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| ORIGINAL ARTICLE |
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| Year : 2016 | Volume
: 23
| Issue : 3 | Page : 127-131 |
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Associations between ocular biometry and anthropometric measurements in a Nigerian population
Sarat Abolore Badmus1, Ayotunde Idowu Ajaiyeoba2, Bernice Oluwakemi Adegbehingbe1, Oluwatoyin Helen Onakpoya1, Adenike Odunmorayo Adeoye1
1 Department of Surgery, College of Health Sciences, Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria
2 Department of Ophthalmology, College of Medicine, University of Ibadan, Oyo State, Nigeria
| Date of Web Publication | 12-Sep-2016 |
Correspondence Address:
Sarat Abolore Badmus
Department of Surgery, College of Health Sciences, Obafemi Awolowo University, Ile-Ife, Osun State
Nigeria
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/1117-1936.190341
Purpose: The study aimed at determining the associations between ocular biometry and anthropometric measurements in a Nigerian adult population.
Subjects and Methods: Participants were healthy members of staff and students of Obafemi Awolowo University Teaching Hospitals Complex, Ile-Ife. The height and weight were measured, and the body mass index (BMI) was calculated. Ocular axial length (AL), anterior chamber depth (ACD), keratometric readings (K) and corneal radius of curvature were measured with the IOL Master. Data were analysed with SPSS version 16 (IBM Corporation), and associations between ocular biometric variables and anthropometric measurements were explored.
Results: Three hundred and fifty healthy participants aged 18–60 years (mean age: 34.8 ± 11.2 years) were enrolled. Height was significantly positively correlated with AL (r = 0.37, P< 0.01) and ACD (r = 0.17, P = 0.01) and negatively correlated with K (r = −0.28, P< 0.01). A significant positive correlation was found between weight and AL (r = 0.13, P = 0.02) while the BMI was only negatively correlated with ACD (r = −0.11, P = 0.04). In multivariate analysis, the relationship between height and AL (R2 = 0.58, P< 0.01) as well as ACD (R2= 0.11, P< 0.01) persisted. The relationship between weight and AL and that between BMI and ACD were totally abolished after controlling for age and height.
Conclusion: The body height is independently associated with ocular AL and ACD while the body weight and BMI are not independently associated with any of the ocular biometric indices studied. Keywords: Anterior chamber depth, axial length, height, ocular biometry, weight
How to cite this article:
Badmus SA, Ajaiyeoba AI, Adegbehingbe BO, Onakpoya OH, Adeoye AO. Associations between ocular biometry and anthropometric measurements in a Nigerian population. Niger Postgrad Med J 2016;23:127-31 |
How to cite this URL:
Badmus SA, Ajaiyeoba AI, Adegbehingbe BO, Onakpoya OH, Adeoye AO. Associations between ocular biometry and anthropometric measurements in a Nigerian population. Niger Postgrad Med J [serial online] 2016 [cited 2023 Mar 29];23:127-31. Available from: https://www.npmj.org/text.asp?2016/23/3/127/190341 |
| Introduction | |  |
Ocular biometry refers to the dimensions of the eye. The type and magnitude of the refractive error of an eye are determined by the relationships between the dimensions of its optical components.[1],[2],[3] The average values of the dimensions of the optical components depend on race, age and gender.[4] Optical anterior chamber depth (ACD) of the eye is defined as the distance from the anterior corneal surface to the anterior lens surface,[5] and axial length (AL) of the eye is defined as the distance between the anterior corneal surface and internal limiting membrane of the retina.[6] Changes in lifestyles, nutrition and ethnic composition of populations lead to changes in the distribution of body dimensions.[7],[8]
When populations share genetic background and environmental factors, average height is frequently characteristic within the group. In regions of extreme poverty or prolonged warfare, environmental factors such as malnutrition during childhood or adolescence may account for marked reductions in adult stature.[7],[9] Body weight is affected by environment, race and genetics.[9],[10] Since similar factors such as race, environment and genetics affect height and weight as well as ocular dimensions, it is likely that there will be some form of relationship between these parameters. Several earlier studies have shown that the ocular AL increases concomitantly with the overall growth and development of the child.[11],[12],[13],[14] Despite this, the relationship between ocular biometry and anthropometrics is not very clear. While AL has been shown to increase with age in children,[12],[14] some studies on adults have shown reduction in AL with age.[4],[15],[16] Furthermore, in another adult population, Mallen et al. did not find any change in AL with age,[17] whereas Nangia et al. have reported increase in AL with increasing age among adults aged 30–100 years.[18]
Studies have suggested that ocular biometric values are related to height [13],[19],[20],[21],[22] and weight.[13],[15] It has been documented that AL of the globe is significantly related to the height of the individual.[13],[20],[20] Extremes of ocular dimensions are related to several pathological ophthalmic conditions, notably refractive error and glaucoma, which cause a large burden of ocular morbidity.[15] Short stature has been associated with narrow anterior chamber angle and a risk factor for angle closure glaucoma.[23],[24] AL and keratometric readings are major measurements in the calculation of intraocular lens power in cataract surgery today. Considering the importance of these ocular biometric variables and the ease of measuring the height and weight, it will be worthwhile to explore any relationship that may exist between them. The aim of this study was to determine the relationship between adult ocular biometric indices and anthropometric measurements in a Nigerian adult population.
| Subjects and Methods | | |
This observational, cross-sectional study was carried out at the Eye Care Centre on healthy volunteers from the staff and student population of Obafemi Awolowo University Teaching Hospitals Complex, Ile-Ife, aged 18 years and older. The Declaration of Helsinki was adhered to throughout the study. The study was conducted between August 2011 and January 2012. Ethical and Research Committee of the Hospital approved the study, and informed consent was obtained from the participants. Individuals with a history of ocular trauma or surgery, current eye diseases except possible refractive errors, limb or spinal deformity, pregnancy and recent weight loss were excluded from the study.
The respondents' age at the last birthday and gender were documented in a predesigned proforma. Participants' height and weight were measured with stadiometer in metres (m) and standard weighing scale in kilogram (kg), respectively, after adjusting for the zero error. The body mass index (BMI) was calculated as weight in kilogram divided by the square of the height in metres (kg/m 2). Distance visual acuity was measured unaided and with pinhole, using an illuminated Snellen chart at 6 m in a well-lit room, one eye at a time. The anterior segment was examined with bright pen torch and slit lamp biomicroscope (Haag-Streit, Switzerland) while the posterior segment was examined with the direct ophthalmoscope (Welch Allyn) by one of the investigators.
All ocular biometric measurements were done by one of the investigators (Sarat Abolore Badmus) for all the participants using the IOL Master (Carl Zeiss Meditec AG, 07740 Jena Germany). Corneal refractive power is measured as keratometric readings in the meridians of the greatest and least radii of corneal curvature. The corneal radius of curvature (CR) was the radius of the central spherical 3–4 mm of the cornea. Horizontal corneal diameter (HCD) is the longest distance from limbus to limbus in the horizontal meridian. Participants were seated comfortably with their chin on the chin rest and their forehead rested firmly on the headband of the IOL Master. They were instructed to constantly fixate on the internal fixation target within the machine. Five measurements per eye were taken each for the AL and the ACD, and the mean values were used in the analysis.[6] The average of three keratometric readings in the greatest and least meridians of corneal radial curvature (K1, K2, respectively) was determined for each eye, and the average keratometric reading (K) was finally calculated as the sum of K1 and K2 divided by two in dioptres. The greatest and least corneal radii of curvature were measured once for each eye, and the average was recorded as the average CR in millimetres. The HCD (white to white in millimetres) was measured once in each eye.[16]
Data obtained were entered into SPSS Statistical Software version 16 (IBM Corporation), explored and analysed. Differences between means were examined using the t-test, and Pearson correlation analysis was performed to show relationship between the various variables. Only measurements in the right eyes were used for analysis because very strong positive correlations exist between the ocular biometric values in the two eyes. A multivariate linear regression model was fitted to explore the influence of age, gender, height, weight and BMI on ocular AL, ACD, average keratometric reading, CR and HCD. Predictor selection was done using the best-fit approach. Level of statistical significance was set at 5% (P < 0.05).
| Results | | |
Three hundred and fifty healthy Nigerian students and staff aged 18–60 years (mean: 34. 8 ± 11.2 years) were enrolled in this study. One hundred and ten (31.43%) were students while 240 (68.57%) were workers. One hundred and sixty-seven (47.7%) of the participants were males and 183 (52.3%) were females, with male:female ratio of 1:1.1. [Figure 1] shows the age and gender distribution of participants. There was no statistically significant difference in age between gender (P = 0.96). | Figure 1: A bar chart showing the age and gender distribution of the 350 participants enrolled in the study
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[Table 1] shows the distribution of the anthropometric measurements and ocular biometric indices by gender. The mean height and weight were significantly higher in males than in females, but the BMI was significantly lower in the males. The mean height in males was 1.70 ± 0.07 m while it was 1.60 ± 0.06 m in females (P < 0.01). All the ocular biometric indices examined were significantly different between the males and females, except the ACD. AL in males versus females was 24.07 ± 0.87 mm versus 23.52 ± 0.87 mm (P < 0.01) while ACD in males versus females was 3.30 ± 0.37 mm versus 3.24 ± 0.31 mm (P = 0.12). | Table 1: Mean anthropometric measurements and ocular biometric variables by gender
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[Table 2] shows the correlation between anthropometric parameters and ocular biometric indices. The correlation between height and AL was 0.37 (P < 0.01) while the correlation between ACD and height was 0.17 (P < 0.01). [Figure 2] is a scatter plot between AL of the eye and body height showing a positive correlation between the two variables. [Figure 3] shows a negative correlation between age and ACD (r = −0.29, P < 0.01). In bivariate analysis, height and weight were both correlated with ocular AL; however, in multivariate regression analysis, after controlling for height, weight ceases to be an independent determinant of AL. Each centimetre increase in height is associated with a 0.04 mm increase in AL (95% confidence interval [CI]: 0.03–0.05 mm). R2 = 0.58, P < 0.01, implying that height accounted for 58% of the AL. | Table 2: Correlation between age, height, weight, body mass index and ocular biometric variables
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 | Figure 2: Scatter plot showing the correlation between axial length and body heightweight
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 | Figure 3: Scatter plot showing correlation between anterior chamber depth against age
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While age and BMI showed a negative correlation with ACD, height showed a positive correlation. The relationship between ACD and BMI was totally abolished in multivariate analysis controlling for age and height. The relationship between ACD and both age and height persisted in multivariate analysis although age was more highly related to ACD than height. For every 10-year increase in age, there is a corresponding reduction in ACD of 0.10 mm (95% CI: 0.07–0.13 mm, P < 0.01) while every centimetre increase in height is associated with an increase in the ACD by 0.01 mm (95% CI: 0.00–0.01 mm, P < 0.01). R2 = 0.11, P < 0.01 implying that height account for about 11% of the variability in ACD.
The effect of age on average keratometric reading persisted after controlling for height. Each 10-year increase in age increases K by 0.18D (95% CI: 0.04–0.32D, P = 0.01) while 1 cm increase in height reduces K by 0.05D (95% CI: 0.03–0.07D, P < 0.01). Regression also showed that 1 cm increase in height increases CR by 0.01 mm (95% CI: 0.01–0.01, P < 0.01).
AL was significantly associated with all other ocular biometric variables in bivariate analysis. A regression model was fitted, in which AL was the dependent variable and height and all the other biometric indices were the predictors. The relationship between ACD and AL and height and AL persisted, but there was attenuation in the relationship between height and AL. One centimetre increase in height now increases AL by 0.02 mm (95% CI: 0.01–0.02, P < 0.01). One millimetre increase in ACD is associated with 0.86 mm increase in AL (95% CI: 0.66–1.06 mm, P < 0.01). The other ocular biometric parameters were no longer significantly associated with AL.
| Discussion | | |
Gender appears to be a determinant of most ocular biometric indices examined in this cross-sectional study. Both the anthropometric parameters and the ocular indices were significantly different between males and females, except the ACD. Although the ACD was higher in men than in women, this difference was not statistically significant. Our finding of no significant gender difference in ACD is in contrast to many earlier studies which have identified the female gender as a risk factor for shallow anterior chamber [15],[21],[25],[26] but in agreement with the findings of our study are other researchers who have also found no gender variation in ACD.[4],[27] Our result on the relationship between ACD and gender may be similar to the finding of Hsu et al. in Indians, where multivariate analysis showed no independent relationship between ACD and gender.[28]
The AL, average CR and HCD were higher in males while the average keratometric reading was lower in males. This interaction between AL and average keratometric reading which were both key factors in final refractive status of the eye confirms the work of earlier workers [1],[2] that for final emmetropia, a delicate balance between the different optical components of the eye is imperative. Our study confirms the earlier findings of higher AL in males than in females [15],[16],[21],[29] although this is in contrast with the findings in the Alaskan Eskimos [4] and the rural Indian,[18] in which no significant gender difference was demonstrable in the AL. The present study confirms the positive relationship that has been found consistently between AL of the globe and height.[13],[15],[18],[20],[21],[22],[27] Our finding of positive correlation between anterior chamber depth and height may be comparable to the finding in Beijing Eye study in which shorter subjects had shallower peripheral anterior chamber.[30]
Although weight was also positively related to AL of the globe in bivariate analysis, this is to a lesser extent than the relationship between height and AL in this study. This relationship between weight and AL ceased to exist after controlling for age, gender and height. This relationship between weight and AL is similar to the findings in Reykjavik Eye study [21] and the Central Rural Indians.[18] Age does not seem to affect the distribution of AL in this adult Nigerian population. It is possible that the anteroposterior growth of the globe has been stabilised in most of the subjects because this is an adult study. In support of this, there are earlier studies in which AL was found to have stabilised between the ages of 10 and 15 years.[12],[14] Some other studies in which AL was found to reduce with age have attributed it to possible cohort effect and not necessarily a longitudinal effect.[18],[25] The reduction in ACD with age is understandable because the crystalline lens has been documented to continuously increase in size throughout life, thus causing gradual progressive shallowing of the anterior chamber.[21],[25]
The average keratometric reading K, CR, and HCD are all corneal attributes; however, while K increases with age and HCD reduces with age, there was no significant relationship between age and CR. The reason for this discrepancy is not immediately apparent, but a longitudinal study may be required to rule out a cohort effect since an earlier study has found that K does not increase significantly after the age of 6 months.[12] Of the studied anthropometric parameters in this study, height appears to be the most relevant association with ocular biometric indices and it was associated with all the ocular indices studied. Similar to earlier findings in other studies, height is positively correlated with AL.[13],[15],[18],[20],[21],[22],[27] While AL, ACD, CR and HCD were positively correlated with height, only K had a negative correlation. The AL and K are key determinants of the final refractive status, which usually tends towards emmetropia. It may then make sense that taller subjects with increased AL would have reduced K to ensure an emmetropic or near-emmetropic balance. This is further corroborated by the strong inverse correlation between AL and K in this study.
| Conclusion | | |
This study has shown that there is no significant gender difference in the ACD of the participants. The body height is independently associated with ocular AL and the ACD while the body weight and the BMI are not independently associated with any of the ocular biometric indices studied.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Grosvenor T. High axial length/corneal radius ratio as a risk factor in the development of myopia. Am J Optom Physiol Opt 1988;65:689-96.  |
| 2. | Brown NP, Koretz JF, Bron AJ. The development and maintenance of emmetropia. Eye (Lond) 1999;13(Pt 1):83-92. |
| 3. | Lyhne N, Sjølie AK, Kyvik KO, Green A. The importance of genes and environment for ocular refraction and its determiners: A population based study among 20-45 year old twins. Br J Ophthalmol 2001;85:1470-6. |
| 4. | Wojciechowski R, Congdon N, Anninger W, Teo Broman A. Age, gender, biometry, refractive error, and the anterior chamber angle among Alaskan Eskimos. Ophthalmology 2003;110:365-75. |
| 5. | Vetrugno M, Cardascia N, Cardia L. Anterior chamber depth measured by two methods in myopic and hyperopic phakic IOL implant. Br J Ophthalmol 2000;84:1113-6. |
| 6. | Ojaimi E, Rose KA, Morgan IG, Smith W, Martin FJ, Kifley A, et al. Distribution of ocular biometric parameters and refraction in a population-based study of Australian children. Invest Ophthalmol Vis Sci 2005;46:2748-54. |
| 7. | Beard AS, Blaser MJ. The ecology of height: The effect of microbial transmission on human height. Perspect Biol Med 2002;45:475-98. |
| 8. | Kapinos KA, Yakusheva O. Environmental influences on young adult weight gain: Evidence from a natural experiment. J Adolesc Health 2011;48:52-8. |
| 9. | Stunkard AJ, Harris JR, Pedersen NL, McClearn GE. The body-mass index of twins who have been reared apart. N Engl J Med 1990;322:1483-7. |
| 10. | Okosun IS, Cooper RS, Rotimi CN, Osotimehin B, Forrester T. Association of waist circumference with risk of hypertension and type 2 diabetes in Nigerians, Jamaicans, and African-Americans. Diabetes Care 1998;21:1836-42. |
| 11. | Larsen JS. The sagittal growth of the eye. IV. Ultrasonic measurement of the axial length of the eye from birth to puberty. Acta Ophthalmol (Copenh) 1971;49:873-86. |
| 12. | Gordon RA, Donzis PB. Refractive development of the human eye. Arch Ophthalmol 1985;103:785-9. |
| 13. | Saw SM, Chua WH, Hong CY, Wu HM, Chia KS, Stone RA, et al. Height and its relationship to refraction and biometry parameters in Singapore Chinese children. Invest Ophthalmol Vis Sci 2002;43:1408-13. |
| 14. | Song HT, Kim YJ, Lee SJ, Moon YS. Relations between age, weight, refractive error and eye shape by computerized tomography in children. Korean J Ophthalmol 2007;21:163-8. |
| 15. | Foster PJ, Broadway DC, Hayat S, Luben R, Dalzell N, Bingham S, et al. Refractive error, axial length and anterior chamber depth of the eye in British adults: The EPIC-Norfolk Eye Study. Br J Ophthalmol 2010;94:827-30. |
| 16. | Fotedar R, Wang JJ, Burlutsky G, Morgan IG, Rose K, Wong TY, et al. Distribution of axial length and ocular biometry measured using partial coherence laser interferometry (IOL Master) in an older white population. Ophthalmology 2010;117:417-23. |
| 17. | Mallen EA, Gammoh Y, Al-Bdour M, Sayegh FN. Refractive error and ocular biometry in Jordanian adults. Ophthalmic Physiol Opt 2005;25:302-9. |
| 18. | Nangia V, Jonas JB, Sinha A, Matin A, Kulkarni M, Panda-Jonas S. Ocular axial length and its associations in an adult population of central rural India: The Central India Eye and Medical Study. Ophthalmology 2010;117:1360-6. |
| 19. | Teasdale TW, Goldschmidt E. Myopia and its relationship to education, intelligence and height. Preliminary results from an on-going study of Danish draftees. Acta Ophthalmol 1988;185:41-3. |
| 20. | Wong TY, Foster PJ, Johnson GJ, Klein BE, Seah SK. The relationship between ocular dimensions and refraction with adult stature: The Tanjong Pagar Survey. Invest Ophthalmol Vis Sci 2001;42:1237-42. |
| 21. | Eysteinsson T, Jonasson F, Arnarsson A, Sasaki H, Sasaki K. Relationships between ocular dimensions and adult stature among participants in the Reykjavik Eye Study. Acta Ophthalmol Scand 2005;83:734-8. |
| 22. | Wu HM, Gupta A, Newland HS, Selva D, Aung T, Casson RJ. Association between stature, ocular biometry and refraction in an adult population in rural Myanmar: The Meiktila eye study. Clin Experiment Ophthalmol 2007;35:834-9. |
| 23. | Xu L, Li J, Wang Y, Jonas JB. Anthropomorphic differences between angle-closure and open-angle glaucoma: The Beijing Eye Study. Acta Ophthalmol Scand 2007;85:914-5. |
| 24. | Xu L, Cao WF, Wang YX, Chen CX, Jonas JB. Anterior chamber depth and chamber angle and their associations with ocular and general parameters: The Beijing Eye Study. Am J Ophthalmol 2008;145:929-36. |
| 25. | Hashemi H, Khabazkhoob M, Miraftab M, Emamian MH, Shariati M, Abdolahinia T, et al. The distribution of axial length, anterior chamber depth, lens thickness, and vitreous chamber depth in an adult population of Shahroud, Iran. BMC Ophthalmol 2012;12:50. |
| 26. | Jonas JB, Nangia V, Gupta R, Khare A, Sinha A, Agarwal S, et al. Anterior chamber depth and its associations with ocular and general parameters in adults. Clin Experiment Ophthalmol 2012;40:550-6. |
| 27. | Lee KE, Klein BE, Klein R, Quandt Z, Wong TY. Association of age, stature, and education with ocular dimensions in an older white population. Arch Ophthalmol 2009;127:88-93. |
| 28. | Hsu WC, Shen EP, Hsieh YT. Is being female a risk factor for shallow anterior chamber? The associations between anterior chamber depth and age, sex, and body height. Indian J Ophthalmol 2014;62:446-9. [ PUBMED]  |
| 29. | Fanny A, Ouattara A, Aka J, Coulibaly F, Gbé K, Boni S, et al. Ocular biometric values of the black African patient and theoretical consideration of the role of these values in various pathologies: Analysis of 325 eyes. J Fr Ophtalmol 2007;30:68-72. |
| 30. | Xu L, Li JJ, Xia CR, Wang YX, Jonas JB. Anterior chamber depth correlated with anthropomorphic measurements: The Beijing Eye Study. Eye (Lond) 2009;23:632-4. |
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2]
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