  124.9    32.6  127.6     34.4

(a) SD, standard deviation; IOP, intraocular pressure; BMI, body mass
index; WC, waist circumference; SBP, systolic blood pressure; DBP,
diastolic blood pressure; TC, total cholesterol; TG, triglycerides; HDL,
high-density lipoprotein; LDL, low-density lipoprotein.
(b) P = 0.002.
(c) P = 0.007.
(d) P < 0.0001.

Table 2. Intraocular pressure (mm Hg) by age and sex

                                 Men (N = 674)       Women (N = 597)
Age                           N    %     mean  SD   N    %     mean   SD

20-39 (yr)                    140  20.8  14.0  2.8  102  17.1  13.2  2.7
40-64                         466  69.1  13.7  2.9  403  67.5  13.6  2.8
[greater than or equal to]65   68  10.1  13.3  3.5   92  15.4  13.4  3.3

Men, P = 0.272.
Women, P = 0.407.

Table 3. Pearson correlation coefficients for intraocular pressure and
covariates (a)

                              Men N = 674              Women N = 597
Variates                 IOP          P value       IOP          P value

IOP                      1.000                      1.000
Age                     -0.105        0.006         0.023        0.571
BMI                      0.057        0.140         0.053        0.195
WC                       0.046        0.233         0.044        0.285
SBP                      0.179       <0.0001        0.149        0.0003
DBP                      0.183       <0.0001        0.119        0.004
Glucose                  0.071        0.067         0.086        0.035
TC                       0.072        0.061         0.063        0.126
TG                       0.091        0.019         0.022        0.596
HDL                      0.007        0.847         0.008        0.854
LDL                      0.044        0.252         0.061        0.135

(a) IOP, intraocular pressure; BMI, body mass index; WC, waist
circumference; SBP, systolic blood pressure; DBP, diastolic blood
pressure; TC, total cholesterol; TG, triglycerides; HDL, high-density
lipoprotein; LDL, lowdensity lipoprotein.

Table 4. Multiple linear regression analysis for intraocular pressure
and its covariates in men and women (a)

                         Men N = 674                 Women N = 597
                    Parameter  Standard           Parameter   Standard
Variates            estimate   error     P value  estimate    error

Age                 -0.039     0.010     0.0001   -0.020      0.012
SBP                  0.024     0.011     0.031     0.030      0.012
DBP                  0.021     0.017     0.223    -0.007      0.020
Glucose              0.005     0.003     0.072     0.007      0.004
TG                   0.002     0.002     0.179    -0.001      0.002
Adjusted [R.sup.2]   0.057              <0.0001    0.023

Variates            P value

Age                 0.085
SBP                 0.014
DBP                 0.730
Glucose             0.099
TG                  0.542
Adjusted [R.sup.2]  0.002

(a) SBP, systolic blood pressure; DBP, diastolic blood pressure; TG,
triglycerides.

Accepted July 7, 2004.

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RELATED ARTICLE: Key Points

* There are many epidemiologic studies evaluating the relation in Western and Asian countries between intraocular pressure and systemic health parameters, such as age, sex, blood pressure, obesity, fasting glucose, and blood lipids.

* There are still few studies in Taiwan to evaluate the intraocular pressure with other risk factors.

* In this study, intraocular pressure was found to have a strong positive association with systolic blood pressure in males and females.

* Intraocular pressure was inversely associated with age in males.

Hsin-Yi Chen, MD, and Shih-Wei Lai, MD

From the Department of Ophthalmology and Department of Family Medicine, China Medical University Hospital, Taichung City, Taiwan.

Reprint requests to Dr. Shih-Wei Lai, Department of Family Medicine, Add: No. 2, Yu-Der Road, Taichung City, Taiwan. E-mail: [e-mail]