August 2011
Volume 52, Issue 9
Free
Clinical and Epidemiologic Research  |   August 2011
Ocular Biometry in an Urban Indian Population: The Singapore Indian Eye Study (SINDI)
Author Affiliations & Notes
  • Chen-Wei Pan
    From the Departments of Epidemiology and Public Health and
  • Tien-Yin Wong
    From the Departments of Epidemiology and Public Health and
    Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; and
    the Singapore Eye Research Institute and Singapore National Eye Centre, Singapore.
  • Lan Chang
    From the Departments of Epidemiology and Public Health and
  • Xiao-Yu Lin
    the Singapore Eye Research Institute and Singapore National Eye Centre, Singapore.
  • Raghavan Lavanya
    the Singapore Eye Research Institute and Singapore National Eye Centre, Singapore.
  • Ying-Feng Zheng
    Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; and
    the Singapore Eye Research Institute and Singapore National Eye Centre, Singapore.
  • Yee-Onn Kok
    From the Departments of Epidemiology and Public Health and
  • Ren-Yi Wu
    the Singapore Eye Research Institute and Singapore National Eye Centre, Singapore.
  • Tin Aung
    Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; and
    the Singapore Eye Research Institute and Singapore National Eye Centre, Singapore.
  • Seang-Mei Saw
    From the Departments of Epidemiology and Public Health and
    Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; and
    the Singapore Eye Research Institute and Singapore National Eye Centre, Singapore.
  • Corresponding author: Seang-Mei Saw, Department of Epidemiology and Public Health, Yong Loo Lin School of Medicine, National University of Singapore, 16 Medical Drive (MD 3), Singapore 117597; ephssm@nus.edu.sg
Investigative Ophthalmology & Visual Science August 2011, Vol.52, 6636-6642. doi:10.1167/iovs.10-7148
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      Chen-Wei Pan, Tien-Yin Wong, Lan Chang, Xiao-Yu Lin, Raghavan Lavanya, Ying-Feng Zheng, Yee-Onn Kok, Ren-Yi Wu, Tin Aung, Seang-Mei Saw; Ocular Biometry in an Urban Indian Population: The Singapore Indian Eye Study (SINDI). Invest. Ophthalmol. Vis. Sci. 2011;52(9):6636-6642. doi: 10.1167/iovs.10-7148.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose.: To describe the distribution and determinants of ocular biometric parameters in adult Singapore Indians.

Methods.: A population-based, cross-sectional study was conducted on 3400 Indians aged 40 to 83 years residing in Singapore. Ocular components including axial length (AL), anterior chamber depth (ACD), and corneal radius (CR) were measured by partial coherence interferometry. Refraction was recorded in spherical equivalent (SE).

Results.: After 502 individuals with previous cataract surgery were excluded, ocular biometric data on 2785 adults were analyzed. The mean AL, ACD, and CR were 23.45 ± 1.10, 3.15 ± 0.36, and 7.61 ± 0.26 mm, respectively. The mean AL/CR ratio was 3.08 ± 0.13. The mean AL was 23.53, 23.49, 23.35, and 23.25 mm in 40- to 49-, 50- to 59-, 60- to 69-, and 700 to 83-year age groups, respectively (P < 0.001). Men had significantly longer ALs than women (23.68 mm versus 23.23 mm, P < 0.001). In multivariate linear regression models, AL was found to be longer in adults who were taller (P < 0.001), better educated (University, P < 0.001), and more apt to spend time reading (P < 0.001). Increasing CR was associated with increasing height (P = 0.008). AL was the strongest determinant for refraction in all age groups, whereas lens nuclear opacity was a predictor in adults aged 60 to 83 years.

Conclusions.: The AL in Indians living in Singapore was similar to that of Malays in Singapore, but longer than that of Indians living in India. Time spent reading, height, and educational level were the strongest determinants of AL. AL was the strongest predictor of SE in all age groups.

Myopia is a complex trait associated with various genetic and environmental factors. 1,2 The exact etiology of myopia remains unclear. 3 The refractive status is influenced by ocular biometric parameters such as axial length (AL) and corneal radius (CR) of curvature. The prevalence of myopia in adults over 40 years has been reported in several population-based studies with different results. It is still unclear whether myopia prevalence is higher in East Asian countries than in Western countries. The Tanjong Pagar study reported a prevalence of 51.7% myopia in with and 45.2% in men in the 40 to 49 years age group in Singaporean Chinese (spherical equivalent [SE] < −0.5 D). 4 However, the meta-analysis by Kempen et al. 5 reported a prevalence of 46.3% for North American white females and 36.8% for males using a more conservative definition of myopia (SE < −1.0 D). The myopia prevalence reported in the Singaporean Malays 6 and Indians 7 are also lower than those from North America. 8,9 Understanding the interrelationship between refraction and ocular biometry may help to explain the trends and patterns of refractive errors observed in different populations and ethnicities. 10,11 However, although the epidemiology of refractive errors has been reported in different countries and ethnicities worldwide, only a small fraction of population-based studies have described ocular biometry distribution. 10,12  
Most studies on ocular biometric parameters have focused on children 13,14 and adolescents 15 or on selected groups, such as university students 16,17 and microscopists. 18 In addition, there is evidence that the AL/CR ratio of an emmetropic eye is usually very close to 3.0, and a higher AL/CR ratio was reported to be a risk factor in myopia. 19,20 However, few population-based studies have reported the AL/CR ratio and its association with refractive error. 
There are approximately 1 billion Indians worldwide, including approximately 25 million migrants who live outside India. The Central India Eye and Medical Study measured the ALs of Indians over 30 years of age living in India. 21 To further understand the patterns of ocular biometric parameters in Indians living outside India, we examined the distribution and determinants of ocular biometric parameters and their relationship with refractive status in adult Singaporean Indians. 
Methods
Study Cohort
The Singapore Indian Eye Study (SINDI) is a population-based, cross-sectional study, designed to assess various ocular disorders of adult Indians over 40 years of age. Approximately 7% of the Singaporean population is Indian and most of our study subjects (65%) were born in Singapore. The detailed study protocol has been published elsewhere 22 and follows the protocol of the Singapore Malay Eye Study (SiMES). 23 In brief, Indian adults over 40 years of age residing in South-west Singapore were selected from the Ministry of Home Affairs database by using an age-stratified random sampling process. Of the 4497 subjects eligible from the sampling frame (n = 6350), 3400 (75.6% response rate) were examined between 2007 and 2009. Subjects were ineligible (n = 1853) if they had no longer lived at the registered address or were terminally ill. 
This study was approved by the ethics committee of Singapore National Eye Center and was conducted in accordance with the tenets of the World Medical Association's Declaration of Helsinki. Written informed consent was obtained from all participants. 
Clinic Examination
Ocular biometric parameters of AL and anterior chamber depth (ACD) were measured using noncontact partial coherence interferometry (IOL Master, ver, 3.01; Carl Zeiss Meditec AG, Jena, Germany). 
Noncycloplegic refraction was used in our study. Refraction (sphere, cylinder, and axis) and corneal radius in the horizontal and vertical meridians were initially estimated with an autorefractor (RK-5 Autorefractor-Keratometer; Canon, Inc. Ltd., Tokyo, Japan). A mean value along each meridian was recorded, and the mean CR was calculated as the average of the steep and flat curvatures. Refraction was subjectively refined by study optometrists until the best visual acuity was obtained. These subjective refraction results were used in analysis. If the subjective refraction was not available, results of autorefraction were used instead. 
All participants underwent a standardized slit-lamp (model BQ-900; Haag-Streit, Köniz, Switzerland) examination. Other examinations included weight, height, blood pressure, blood glucose, and cholesterol measurements. Weight was assessed in kilograms by a digital scale, with subjects removing the outer layers. Height was measured by a wall-mounted metric measuring tape with shoes removed. Blood pressure was measured with a digital automatic blood pressure monitor (Dinamap model Pro Series DP110X-RW, 100V2; GE Medical Systems Information Technologies, Inc., Hermosa Beach, CA) with the subject in a seated position, after 5 minutes of rest. Venous blood was collected to determine nonfasting blood glucose, total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. 
Questionnaires and Interview
A detailed interview was administered using a standardized questionnaire to collect information on medical history, cigarette smoking (never smoked/current smoker/past smoker), alcohol consumption (yes/never), educational level (no formal education/primary education/secondary education/polytechnic/university), and near-work activities (number of hours spent reading and using the computer per day). 
Definitions of Diseases
Lens opacity was graded under the slit-lamp using modified Lens Opacities Classification System III (LOCS III) scores. 24 Any cataract was defined as the presence of any nuclear cataract (LOCS III score for nuclear opalescence or nuclear color of 4 or more), any cortical cataract (LOCS III score of 2 or more), or any posterior subcapsular cataract (LOCS III of 2 or more) in either eye. Hypertension was defined as systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or a physician diagnosis. Diabetes mellitus was identified from nonfasting blood glucose ≥200 mg/dL (11.1 mmol/L), or self-reported use of diabetic medication, or physician-diagnosed diabetes. Body mass index (BMI) was calculated as the weight divided by the square of the height (kilograms per meter squared). 
Statistical Analyses
Among the 3400 subjects, those with cataract surgery history (n = 502) were excluded from analyses. We also excluded phakic participants without ocular biometry data (n = 113). As a result, 2785 (84.6%) participants were included in the analyses. Since ocular biometric parameters for the right and left eyes correlated highly (Pearson correlation coefficient for AL = 0.94, P < 0.001; ACD = 0.89, P < 0.001; and CR = 0.99, P < 0.001), analyses were performed on right eyes only. 
Mean biometry data were compared across each age group stratified by sex, and linear test for trend was used to investigate significance for each age group. Possible predictors for each biometric parameter were assessed in univariate analyses. Variables with a P < 0.05 in univariate analyses and of scientific importance were included in multiple linear regression models, and manual backward stepwise elimination procedures were performed based on a criterion of P < 0.05 to achieve the final, most parsimonious model. Linear regression models were then constructed to evaluate independent effects of lens opacity and ocular biometric components (independent variables) on refraction (dependent variable) in all age groups. Standardized regression coefficients in these models were used to determine the relative importance of nuclear opacity (NO) and each biometric component on refraction (SPSS 16.0; SPSS Inc., Chicago, IL). 
Results
Participants in the study (n = 3400, mean age: 57.8 ± 10.1 years) were younger than nonparticipants (n = 1097, mean age: 61.1 ± 10.5 years; P < 0.001), but there was no difference in sex (P = 0.28). 
Table 1 shows the means of ocular biometric parameters by age and sex. The mean AL, ACD, and CR for the overall population were 23.45 ± 1.10, 3.15 ± 0.36, and 7.61 ± 0.26 mm, respectively. The mean AL/CR ratio was 3.08 ± 0.13. The men had significantly longer AL (P < 0.001), deeper ACD (P < 0.001), and flatter CR (P < 0.001) than the women had. There was a significant trend of decreasing AL and ACD with increasing age for the population as a whole and for the men and women separately. On average, persons aged 40 to 49 years, when compared with those aged 70 to 83 years, had longer ALs (mean difference, 0.18 mm) and deeper ACDs (mean difference, 0.32 mm). CR did not vary significantly with age (P = 0.22). There were no age (P = 0.11) or sex (P = 0.37) differences seen in AL/CR ratio comparisons. 
Table 1.
 
AL, ACD, CR, and AL/CR Ratio by Age and Sex
Table 1.
 
AL, ACD, CR, and AL/CR Ratio by Age and Sex
n AL (mm) ACD (mm) CR (mm) AL/CR
All persons 2785 23.45 ± 1.10 3.15 ± 0.36 7.61 ± 0.26 3.08 ± 0.13
Men 1406 23.68 ± 1.06 3.19 ± 0.36 7.68 ± 0.26 3.09 ± 0.12
Women 1379 23.23 ± 1.10 3.10 ± 0.35 7.55 ± 0.25 3.08 ± 0.14
P <0.001 <0.001 <0.001 0.37
All persons
    40–49 years 871 23.53 ± 1.08 3.24 ± 0.35 7.62 ± 0.26 3.09 ± 0.14
    50–59 years 1019 23.49 ± 1.15 3.18 ± 0.35 7.61 ± 0.26 3.09 ± 0.14
    60–69 years 682 23.35 ± 1.14 3.05 ± 0.35 7.60 ± 0.26 3.07 ± 0.13
    70–83 years 213 23.25 ± 0.78 2.92 ± 0.36 7.61 ± 0.26 3.06 ± 0.10
    P trend <0.001 <0.001 0.22 0.11
Men
    40–49 years 427 23.71 ± 1.01 3.27 ± 0.36 7.68 ± 0.26 3.09 ± 0.13
    50–59 years 498 23.72 ± 1.07 3.23 ± 0.34 7.68 ± 0.25 3.09 ± 0.12
    60–69 years 357 23.68 ± 1.19 3.11 ± 0.36 7.68 ± 0.26 3.08 ± 0.13
    70–83 years 124 23.36 ± 0.70 2.97 ± 0.34 7.64 ± 0.27 3.06 ± 0.09
    P trend 0.02 <0.001 0.44 0.09
Women
    40–49 years 444 23.36 ± 1.12 3.20 ± 0.33 7.57 ± 0.26 3.09 ± 0.15
    50–59 years 521 23.28 ± 1.18 3.13 ± 0.34 7.55 ± 0.25 3.09 ± 0.15
    60–69 years 325 22.99 ± 0.96 2.98 ± 0.33 7.51 ± 0.24 3.06 ± 0.13
    70–83 years 89 23.09 ± 1.25 2.85 ± 0.32 7.58 ± 0.25 3.05 ± 0.11
    P trend <0.001 <0.001 0.06 0.12
The distribution of ALs is shown in Figures 1 and 2. ALs for the overall population did not demonstrate normal distribution (kurtosis = 6.1; skewness = 1.4; P for Kolmogorov-Smirnov [K-S] test < 0.001). When stratified by age groups, only AL followed a normal distribution in the oldest age group (70–83 years; kurtosis = 1.3; skewness = 0.05; P for K-S test = 0.68). In younger age groups, the distributions of ALs were all positively skewed. The distributions ALs were also positively skewed in the men (kurtosis = 8.7, skewness = 1.2, P for K-S test <0.001) and the women (kurtosis = 4.7, skewness = 1.4; P for K-S test <0.001). Both ACDs and CRs were normally distributed in this population. 
Figure 1.
 
Distribution of AL in the overall sample.
Figure 1.
 
Distribution of AL in the overall sample.
Figure 2.
 
Distribution of AL by age groups.
Figure 2.
 
Distribution of AL by age groups.
The correlation between SE and AL/CR (r = −0.78; P < 0.01) was stronger than that between SE and AL (r = −0.65; P < 0.01). Persons with a more negative SE had longer AL or higher AL/CR ratio. The relationship between AL and SE was different in adults with and without nuclear cataract (Fig. 3). CR showed a weak positive relationship with AL (r = 0.48, P < 0.05), but there was no relationship with CR and SE (r = 0.08, P = 0.65). ACD correlated positively with AL (r = 0.47, P < 0.01), but was negatively associated with SE (r = −0.31, P < 0.01). The relationship between AL and CR was stronger in nonmyopic eyes than in myopic eyes (Fig. 4). 
Figure 3.
 
Association between AL and SE in adults with and without nuclear cataract.
Figure 3.
 
Association between AL and SE in adults with and without nuclear cataract.
Figure 4.
 
Correlations between AL and CR by refractive status.
Figure 4.
 
Correlations between AL and CR by refractive status.
Three multivariate linear regression models were constructed to explore the determinants for AL, ACD, and CR. After adjustment for age, sex, diabetes, and nuclear cataract, each centimeter of height increase was associated with 0.034-mm increase in AL. For every additional hour spent on reading and writing per day, there was a 0.064-mm increase in AL. Adults with a university education had 0.408-mm longer mean AL than those with no formal education. Deeper ACDs were found in adults who were younger (regression coefficient = −0.01 mm, P < 0.001), taller (regression coefficient = 0.004 mm, P < 0.001), and read more per day (regression coefficient = 0.01 mm, P = 0.02). Increasing CRs were positively associated with height (regression coefficient = 0.009 mm, P = 0.008; Table 2). 
Table 2.
 
Multiple Linear Regression Models of Ocular Biometric Parameters
Table 2.
 
Multiple Linear Regression Models of Ocular Biometric Parameters
AL (mm) ACD (mm) CR (mm)
β 95% CI P β 95% CI P β 95% CI P
Age, y −0.001 −0.007 to 0.004 0.61 −0.011 −0.018,−0.004 <0.001 0.001 −0.004,0.006 0.67
Female 0.098 −0.018 to 0.215 0.10 −0.028 −0.061 to 0.005 0.16 −0.009 −0.125 to 0.107 0.88
Reading hours per day 0.064 0.034 to 0.094 <0.001 0.013 0.004 to 0.022 0.02
Education level
    No formal education 0
    Primary education 0.065 −0.104 to 0.235 0.45
    Secondary education 0.166 −0.020 to 0.351 0.08
    Polytechnic 0.350 0.142 to 0.558 0.001
    University 0.408 0.192 to 0.624 <0.001
Height, cm 0.034 0.0034 to 0.028 <0.001 0.004 0.0008 to 0.007 <0.001 0.009 0.002 to 0.015 0.008
Diabetes −0.078 −0.164 to 0.007 0.07
Nuclear cataract 0.001 −0.142 to 0.143 0.99
Linear regression models were constructed to evaluate the independent effect of biometric components on SE in all age groups. In model 1, AL, CR, and NO (LOCS III) were analyzed as independent variables, with SE as the dependent variable. In model 2, the AL/CR ratio and NO (LOCS III) were analyzed as independent variables, with SE as the dependent variable. A standardized regression coefficient was used to estimate the relative effect of each biometric component on SE. In all age groups, AL or the AL/CR ratio was the highest relative predictor of SE, with the standardized regression coefficient being the largest. NO was not a significantly predictor of SE in the 40- to 59-year age group. However, NO played a more important role in older age groups. The standardized regression coefficients were −0.27 in model 1 and −0.31 in model 2 for NO in the 70- to 83-year age group (Table 3). 
Table 3.
 
Multivariable Linear Regression Models for Spherical Equivalent Refraction, by AL, CR, AL/CR ratio and NO (LOCS III) Stratified by Age
Table 3.
 
Multivariable Linear Regression Models for Spherical Equivalent Refraction, by AL, CR, AL/CR ratio and NO (LOCS III) Stratified by Age
Unstandardized Regression Coefficient Standardized Regression Coefficient P
All persons
    Model 1
        AL −1.88 −0.91 <0.001
        CR 4.39 0.53 <0.001
        NO (LOCS III) −0.009 −0.005 0.73
    Model 2
        AL/CR −13.5 −0.8 <0.001
        NO (LOCS III) 0.02 0.01 0.47
40–49 years
    Model 1
        AL −1.81 −0.95 <0.001
        CR 4.2 0.54 <0.001
        NO (LOCS III) 0.18 0.005 0.76
    Model 2
        AL/CR −12.8 −0.84 <0.001
        NO (LOCS III) −0.03 −0.01 0.57
50–59 years
    Model 1
        AL −1.94 −0.97 <0.001
        CR 4.62 0.52 <0.001
        NO (LOCS III) −0.44 −0.04 0.02
    Model 2
        AL/CR −14.1 −0.84 <0.001
        NO (LOCS III) 0.004 0.002 0.93
60–69 years
    Model 1
        AL −1.81 −0.87 <0.001
        CR 4.36 0.52 <0.001
        NO (LOCS III) −0.8 −0.14 <0.001
    Model 2
        AL/CR −13.1 −0.74 <0.001
        NO (LOCS III) −0.28 −0.15 <0.001
70–83 years
    Model 1
        AL −1.5 −0.57 <0.001
        CR 4.42 0.57 <0.001
        NO (LOCS III) −1.12 −0.27 <0.001
    Model 2
        AL/CR −11.5 −0.55 <0.001
        NO (LOCS III) −0.54 −0.31 <0.001
Discussion
This study documented population-based data on ocular biometry of Indians in urban Singapore. The mean AL, ACD, and CR of this population were 23.45, 3.15, and 7.61 mm, respectively. A more myopic refraction was predominately explained by longer AL or greater AL/CR ratio throughout the whole age range, although lens NO was also a predictor of refraction in older age groups. Height, time spent reading, and educational level were the most important predictors of AL. 
In previous studies, 10,25 27 AL was measured by A-scan ultrasound biometry which requires corneal surface contact, and the measurement is more time consuming. The noncontact optical biometry measurement which uses partial coherence interferometry technology (IOL Master; Carl Zeiss Meditec) eliminates the deficiency of A-scan ultrasound measurement. It was suggested that the IOL Master is a better predictor of normative ocular biometric data than is ultrasound biometry. 21 Biometry data from ultrasound and laser interferometry may be slightly different. 28 ACD using ultrasound was found to be significantly shorter than that with noncontact measuring systems. 29 Compared with A-scan ultrasound, IOL Master could either overestimate 30 or underestimate 31 AL. IOL Master also does not provide lens thickness measurements. 
It is worthwhile comparing our findings with those of the Central India Eye and Medical Study on Indians living in India. The mean AL in that study (22.6 mm) was significantly shorter than in our SINDI study (23.45 mm). The magnitude of the difference is considerable, and it is unlikely to be explained by differences in AL measurement method or age range of the participants. The difference in AL may be explained by a greater degree of urbanization in Singapore and subsequently a higher rate of axial myopia. 
Comparing the mean AL among different population-based studies would help to clarify the interethnic variation in AL and its association with refractive errors. Compared with the other two major ethnic groups in Singapore, the mean AL in this Singaporean Indian cohort is similar to that of the Singaporean Malays in the SiMES, but slightly longer than that of Singaporean Chinese in the Tanjong Pagar Survey. However, different age and sex distributions may account for the differences observed among these population-based studies. To compare the association between AL and SE more accurately, we compared the mean AL and SE in different population-based studies in the 40 to 49 years age group since SE is mostly explained by AL and influence by lens opacity is minimal in this age group (Table 4). We found longer AL to be associated with more negative SE. Singaporean Chinese with the longest mean AL have the most negative mean SE. As can be seen in Table 4, there was a trend toward longer AL among the populations with more negative SE, although there was no significant difference (P = 0.08 for men and P = 0.13 for women) due the small sample size. 
Table 4.
 
Mean Axial Length and Spherical Equivalent in Adults 40–49 Years of Age in Different Population-Based Studies
Table 4.
 
Mean Axial Length and Spherical Equivalent in Adults 40–49 Years of Age in Different Population-Based Studies
Study Ethnicity Measurement of AL Mean SE (Diopters) Mean AL (mm)
Men Women Men Women
The Los Angeles Latino Eye Study 25 Latinos Ultrasound −0.3 −0.3 23.7 23.2
The Mongolian Study 27 Mongolians Ultrasound 0.1 −0.3 23.4 23.0
The Tanjong Pagar Survey 10 Chinese Ultrasound −1.4 −2.1 23.8 23.4
The Meiktila Eye Study 26 Burmese Ultrasound −0.4 −0.6 23.2 22.6
The Singapore Malay Eye Study 12 Malay IOL Master −0.6 −1.1 23.8 23.6
The Singapore Indian Eye Study Indians IOL Master −0.5 −0.8 23.7 23.2
In our study, older adults tended to have shorter ALs. This has also been observed in Singaporean Chinese 10 and Singaporean Malays, 12 but not in Latinos, 25 Burmese, 26 and Mongolians. 27 In addition, age was only associated with AL in univariate analyses, and the association disappeared when height and education were adjusted in the multivariate model in our study. This suggests that younger subjects may be generally taller and more educated, which correspondingly make AL longer than those of older counterparts. In SiMES, age was also associated with AL in univariate analysis (P < 0.001), but was not a significant determinant of AL in the multiple logistic model (P = 0.55). Although AL may decrease with increasing age, 32 the age pattern for AeL is more likely due to cohort effect than age effect, at least in Singapore. 
In our study, longer ALs were found in adults who were taller, more educated, and spent more time on reading. Height was the strongest predictor of AL in prior studies. 10,25,26,33 35 The association between more time on near work and longer ALs was reported in studies on children, and our study confirmed this association. It was found in Singapore that children who read more than two books per week had ALs that were 0.17 mm longer compared with children who read two or fewer books per week. 14 The mechanism of how near work elongates AL may be the growth induced by excessive accommodation, 36 but this theory remains debatable and has not been supported by findings in animal studies. 37,38 Previous population-based studies on adults have found an association between educational level and AL. 39 In SiMES, increasing AL was associated with higher education level (standardized β = 0.118, P < 0.001). 12 In the Tanjong Pagar Survey on Singaporean Chinese adults, AL increase by 0.60 mm for every 10 years of education (95% CI, 0.34–0.85). 10 Our study found that this association exists only at college or university educational level. The implications of AL as an endophenotype compared with refractive error should be considered. AL is used as an endophenotype for refraction, since refraction is affected both by genetic and environmental factors, whereas AL may provide a simpler phenotype. 40 However, our study showed that AL is also associated with environmental factors such as near work and educational level, in addition to height. Moreover, AL may be related to genetic variants too. Thus, AL as an endophenotype for refraction is still controversial and should be studied further. Both refraction and AL should be examined in detail in further epidemiologic studies of myopia. 
AL is the most important predictor of refraction, with standardized regression coefficients of AL being the largest in all age groups (Table 3). In younger age groups such as 40 to 49 years and 50 to 59 years, AL accounts for most of the variation in refraction. Although lens opacity became an additional significant predictor of refraction in older age groups, explaining why there was a myopic shift from 60 to 69 to 70 to 83 years. Lens opacity affect refraction through increased power of the more sclerotic lens rather than increased AL. 41 44 This pattern is supported by the Tanjong Pagar Survey 10 and the Los Angeles Latino Eye Study. 25  
In our study, taller adults were also found to have deeper ACDs and flatter corneas, indicating an overall increase in eye globe size. However, SE correlated weakly with CR or ACD, confirming other reports that AL is the main determinant of SE, whereas CR and ACD are of relatively minor importance. AL/CR ratio correlated even more highly with SE than AL alone in our study. This correlation indicates that longer eyes, including those that are long because of overall body stature, are not necessarily myopic. Eyes that are long because of excessive axial elongation are in fact myopic. In our study, ALs correlated less with CRs in myopic eyes than in nonmyopic eyes, indicating that emmetropization is substantially based on matching AL to CR, and thus this ratio normalizes for overall eye size and its relationship to height. 
Our study has several strengths. First, it provides the first population-based data on ocular biometry measured by IOL Master in urban Indians. Furthermore, the sample size is sufficient and the response rate (75.6%) is reasonable. Finally, our study used standardized protocols to obtain biometric measurements and refractive error, which allows comparison of our data to other population-based data. However, there are several limitations of our studies. First, there may be selection bias, as participants were generally younger than nonparticipants. Second, cross-sectional study design could not separate cause from effect when assessing determinants of ocular biometric parameters. Finally, the IOL Master does not measure other important biometric parameters, such as lens thickness and vitreous chamber depth. 
In conclusion, in this urban Indian population in Singapore, the mean ocular AL was longer than that of those living in rural India. Longer AL was associated with more time spent reading, higher educational level, and taller stature. Refraction was mostly explained by AL and was partially explained by lens NO in older age groups. 
Footnotes
 Supported by Biomedical Research Council (BMRC) Grant 08/1/35/19/550 and National Medical Research Council (NMRC) Grant STaR/0003/2008, Singapore.
Footnotes
 Disclosure: C.-W. Pan, None; T.-Y. Wong, None; L. Chang, None; X.-Y. Lin, None; R. Lavanya, None; Y.-F. Zheng, None; Y.-O. Kok, None; R.-Y. Wu, None; T. Aung, None; S.-M. Saw, None
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Figure 1.
 
Distribution of AL in the overall sample.
Figure 1.
 
Distribution of AL in the overall sample.
Figure 2.
 
Distribution of AL by age groups.
Figure 2.
 
Distribution of AL by age groups.
Figure 3.
 
Association between AL and SE in adults with and without nuclear cataract.
Figure 3.
 
Association between AL and SE in adults with and without nuclear cataract.
Figure 4.
 
Correlations between AL and CR by refractive status.
Figure 4.
 
Correlations between AL and CR by refractive status.
Table 1.
 
AL, ACD, CR, and AL/CR Ratio by Age and Sex
Table 1.
 
AL, ACD, CR, and AL/CR Ratio by Age and Sex
n AL (mm) ACD (mm) CR (mm) AL/CR
All persons 2785 23.45 ± 1.10 3.15 ± 0.36 7.61 ± 0.26 3.08 ± 0.13
Men 1406 23.68 ± 1.06 3.19 ± 0.36 7.68 ± 0.26 3.09 ± 0.12
Women 1379 23.23 ± 1.10 3.10 ± 0.35 7.55 ± 0.25 3.08 ± 0.14
P <0.001 <0.001 <0.001 0.37
All persons
    40–49 years 871 23.53 ± 1.08 3.24 ± 0.35 7.62 ± 0.26 3.09 ± 0.14
    50–59 years 1019 23.49 ± 1.15 3.18 ± 0.35 7.61 ± 0.26 3.09 ± 0.14
    60–69 years 682 23.35 ± 1.14 3.05 ± 0.35 7.60 ± 0.26 3.07 ± 0.13
    70–83 years 213 23.25 ± 0.78 2.92 ± 0.36 7.61 ± 0.26 3.06 ± 0.10
    P trend <0.001 <0.001 0.22 0.11
Men
    40–49 years 427 23.71 ± 1.01 3.27 ± 0.36 7.68 ± 0.26 3.09 ± 0.13
    50–59 years 498 23.72 ± 1.07 3.23 ± 0.34 7.68 ± 0.25 3.09 ± 0.12
    60–69 years 357 23.68 ± 1.19 3.11 ± 0.36 7.68 ± 0.26 3.08 ± 0.13
    70–83 years 124 23.36 ± 0.70 2.97 ± 0.34 7.64 ± 0.27 3.06 ± 0.09
    P trend 0.02 <0.001 0.44 0.09
Women
    40–49 years 444 23.36 ± 1.12 3.20 ± 0.33 7.57 ± 0.26 3.09 ± 0.15
    50–59 years 521 23.28 ± 1.18 3.13 ± 0.34 7.55 ± 0.25 3.09 ± 0.15
    60–69 years 325 22.99 ± 0.96 2.98 ± 0.33 7.51 ± 0.24 3.06 ± 0.13
    70–83 years 89 23.09 ± 1.25 2.85 ± 0.32 7.58 ± 0.25 3.05 ± 0.11
    P trend <0.001 <0.001 0.06 0.12
Table 2.
 
Multiple Linear Regression Models of Ocular Biometric Parameters
Table 2.
 
Multiple Linear Regression Models of Ocular Biometric Parameters
AL (mm) ACD (mm) CR (mm)
β 95% CI P β 95% CI P β 95% CI P
Age, y −0.001 −0.007 to 0.004 0.61 −0.011 −0.018,−0.004 <0.001 0.001 −0.004,0.006 0.67
Female 0.098 −0.018 to 0.215 0.10 −0.028 −0.061 to 0.005 0.16 −0.009 −0.125 to 0.107 0.88
Reading hours per day 0.064 0.034 to 0.094 <0.001 0.013 0.004 to 0.022 0.02
Education level
    No formal education 0
    Primary education 0.065 −0.104 to 0.235 0.45
    Secondary education 0.166 −0.020 to 0.351 0.08
    Polytechnic 0.350 0.142 to 0.558 0.001
    University 0.408 0.192 to 0.624 <0.001
Height, cm 0.034 0.0034 to 0.028 <0.001 0.004 0.0008 to 0.007 <0.001 0.009 0.002 to 0.015 0.008
Diabetes −0.078 −0.164 to 0.007 0.07
Nuclear cataract 0.001 −0.142 to 0.143 0.99
Table 3.
 
Multivariable Linear Regression Models for Spherical Equivalent Refraction, by AL, CR, AL/CR ratio and NO (LOCS III) Stratified by Age
Table 3.
 
Multivariable Linear Regression Models for Spherical Equivalent Refraction, by AL, CR, AL/CR ratio and NO (LOCS III) Stratified by Age
Unstandardized Regression Coefficient Standardized Regression Coefficient P
All persons
    Model 1
        AL −1.88 −0.91 <0.001
        CR 4.39 0.53 <0.001
        NO (LOCS III) −0.009 −0.005 0.73
    Model 2
        AL/CR −13.5 −0.8 <0.001
        NO (LOCS III) 0.02 0.01 0.47
40–49 years
    Model 1
        AL −1.81 −0.95 <0.001
        CR 4.2 0.54 <0.001
        NO (LOCS III) 0.18 0.005 0.76
    Model 2
        AL/CR −12.8 −0.84 <0.001
        NO (LOCS III) −0.03 −0.01 0.57
50–59 years
    Model 1
        AL −1.94 −0.97 <0.001
        CR 4.62 0.52 <0.001
        NO (LOCS III) −0.44 −0.04 0.02
    Model 2
        AL/CR −14.1 −0.84 <0.001
        NO (LOCS III) 0.004 0.002 0.93
60–69 years
    Model 1
        AL −1.81 −0.87 <0.001
        CR 4.36 0.52 <0.001
        NO (LOCS III) −0.8 −0.14 <0.001
    Model 2
        AL/CR −13.1 −0.74 <0.001
        NO (LOCS III) −0.28 −0.15 <0.001
70–83 years
    Model 1
        AL −1.5 −0.57 <0.001
        CR 4.42 0.57 <0.001
        NO (LOCS III) −1.12 −0.27 <0.001
    Model 2
        AL/CR −11.5 −0.55 <0.001
        NO (LOCS III) −0.54 −0.31 <0.001
Table 4.
 
Mean Axial Length and Spherical Equivalent in Adults 40–49 Years of Age in Different Population-Based Studies
Table 4.
 
Mean Axial Length and Spherical Equivalent in Adults 40–49 Years of Age in Different Population-Based Studies
Study Ethnicity Measurement of AL Mean SE (Diopters) Mean AL (mm)
Men Women Men Women
The Los Angeles Latino Eye Study 25 Latinos Ultrasound −0.3 −0.3 23.7 23.2
The Mongolian Study 27 Mongolians Ultrasound 0.1 −0.3 23.4 23.0
The Tanjong Pagar Survey 10 Chinese Ultrasound −1.4 −2.1 23.8 23.4
The Meiktila Eye Study 26 Burmese Ultrasound −0.4 −0.6 23.2 22.6
The Singapore Malay Eye Study 12 Malay IOL Master −0.6 −1.1 23.8 23.6
The Singapore Indian Eye Study Indians IOL Master −0.5 −0.8 23.7 23.2
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