October 2011
Volume 52, Issue 11
Free
Clinical and Epidemiologic Research  |   October 2011
Intraocular Pressure and Corneal Biomechanics in an Adult British Population: The EPIC-Norfolk Eye Study
Author Affiliations & Notes
  • Paul J. Foster
    From the National Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital, and Division of Genetics and Epidemiology, UCL Institute of Ophthalmology, London, United Kingdom;
  • David C. Broadway
    the Department of Ophthalmology, Norfolk and Norwich University Hospital, Norwich, United Kingdom;
  • David F. Garway-Heath
    From the National Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital, and Division of Genetics and Epidemiology, UCL Institute of Ophthalmology, London, United Kingdom;
  • Jennifer L. Y. Yip
    the Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom; and
  • Robert Luben
    the Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom; and
  • Shabina Hayat
    the Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom; and
  • Nichola Dalzell
    the Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom; and
  • Nicholas J. Wareham
    the MRC (Medical Research Council) Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom.
  • Kay-Tee Khaw
    the Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom; and
  • Corresponding author: Paul J. Foster, Division of Genetics and Epidemiology, UCL Institute of Ophthalmology, 11-43 Bath Street, London EC1V 9EL, UK; p.foster@ucl.ac.uk
Investigative Ophthalmology & Visual Science October 2011, Vol.52, 8179-8185. doi:10.1167/iovs.11-7853
  • Views
  • PDF
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Paul J. Foster, David C. Broadway, David F. Garway-Heath, Jennifer L. Y. Yip, Robert Luben, Shabina Hayat, Nichola Dalzell, Nicholas J. Wareham, Kay-Tee Khaw; Intraocular Pressure and Corneal Biomechanics in an Adult British Population: The EPIC-Norfolk Eye Study. Invest. Ophthalmol. Vis. Sci. 2011;52(11):8179-8185. doi: 10.1167/iovs.11-7853.

      Download citation file:


      © 2016 Association for Research in Vision and Ophthalmology.

      ×
  • Supplements
Abstract

Purpose.: To describe the distribution and determinants of intraocular pressure (IOP) and indices of corneal biomechanics in an adult British population.

Methods.: Goldmann-equivalent IOP (IOPg), corneal mechanical characteristics (corneal hysteresis, CH; corneal resistance factor, CRF), and IOP adjusted for corneal factors (IOPcc) were measured. Ocular biometric characteristics were also measured in 4184 consecutive individuals aged 48 to 91 years recruited from the European Prospective Investigation into Cancer (EPIC)-Norfolk cohort. Sociodemographic data were recorded with a standardized questionnaire. Blood pressure and anthropometric data were recorded by trained staff according to a standard protocol.

Results.: Mean IOP was similar to that reported in previous United Kingdom population studies (IOPg: 16.0 mm Hg, SD 3.68). These data confirmed systolic blood pressure as the major identifiable correlate of IOP. There was a significant positive association between IOP and axial length of the eye. The IOPg, but not IOPcc, was higher in the women than in the men. No difference in IOP between the different age groups was identified. CRF and CH varied with IOPg, age, sex, and axial length of the eye.

Conclusions.: The study provided current population-based values for IOP and corneal biomechanical parameters. Mean IOP in this British population was very similar to levels previously reported over 40 years ago. There was no identifiable relationship between IOP and age within this cohort with an older age range, in contrast to previous studies. Systolic blood pressure was identified as the major correlate of IOP.

Intraocular pressure (IOP) is the cardinal known modifiable risk factor for glaucoma. 1 3 Glaucoma is the second commonest cause of blindness globally. 4 The prevalence of glaucomatous optic neuropathy increases exponentially with increases in IOP. 5 In 1966 Hollows and Graham 6 published data on IOP distribution in 4042 people aged 40 years and older living in the Rhondda Valley in South Wales. IOP was conspicuously higher in women and older people. Perkins 7 subsequently described IOP in 1466 people aged 40 years and older living in Bedford, central-southern England in 1973. IOP in a large U.K. population has not been studied since the 1970s. 
It has long been recognized that clinical methods of assessing IOP are subject to various sources of error 8 and may suffer inaccuracies as great as 30% of the population mean IOP. 9 In particular, biomechanical properties of the cornea have attracted the attention of researchers as an important source of measurement error. 10 The central cornea is thicker in people with ocular hypertension, compared with those with glaucoma, or those without glaucoma who have normal IOP. 11 Ehlers et al. 9 reported a linear relationship between clinical measures of IOP and central corneal thickness (CCT). However, CCT is a relatively crude surrogate for the complex biomechanical properties of ocular connective tissues that influence the clinical estimates of IOP. 10,12 Since 2005, a new noncontact tonometer, the Ocular Response Analyzer (ORA; Reichert, Inc., Depew, NY), has been promoted as a means of measuring the biomechanical properties of the cornea, in addition to determining IOP, while adjusting for the confounding effects of the physical properties of ocular connective tissues. IOP measurements made with the ORA have been found to have only a weak association with CCT. 13,14  
Aside from corneal factors, IOP has been reported to be associated with a variety of sociodemographic, anthropomorphic, and lifestyle factors, including age, sex, systemic blood pressure (BP), body mass index, diabetes history, family history of glaucoma, alcohol use, and smoking. These nonocular factors explain <10% of the variation in IOP. 15 17 IOP also varies with geographic location and racial background for the population under study. African people living in the Caribbean tend to have higher mean IOP than their neighbors of European origin (18.7 vs. 16.5 mm Hg). 18 Among continental Europeans, mean IOPs of 15.3 and 15.5 mm Hg have been reported in Greece and Northern Italy. 19,20 In turn, East Asian people tend to have significantly lower IOP than other racial groups. The population with the lowest recorded adult IOPs is that of rural Mongolia, among whom the mean adult IOP has been reported to be as low as 12.6 mm Hg. 21 The Tajimi Eye Study Group in Japan recently reported a mean IOP of 14.5 mm Hg, 22 very similar to data reported for Chinese Singaporeans (14.7 mm Hg). 17  
In this article, we report the distribution of IOP and present the first population-based measures of corneal biomechanics in an older British population resident in and around the city of Norwich in the East Anglia region of England. 
Methods
The European Prospective Investigation into Cancer (EPIC) was conceived as a pan-European study of the genetic and environmental determinants of cancer. 23 The EPIC-Norfolk cohort comprised approximately 25,000 men and women aged 40 to 79 recruited between 1993 and 1997 and was predominantly white, with a mixture of urban–rural residence, socioeconomic standards, and educational achievements reflecting the population of the county of Norfolk. From the outset, data collection was expanded to include extensive lifestyle and biological data to enable a broader longitudinal study of the determinants of health and disease. 24 A third health examination was initiated in 2006 with the purpose of assessing objectively various physical, cognitive, and ocular characteristics of participants now aged 48 to 91 years. The third health examination was reviewed and approved by the East Norfolk and Waverney NHS Research Governance Committee (2005EC07L) and the Norfolk Research Ethics Committee (05/Q0101/191). The work was performed in accordance with the principles of the Declaration of Helsinki. Sex and age at time of examination were recorded. Participants completed health and lifestyle questionnaires at baseline and at follow-up time points. Sociodemographic data were derived from the baseline questionnaire from 1993 through 1997. Social class was classified according to the Registrar General's occupation-based system, identifying five main categories: class I, professionals; class II, managerial and technical occupations; class III, nonmanual and manual skilled workers; class IV, partially skilled workers; and class V, unskilled manual workers. Those in social classes I, II, and III nonmanual were nonmanual workers, whereas those in social classes III-manual, IV, and V were manual workers. Educational attainment was based on highest qualification attained and was categorized into four groups: degree or equivalent, A-level or equivalent (i.e., education to 17 years), O-level or equivalent (i.e., education to 15 years), and less than O-level or no qualifications. Based on previous analyses of the association between health and educational attainment in this older cohort, we used a cutoff point of school completion to 15 years (O-level) to group individuals. 25 Height and weight were measured by trained nurses, for participants wearing light-weight clothing and no shoes, according to standard protocols. Height was measured to the nearest 0.1 cm with a stadiometer, whereas weight was measured to the nearest 0.1 kg with digital weighing scales (Tanita UK, Ltd., Yiewsley, UK). Systolic and diastolic blood pressures (sBP and dBP, respectively) were measured (Accutorr Plus; Datascope Patient Monitoring, Mindray UK, Ltd., Huntington, UK). Mean arterial pressure (MAP) and pulse amplitude (P-AMP) were derived accordingly; MAP = dBP +1/3(sBP dBP), P-AMP = sBP − dBP. 
IOP was measured three times in each eye using a noncontact ORA (Reichert) to generate IOPg. The ORA uses a short (20-ms) pulse of air to applanate and slightly indent the cornea, the events being recorded by an electro-optical system. The two specific stages recorded are (1) when the cornea is applanated while moving inward and (2) at applanation as the cornea is returning to its resting state. The average of inward (P1) and outward (P2) applanation forces is calibrated to be equivalent to IOPg. The cornea offers greater resistance to the air pulse at the beginning of the process of deformation, slightly delaying inward movement. In contrast, the applanation event during the relaxation phase occurs at a lower air pulse pressure. The difference in air pulse pressure between the two applanation events results is termed the corneal hysteresis (CH). The instrument manufacturers believe this to be a new measurement of corneal tissue properties that is a result of viscous damping in the corneal tissue. Corneal resistance factor (CRF) is promoted as an indicator of the overall resistance or rigidity of the cornea. The values of CH and CRF are used to calculate IOP, corrected for corneal biomechanics (IOPcc), using a proprietary formula. Mean values of IOPg, IOPcc, CH, and CRF were calculated for each eye, and values for right eyes were arbitrarily selected for presentation, since no difference in laterality was detected. 
Differences between men and women were tested using Student's t-test. Associations between age and IOP, CH, and CRF were tested using linear regression analysis. 
Results
IOP and corneal biomechanical data were obtained from 4184 consecutive study participants aged 48 to 91 years (56.2% women). Table 1 summarizes key characteristics of the cohort. IOP variables (IOPg, IOPcc) showed a Gaussian distribution with a typically exaggerated central peak (leptokurtotic) and right-handed skew. IOP was measured between the hours of 07.46 and 19.41. Mean IOPg was highest between 10.00 and 10.59 hours, and lowest after 19.00 hours, varying by 1.14 mm Hg. A seasonal variation was also observed, with mean IOPg being lowest in summer and highest in the winter, differing by 0.35 mm Hg. Corneal biomechanical measures (CRF and CH) were fully Gaussian. Mean IOPg was 16.0 mm Hg in men and women (P = 0.56). Although no association between IOP and age was identified on linear regression (P = 0.54), a modest increase in mean IOP from the late 40s to the age of 79 years (0.25 mm Hg) was observed in the men, but not in the women in whom IOP peaked in the 60s. The IOPcc was slightly higher in men (16.7 vs. 16.5; P = 0.053) and showed a small difference between decade age groups (increasing 0.2 mm Hg/decade, P = 0.004). Both CRF and CH were significantly higher in the women than in the men (10.4 vs. 10.02, P < 0.001; 10.2 vs. 9.79, P < 0.001, respectively). The CRF declined significantly with age at a rate of 0.31 mm Hg/decade (P < 0.001), as did CH by 0.34 mm Hg/decade (P < 0.001). Table 2 gives age- and sex-specific data for IOP, CRF and CH. The significance of the age-related variations is discussed below. 
Table 1.
 
Demographic, Systemic, and Ophthalmic Characteristics of Participants
Table 1.
 
Demographic, Systemic, and Ophthalmic Characteristics of Participants
Age (y)
48–59 60–69 70–79 80+ 48–91
Women, % 61.3 58.5 52.6 47.2 55.7
Nonmanual workers, % 32.9 37.9 35.1 23.8 34.8
Education, % at least O-level 69.2 61.5 56.3 57.6 60.4
Never smoked, % 50.4 46.1 48.6 40.2 46.8
Height, m 167.8 166.9 165.7 163.7 166.3
Weight, kg 75.0 75.4 74.3 71.5 74.6
BMI, kg/m2 26.6 27.0 27.0 26.9 26.9
Waist, cm 90.0 93.4 95.2 96.3 93.9
Hip/waist ratio 0.86 0.88 0.90 0.91 0.89
Systolic BP, mm Hg 130.4 134.8 138.5 140.4 136.0
Diastolic BP, mm Hg 79.3 79.5 77.6 75.9 78.5
Mean arterial pressure, mm Hg 96.3 97.9 97.9 97.4 97.7
Pulse amplitude, mm Hg 51.1 55.3 60.8 64.5 57.5
Birth weight, kg 3.33 3.35 3.32 3.46 3.35
Anterior chamber depth, mm 3.09 3.06 3.00 2.96 3.04
Axial length, mm 23.01 23.29 23.13 22.69 23.15
Refractive error, D −0.63 0.08 0.60 0.41 0.20
Table 2.
 
IOP and Corneal Biomechanics by Age and Sex
Table 2.
 
IOP and Corneal Biomechanics by Age and Sex
Age (y) Sex Mean IOPg* (SD) Mean IOPcc* (SD) CRF (SD) CH (SD)
Men
    48–59 189 15.82 (3.36) 16.19 (3.08) 10.44 (1.83) 10.32 (1.62)
    60–69 783 15.95 (3.80) 16.64 (3.92) 10.10 (1.82) 9.89 (1.62)
    70–79 638 16.07 (3.86) 16.89 (4.07) 9.95 (1.68) 9.66 (1.73)
    80+ 220 15.86 (4.21) 16.87 (4.28) 9.62 (1.71) 9.35 (1.67)
    48–91 1831 15.96 (3.89) 16.71 (3.95) 10.02 (1.77) 9.79 (1.69)
Women
    48–59 321 15.64 (3.58) 15.88 (3.42) 10.55 (1.76) 10.51 (1.61)
    60–69 1127 16.28 (3.60) 16.60 (3.52) 10.57 (1.67) 10.31 (1.56)
    70–79 712 15.99 (3.53) 16.64 (3.66) 10.15 (1.62) 9.93 (1.52)
    80+ 193 15.39 (3.22) 16.25 (3.28) 9.80 (1.77) 9.74 (1.62)
    48–91 2353 16.03 (3.55) 16.49 (3.54) 10.38 (1.69) 10.18 (1.58)
Men and women
    48–91 4184 16.00 (3.68) 16.58 (3.72) 10.22 (1.74) 10.00 (1.64)
IOPg and IOPcc correlated strongly (Pearson r = 0.915, P < 0.001). Mean IOPcc was greater than mean IOPg by 0.57 ± 1.53 (SD) mm Hg (95% CI: 0.53–0.62). The IOPg − IOPcc difference (Δ) showed a Gaussian distribution with some leptokurtosis. The 95% limits of agreement 26 for ΔIOPg − IOPcc were +2.41 to −3.57 mm Hg (Δ range: +8.7 to −35.6 mm Hg). This difference (IOPg − IOPcc) was greater in older people than among the younger participants: −0.27 mm Hg in people aged <60 years compared with −1.02 mm Hg in people aged 80 years and older. A scatterplot (Fig. 1) shows IOPg versus IOPcc. Table 3 summarizes differences between IOPg and IOPcc by age and sex. 
Figure 1.
 
A scatterplot of IOPg against IOPcc from the Ocular Response Analyzer (Reichert, Depew, NY). Mean difference (IOPg − IOPcc) was −0.58 mm Hg (SD 1.52). The two measures are highly correlated (Pearson r = 0.915; P < 0.001). There were five persons in whom the IOPg − IOPcc was > −10 mm Hg. The 95% limits of agreement for the two measures were +2.41 to −3.57 mm Hg.
Figure 1.
 
A scatterplot of IOPg against IOPcc from the Ocular Response Analyzer (Reichert, Depew, NY). Mean difference (IOPg − IOPcc) was −0.58 mm Hg (SD 1.52). The two measures are highly correlated (Pearson r = 0.915; P < 0.001). There were five persons in whom the IOPg − IOPcc was > −10 mm Hg. The 95% limits of agreement for the two measures were +2.41 to −3.57 mm Hg.
Table 3.
 
Differences between IOPg and IOPcc
Table 3.
 
Differences between IOPg and IOPcc
Age (y) Sex IOPg − IOPcc* SD (IOPg − IOPcc) 95% CI Difference (IOPg − IOPcc)
48–59 M: 189 −0.37 (−4.33 to 3.93) 1.23 −0.54 to −0.19
F: 321 −0.23 (−5.47 to 7.70) 1.32 −0.37 to −0.85
60–69 M: 783 −0.70 (−35.57 to 5.13) 1.86 −0.83 to −0.57
F: 1127 −0.32 (−5.73 to 7.87) 1.21 −0.39 to −0.25
70–79 M: 638 −0.82 (−32.27 to 5.90) 1.39 −1.19 to −0.82
F: 712 −0.65 (−23.83 to 3.03) 1.45 −0.76 to −0.54
80+ M: 220 −1.01 (−8.27 to 8.67) 1.39 −1.19 to −0.82
F: 193 −0.86 (−12.57 to −0.64) 1.54 −1.07 to −0.64
48–91 M: 1831 −0.74 (−35.57 to 8.67) 1.72 −0.82 to −0.66
F: 2353 −0.45 (−23.83 to 7.87) 1.34 −0.51 to −0.40
48–91 M & F: 4184 −0.57 (−35.57 to 8.67) 1.53 −0.62 to −0.53
Table 4 gives details of univariate regression of sociodemographic, systemic, and anthropomorphic factors plausibly associated with IOP and corneal biomechanics. Given the number of statistical tests performed in this table, we adopted a threshold of P < 0.001 for rejection of the null hypothesis. IOPg was associated with measures of BP, axial length, and refractive error. IOPcc showed the same associations with very similar regression coefficients, implying that these were genuine associations with IOP and not the product of variations in corneal biomechanics. The IOPcc also showed borderline associations with age and sex (slightly higher in older people and men). Both CH and CRF showed strong statistical associations with age, sex, height, hip/waist ratio, and sBP. The CH was inversely associated with waist circumference and axial length of the globe. No differences in IOP relative to education (at least O-level qualifications versus not: 15.9 vs. 16.0 mm Hg, P = 0.57, unpaired t-test) were observed. People previously or currently in a nonmanual job had a slightly higher IOP than those performing manual work (16.1 vs. 15.8 mm Hg; P = 0.012, unpaired t-test). 
Table 4.
 
Univariate Regression Coefficients (β) of Sociodemographic, Systemic, Physical Factors Plausibly Associated with IOP and Corneal Biomechanics
Table 4.
 
Univariate Regression Coefficients (β) of Sociodemographic, Systemic, Physical Factors Plausibly Associated with IOP and Corneal Biomechanics
IOPg IOPcc CRF CH
Age, decade −0.043 (0.54) 0.202 (0.004) −0.306 (<0.001)* 0.340 (<0.001)*
Sex† 0.064 (0.57) −0.224 (0.053) 0.351 (<0.001)* 0.387 (<0.001)*
Social class‡ −0.008 (0.18) −0.004 (0.57) 0.006 (0.048) −0.004 (0.18)
Classes I–V
Nonmanual versus manual workers§ 0.30 (0.012) 0.292 (0.016) −0.075 (0.19) 0.016 (0.77)
Education‖ −0.032 (0.55) −0.030 (0.57) −0.002 (0.92) 0.006 (0.80)
At least O-level, versus no O-levels −0.065 (0.57) −0.036 (0.76) −0.032 (0.56) −0.020 (0.70)
Height, m −0.006 (0.37) 0.005 (0.42) 0.013* (<0.001)* 0.014* (<0.001)*
Weight, kg 0.003 (0.40) 0.006 (0.16) −0.002 (0.26) 0.004 (0.039)
BMI, kg/m2 0.006 (0.57) 0.005 (0.62) 0.001 (0.81) −0.001 (0.86)
Waist, cm 0.002 (0.66) 0.007 (0.14) 0.005 (0.020) 0.007 (0.001)*
Hip/waist ratio −0.335 (0.62) 0.936 (0.18) 1.474 (<0.001)* 1.584 (<0.001)*
sBP, per 10 mm Hg 0.37 (<0.001)* 0.38 (<0.001)* 0.06 (0.001)* 0.06 (<0.001)*
dBP, per 10 mm Hg 0.48 (<0.001)* 0.49 (<0.001)* 0.09 (0.003) 0.07 (0.011)
MAP, per 10 mm Hg 0.55 (<0.001)* 0.56 (<0.001)* 0.09 (<0.001)* 0.08 (=0.001)
P-AMP, per 10 mm Hg 0.32 (<0.001)* 0.33 (<0.001)* 0.05 (0.032) 0.06 (0.005)
Birth weight, kg 0.017 (0.88) 0.124 (0.26) −0.096 (0.072) 0.120 (0.014)
Smoking status, ever vs. never¶ −0.043 (0.72) −0.071 (0.56) −0.038 (0.51) −0.024 (0.66)
Anterior chamber depth, mm 0.091 (0.41) 0.159 (0.16) −0.065 (0.22) 0.106 (0.033)
Axial length, mm 0.258 (<0.001)* 0.366 (<0.001)* 0.052 (0.011) 0.153 (<0.001)*
Refractive error, D # 0.130 (<0.001)* 0.141 (<0.001)* −0.016 (0.17) 0.027 (0.015)
In a forward stepwise multiple linear regression of variables identified as significantly associated with IOPg (adjusted for age and sex), sBP (beta: 0.37/10 mm Hg; P < 0.001), axial length of the globe (beta: 0.29/mm; P < 0.001) and sex (0.32 mm Hg higher in women) were significantly and independently associated with IOPg. In a similar regression for predictors of IOPcc, only sBP (0.38/10 mm Hg; P < 0.001) and axial length of the globe (0.35/mm; P < 0.001) were significantly linked with IOPcc. 
Multiple regression of predictor variables (Table 5) on CRF identified age (−0.34 mm Hg/decade; P < 0.001), height (−0.08 mm Hg/10 cm; P = 0.039), sBP (0.09 mm Hg/10 mm Hg; P < 0.001), and sex (0.20 mm Hg women > men; P = 0.01) as independently associated with corneal resistance. Similar results were obtained from a regression of predictors on CH; age (−0.33 mm Hg/decade; P < 0.001), sex (0.29 mm Hg women > men), sBP (0.09 mm Hg/10 mm Hg; P < 0.001), and axial length (0.04 mm Hg/mm; P = 0.039). Since IOP itself may plausibly affect the dynamic properties of the cornea, 27 IOPg was included as a predictor variable in the two previous analysis. In the case of CRF, IOPg showed a stronger influence on corneal resistance than any other factor (0.28 mm Hg/mm Hg; P < 0.001). In this iteration of the model, age, axial length, and sex remained significant independent predictors of CRF (all P < 0.001; R 2 = 0.37), although sBP and height were no longer associated. Similarly, IOPg was significantly associated with CH (−0.022 mm Hg; P = 0.002), with age, sex and axial length all associated with statistical significance (P < 0.001). Systolic BP was not associated (P = 0.929) with CH, once IOPg was included in the model. 
Table 5.
 
Multiple Regression of Predictor Variables on Goldmann-Equivalent IOPg
Table 5.
 
Multiple Regression of Predictor Variables on Goldmann-Equivalent IOPg
Regression Coefficents (SE) 95% CI Standardized β P
sBP, per 10 mm Hg 0.396 (0.038) 0.171 <0.001
0.322 to 0.470
Axial length, mm 0.271 (0.044) 0.100 <0.001
0.185 to 0.358
Sex, M = 1, F = 2 0.251 (0.120) 0.034 0.035
0.018 to 0.488
Age, per decade −0.153 (0.073) −0.038 0.035
−0.295 to −0.010
Nonmanual versus manual workers −0.251 (0.122) −0.033 0.039
−0.489 to −0.013
Discussion
In this study of a large community-based cohort living in eastern England, we found the mean population IOP to be 16 mm Hg, in keeping with data from earlier studies in south Wales and central England (16.3 and 15.9 mm Hg). 6,7 No differences in IOP associated with height, weight, body mass index, birth weight, smoking status or education were identified. In a multiple regression analysis, it was found that IOP was higher in younger people, women, those with higher BP, and those who had a sedentary occupation. With respect to age, a modest rise in mean IOP from the late 40s to the age of 79 years (0.25 mm Hg) was observed in the men, but not in the women, in whom IOP peaked in the 60s. Similar associations for BP and cholesterol have been reported previously. 28 The findings of the present study contrast remarkably with those of a previous large U.K. study, in which applanation IOP rose from 15.8 to 16.3 in the men and from 15.6 to 17.2 mm Hg in the women who were aged between 40 and 80 years (Fig. 2). 29 In common with our study, IOP in the Rhondda study was found to be higher in women than in men, although the difference between the sexes in Norfolk appeared to be less pronounced. 6 In most cross-sectional studies, mean IOP has been reported to be higher in older people. Other notable exceptions to the trend for IOP to rise with age come from Japan, Mongolia, and Ireland, where generally IOP appeared to fall with increasing age. 21,30,31 However, cross-sectional data may mask longitudinal trends in physiological characteristics that vary between birth cohorts. A longitudinal study of IOP in 69,643 Japanese people aged 20 to 79 years of age showed that, although cross-sectional data suggested a decline in IOP with advancing age, there was a significant increase in IOP in all age groups over a period of 10 years. 32 As yet, it is unclear whether this is a manifestation of survival bias, where people with lower IOP may have survival advantage, or a result of the smaller number of older people contributing data to the analyses, with a consequent reduction in precision and power. It is highly likely that the participants of the present study who remained most healthy for the duration of investigation were more likely to attend for repeat health checks, and hence our data are probably more representative of the more healthy members of the population. However, we have presented data on a large number of older people, aged over 80. It is possible that IOP increases until the age of 70 years and then declines, but the smaller number of participants has led to insufficient power to identify this U-shaped trend. 
Figure 2.
 
A comparison of age and sex-specific IOP in the EPIC-Norfolk cohort with the 1966 MRC Rhondda Valley Study. 6 Goldmann applanation tonometry data from 1966 on 2200 women and 1891 men in the Rhondda Valley, Wales, 6 compared with Goldmann equivalent data from 2353 female and 1831 male EPIC-Norfolk participants examined 40 years later, 2006–2009. Women in 1966 show higher IOP and older people a trend for higher IOP.
Figure 2.
 
A comparison of age and sex-specific IOP in the EPIC-Norfolk cohort with the 1966 MRC Rhondda Valley Study. 6 Goldmann applanation tonometry data from 1966 on 2200 women and 1891 men in the Rhondda Valley, Wales, 6 compared with Goldmann equivalent data from 2353 female and 1831 male EPIC-Norfolk participants examined 40 years later, 2006–2009. Women in 1966 show higher IOP and older people a trend for higher IOP.
Epidemiologic studies have consistently reported that the major identifiable determinant of IOP is sBP, 16,17,33,34 as confirmed by the present study. It has been reported that BP and IOP correlate over time (a reduction in BP of 10 mm Hg over 5 years being associated with decreased IOP), with this observation leading the authors to suggest modulation of BP as a means of lowering glaucoma risk. 35 However, this concept belies the complexity of the relationship between BP, IOP, and glaucoma; a reduction in BP may result in a reduction of ocular perfusion pressure, which has been implicated as a risk factor for glaucomatous optic neuropathy. 36 In the context of reductions in BP and IOP occurring together, it is plausible that the lack of an age-related increase in mean IOP in our participants may be explicable on the grounds of better population BP control. It was of interest to discover that although sBP increased with age in a regression model (IOPg on age, sex, sBP, and axial length) showed that sBP accounted for 3.7% of all variation in IOP, indicating that our ability to explain the variation in this important ocular physiological characteristic remains far from complete. Therefore, other measures of vascular dynamics, including MAP and P-AMP, were explored. In the Norfolk cohort, it was found that the older people had a higher mean sBP (3.8 mm Hg/decade, P < 0.001) but a lower mean dBP (1.5 mm Hg/decade; P < 0.001). In a separate univariate model, no association between MAP and age (0.24 mm Hg/decade, P = 0.19) was identified. IOP showed no association with age, and that finding may indicate that MAP is the index of vascular perfusion more closely associated with IOP at the population level. However, when included in the final multiple regression model detailed in the results section, MAP did not appear to be significantly linked with IOP (P = 0.593), whereas sBP was still significantly associated (P < 0.001). Mean P-AMP increased in line with age (5 mm Hg/decade, P < 0.001), from 51.1 mm Hg in the 50- to 59-year-olds to 64.5 mm Hg in those aged 80 years and older. 
Two genetic loci linked with IOP have also been implicated as linkage regions for BP. 37 Female sex, tobacco smoking, and higher BMI have been found to be positively associated with IOP. 18,22,38 We have previously reported a difference in IOP between people of different socioeconomic status in Singapore (higher IOP among people of lower SES), which appeared to be mediated by systemic BP. 39 In this analysis, we have shown a positive correlation between IOP and sBP. Contrary to our findings in Singapore, we have also shown that IOP is lower among Norfolk people who have a manual occupation, compared with that in nonmanual workers. Furthermore, manual workers in Norfolk have higher BP than nonmanual workers, contradicting our previous hypothesis. The reason for the disagreement between U.K. and Singapore data remains unclear. 
Previous studies of agreement between Goldmann applanation tonometry (GAT), dynamic contour tonometry (DCT), and IOPcc measurements with the ORA-reported repeatability coefficients for GAT, DCT, and ORA-IOPcc of 2.2, 2.3, and 4.3 mm Hg, respectively. On average, GAT measures were approximately 2 mm Hg lower than both DCT and ORA-IOPcc, suggesting that IOP measurements with each device were not interchangeable. 40 In the present study, we found that mean IOPcc was greater than mean IOPg by 0.57 mm Hg, there being a wide range of differences in some cases (IOPg − IOPcc) −35.6 to + 8.7 mm Hg. It remains to be determined whether IOPg or IOPcc is a more accurate measure of true IOP and whether this is more strongly associated with risk of glaucoma. It is clear that in some people, IOPcc is substantially different from IOPg. The availability of measures of corneal biomechanical characteristics is a novel advantage for the ORA noncontact tonometer. CH (viscous damping) and CRF (tissue resistance to deformation) may be related to a variety of ocular diseases, from keratoconus to glaucoma. 13,41 43 In the present study, the (IOPg − IOPcc) difference was greater in older people: −0.27 mm Hg in people aged <60 years and −1.02 mm Hg in people aged over 80 years. The latter findings appeared to mirror age-related changes in corneal biomechanics; mean values for both CRF and CH are lower in older people, indicating that both stiffness and viscous damping capacity decline with age. A lower value of hysteresis in older people is a recognized finding, 44 although a decline in CRF contrasts with a laboratory study reporting increased corneal stiffness in older corneas. 45  
Several previous studies have explored the relationship between corneal biomechanics assessed by ORA and a variety of explanatory variables. A study of 271 myopic Singaporean school children did not identify any association between refractive error, axial length, and either CRF or CH, although in the case of CH, the regression coefficients from our analysis and the SCORM cohort (β = −0.153, P < 0.001 versus β = −0.11, P = 0.08) may suggest that the latter was underpowered to identify a difference. 46 This view is supported by results from a larger (n = 1233) study of Chinese children, which found that longer axial length was significantly associated with lower CH and higher IOP (P < 0.001). 47 In the Norfolk cohort of older people in the United Kingdom, higher IOP was also associated with higher CRF and lower CH, as shown previously in a clinic-based series. 14 In this context, the statistically significant but clinically irrelevant association with sBP is not surprising. When both IOP and sBP were included in a multiple regression model, the association between BP and CRF/CH was no longer significant. The link between corneal mechanics and IOP is supported by cross-sectional studies, 42 and also more compellingly, with the finding of a change in CRF/CH with changes in IOP. 48 Our data showed a highly significant association between both CRF and CH, as well as with age and sex (mean CRF and CH declined with age, and were higher in women than men). Significant negative correlation has been reported previously between age and both CRF and CH, in common with our current findings. The authors suggested that age-related structural changes resulting from collagen cross-linking may lead to a reduction of corneal biomechanical variables independent of central corneal thickness or IOP. 14,49 The finding of lower CRF/CH in taller and slimmer people did not appear to be consistent findings in multiple regression models. 
In summary, we report the distribution of IOP in an older, predominantly Caucasian, U.K. population. Mean IOP values were similar to those reported in previous U.K. population studies, and confirmed sBP as the major identifiable determinant. Using the ORA (Reichert), values of IOP corrected for corneal biomechanical properties (IOPcc), CH, and CRF were obtained. It was found that CRF and CH varied with age, sex and axial length of the eye. The present study provided current population-based values for important ocular characteristics. 
Footnotes
 Supported by Grant G0401527 from the Medical Research Council, UK, and Grant 262 from Research into Ageing, UK; The Richard Desmond Charitable Trust (via Fight for Sight) (PJF); the Department for Health through an award made by the National Institute for Health Research to Moorfields Eye Hospital (PJF); and The UCL Institute of Ophthalmology for a specialist Biomedical Research Centre for Ophthalmology (PJF). The views expressed in this article are those of the authors and are not necessarily those of the Department for Health.
Footnotes
 Disclosure: P.J. Foster, None; D.C. Broadway, None; D.F. Garway-Heath, None; J.L.Y. Yip, None; R. Luben, None; S. Hayat, None; N. Dalzell, None; N.J. Wareham, None; K.-T. Khaw, None
References
Leske MC Connell AMS Wu S . Risk factors for open angle glaucoma: the Barbados Eye Study. Arch Ophthalmol. 1995;113:918–924. [CrossRef] [PubMed]
Leske MC Heijl A Hussein M . Factors for glaucoma progression and the effect of treatment: the early manifest glaucoma trial. Arch Ophthalmol. 2003;121:48–56. [CrossRef] [PubMed]
Migdal C Gregory W Hitchings RA . Long-term functional outcome after early surgery compared with laser and medicine in open-angle glaucoma. Ophthalmology. 1994;101:1651–1657. [CrossRef] [PubMed]
Resnikoff S Pascolini D Etya'ale D . Global data on visual impairment in the year 2002. Bull World Health Organ. 2004;82:844–851. [PubMed]
Sommer A Tielsch JM Katz J . Relationship between intraocular pressure and primary open-angle glaucoma among white and black Americans: the Baltimore Eye Survey. Arch Ophthalmol. 1991;109:1090–1095. [CrossRef] [PubMed]
Hollows FC Graham PA . Intraocular pressure, glaucoma and glaucoma suspects in a defined population. Br J Ophthalmol. 1966;50:570–586. [CrossRef] [PubMed]
Perkins ES . The Bedford glaucoma survery. II. Rescreening of normal population. Br J Ophthalmol. 1973;57:186–192. [CrossRef] [PubMed]
Whitacre MM Stein R . Sources of error with Goldmann-type tonometers. Surv Ophthalmol. 1993;38:1–30. [CrossRef] [PubMed]
Ehlers N Bramsen T Sperling S . Applanation tonometry and central corneal thickness. Acta Ophthalmol. 1975;53:34–43. [CrossRef]
Orssengo GJ Pye DC . Determination of the true intraocular pressure and modulus of elasticity of the human cornea in vivo. J Math Biol. 1999;61:551–572. [CrossRef]
Argus WA . Ocular hypertension and central corneal thickness. Ophthalmology. 1995;102:1810–1812. [CrossRef] [PubMed]
Purslow PP Karwatowski WSS . Ocular elasticity: is engineering stiffness a more useful characterization parameter than ocular rigidity? Ophthalmology. 1996;103:1686–1692. [CrossRef] [PubMed]
Luce DA . Determining in vivo biomechanical properties of the cornea with an ocular response analyzer. J Cataract Refract Surg. 2005;31:156–162. [CrossRef] [PubMed]
Kotecha A Elsheikh A Roberts CR . Corneal thickness- and age-related biomechanical properties of the cornea measured with the ocular response analyzer. Invest Ophthalmol Vis Sci. 2006;47:5337–5347. [CrossRef] [PubMed]
Wu S-Y Leske MC . Associations with intraocular pressure in the Barbados Eye Study. Arch Ophthalmol. 1997;115:1572–1576. [CrossRef] [PubMed]
Klein BE Klein R . Intraocular pressure and cardiovascular risk variables. Arch Ophthalmol. 1981;99:837–839. [CrossRef] [PubMed]
Foster PJ Machin D Wong TY . Determinants of intraocular pressure and its association with glaucomatous optic neuropathy in Chinese Singaporeans: the Tanjong Pagar Study. Invest Ophthalmol Vis Sci. 2003;44:3885–3891. [CrossRef] [PubMed]
Leske MC Connell AMS Wu S-Y . Distribution of intraocular pressure: the Barbados Eye Study. Arch Ophthalmol. 1997;115:1051–1057. [CrossRef] [PubMed]
Topouzis F Wilson MR Harris A . Prevalence of open-angle glaucoma in Greece: the Thessaloniki Eye Study. Am J Ophthalmol. 2007;144:511–519. [CrossRef] [PubMed]
Bonomi L Marchini G Marrafa M . Prevalence of glaucoma and intraocular pressure distribution in a defined population: the Egna-Neumarkt Study. Ophthalmology. 1998;105:209–215. [CrossRef] [PubMed]
Foster PJ Baasanhu J Alsbirk PH . Central corneal thickness and intraocular pressure in a Mongolian population. Ophthalmology. 1998;105:969–973. [CrossRef] [PubMed]
Kawase K Tomidokoro A Araie M . Ocular and systemic factors related to intraocular pressure in Japanese adults: the Tajimi study. Br J Ophthalmol. 2008;92:1175–1179. [CrossRef] [PubMed]
Riboli E . Nutrition and cancer: background and rationale of the European Prospective Investigation into Cancer and Nutrition (EPIC). Ann Oncol. 1992;2:783–791.
Day NE Oakes S Lubin R . EPIC-Norfolk: study design and characteristics of the cohort: European Prospective Investigation of Cancer. Br J Cancer. 1999;80:95–103. [PubMed]
McFadden E Luben R Wareham N . Occupational social class, educational level, smoking and body mass index, and cause-specific mortality in men and women: a prospective study in the European Prospective Investigation of Cancer and Nutrition in Norfolk (EPIC-Norfolk) cohort. Eur J Epidemiol. 2008;23:511–522. [CrossRef] [PubMed]
Bland JM Altman DG . Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;i:307–310.
El Sheikh A Alhasso D Kotecha A Garway-Heath DF . Assessment of the Ocular Response Analyzer as a tool for intraocular pressure measurement. J Biomech Eng. 2009;131:081010-1–081010-9.
Sprosten K Primatesta P . Risk Factors for Cardiovascular Disease. Health Survey for England 2003. Vol. 2. London: HMSO; 2004.
Graham PA Hollows FC . Sources of variation in tonometry. Trans Ophthalmol Soc UK. 1964;84:597–613. [PubMed]
Shiose Y Kitazawa Y Tsukuhara S . Epidemiology of glaucoma in Japan: a nationwide glaucoma survey. Jpn J Ophthalmol. 1991;35:133–155. [PubMed]
Coffey M Reidy A Wormald R . Prevalence of glaucoma in the west of Ireland. Br J Ophthalmol. 1993;77:17–21. [CrossRef] [PubMed]
Nomura H Shimokata H Ando F . Age-related changes in intraocular pressure in a large Japanese population: a cross-sectional and longitudinal study. Ophthalmology. 1999;106:2016–2022. [CrossRef] [PubMed]
Dielemans I de Jong PT Stolk R . Primary open-angle glaucoma, intraocular pressure, and diabetes mellitus in the general elderly population. The Rotterdam Study. Ophthalmology. 1996;103:1271–1275. [CrossRef] [PubMed]
Klein BE Klein R Linton KL . Intraocular pressure in an American community: the Beaver Dam Eye Study. Invest Ophthalmol Vis Sci. 1992;33:2224–2228. [PubMed]
Klein BE Klein R Knudtson MD . Intraocular pressure and systemic blood pressure: longitudinal perspective—the Beaver Dam Eye Study. Br J Ophthalmol. 2005;89:284–287. [CrossRef] [PubMed]
Tielsch JM Katz J Sommer A . Hypertension, perfusion pressure and primary open-angle glaucoma. Arch Ophthalmol. 1995;113:216–221. [CrossRef] [PubMed]
Duggal P Klein AP Lee KE . Identification of novel genetic loci for intraocular pressure: a genomewide scan of the Beaver Dam Eye Study. Arch Ophthalmol. 2007;125:74–79. [CrossRef] [PubMed]
Leske MC Podger MJ . Intraocular pressure: cardiovascular risk variables and visual field defects. Am J Epidemiol. 1983;118:280–287. [PubMed]
Yip JLY Aung T Wong TY . Socioeconomic status, systolic blood pressure and intraocular pressure: the Tanjong Pagar Study. Br J Ophthalmol. 2007;91:56–61. [CrossRef] [PubMed]
Kotecha A White E Schlottmann PG Garway-Heath DF . Intraocular pressure measurement precision with the Goldmann applanation, dynamic contour, and ocular response analyzer tonometers. Ophthalmology. 2010;117:730–737. [CrossRef] [PubMed]
Mangouritsas G Morphis G Mourtzoukos S Feretis E . Association between corneal hysteresis and central corneal thickness in glaucomatous and non-glaucomatous eyes. Acta Ophthalmol. 2009;87:901–905. [CrossRef] [PubMed]
Ang GS Bochmann F Townend J Azuara-Blanco A . Corneal biomechanical properties in primary open angle glaucoma and normal tension glaucoma. J Glaucoma. 2008;17:259–262. [CrossRef] [PubMed]
Wells AP Garway-Heath DF Poostchi A . Corneal hysteresis but not corneal thickness correlates with optic nerve surface compliance in glaucoma patients. Invest Ophthalmol Vis Sci. 2008;49:3262–3268. [CrossRef] [PubMed]
Elsheikh A Wang D Rama P . Experimental assessment of human corneal hysteresis. Curr Eye Res. 2008;33:205–213. [CrossRef] [PubMed]
Elsheikh A Wang D Brown M . Assessment of corneal biomechanical properties and their variation with age. Curr Eye Res. 2007;32:11–19. [CrossRef] [PubMed]
Lim L Gazzard G Chan YH . Cornea biomechanical characteristics and their correlates with refractive error in Singaporean children. Invest Ophthalmol Vis Sci. 2008;49:3852–3857. [CrossRef] [PubMed]
Song Y Congdon N Li L . Corneal hysteresis and axial length among Chinese secondary school children: the Xichang Pediatric Refractive Error Study (X-PRES) report no. 4. Am J Ophthalmol. 2008;145:819–826. [CrossRef] [PubMed]
Sun L Shen M Wang J . Recovery of corneal hysteresis after reduction of intraocular pressure in chronic primary angle-closure glaucoma. Am J Ophthalmol. 2009;147:1061–1066. [CrossRef] [PubMed]
Kamiya K Shimizu K Ohmoto F . Effect of aging on corneal biomechanical parameters using the ocular response analyzer. J Refract Surg. 2009;25:888–893. [CrossRef] [PubMed]
Figure 1.
 
A scatterplot of IOPg against IOPcc from the Ocular Response Analyzer (Reichert, Depew, NY). Mean difference (IOPg − IOPcc) was −0.58 mm Hg (SD 1.52). The two measures are highly correlated (Pearson r = 0.915; P < 0.001). There were five persons in whom the IOPg − IOPcc was > −10 mm Hg. The 95% limits of agreement for the two measures were +2.41 to −3.57 mm Hg.
Figure 1.
 
A scatterplot of IOPg against IOPcc from the Ocular Response Analyzer (Reichert, Depew, NY). Mean difference (IOPg − IOPcc) was −0.58 mm Hg (SD 1.52). The two measures are highly correlated (Pearson r = 0.915; P < 0.001). There were five persons in whom the IOPg − IOPcc was > −10 mm Hg. The 95% limits of agreement for the two measures were +2.41 to −3.57 mm Hg.
Figure 2.
 
A comparison of age and sex-specific IOP in the EPIC-Norfolk cohort with the 1966 MRC Rhondda Valley Study. 6 Goldmann applanation tonometry data from 1966 on 2200 women and 1891 men in the Rhondda Valley, Wales, 6 compared with Goldmann equivalent data from 2353 female and 1831 male EPIC-Norfolk participants examined 40 years later, 2006–2009. Women in 1966 show higher IOP and older people a trend for higher IOP.
Figure 2.
 
A comparison of age and sex-specific IOP in the EPIC-Norfolk cohort with the 1966 MRC Rhondda Valley Study. 6 Goldmann applanation tonometry data from 1966 on 2200 women and 1891 men in the Rhondda Valley, Wales, 6 compared with Goldmann equivalent data from 2353 female and 1831 male EPIC-Norfolk participants examined 40 years later, 2006–2009. Women in 1966 show higher IOP and older people a trend for higher IOP.
Table 1.
 
Demographic, Systemic, and Ophthalmic Characteristics of Participants
Table 1.
 
Demographic, Systemic, and Ophthalmic Characteristics of Participants
Age (y)
48–59 60–69 70–79 80+ 48–91
Women, % 61.3 58.5 52.6 47.2 55.7
Nonmanual workers, % 32.9 37.9 35.1 23.8 34.8
Education, % at least O-level 69.2 61.5 56.3 57.6 60.4
Never smoked, % 50.4 46.1 48.6 40.2 46.8
Height, m 167.8 166.9 165.7 163.7 166.3
Weight, kg 75.0 75.4 74.3 71.5 74.6
BMI, kg/m2 26.6 27.0 27.0 26.9 26.9
Waist, cm 90.0 93.4 95.2 96.3 93.9
Hip/waist ratio 0.86 0.88 0.90 0.91 0.89
Systolic BP, mm Hg 130.4 134.8 138.5 140.4 136.0
Diastolic BP, mm Hg 79.3 79.5 77.6 75.9 78.5
Mean arterial pressure, mm Hg 96.3 97.9 97.9 97.4 97.7
Pulse amplitude, mm Hg 51.1 55.3 60.8 64.5 57.5
Birth weight, kg 3.33 3.35 3.32 3.46 3.35
Anterior chamber depth, mm 3.09 3.06 3.00 2.96 3.04
Axial length, mm 23.01 23.29 23.13 22.69 23.15
Refractive error, D −0.63 0.08 0.60 0.41 0.20
Table 2.
 
IOP and Corneal Biomechanics by Age and Sex
Table 2.
 
IOP and Corneal Biomechanics by Age and Sex
Age (y) Sex Mean IOPg* (SD) Mean IOPcc* (SD) CRF (SD) CH (SD)
Men
    48–59 189 15.82 (3.36) 16.19 (3.08) 10.44 (1.83) 10.32 (1.62)
    60–69 783 15.95 (3.80) 16.64 (3.92) 10.10 (1.82) 9.89 (1.62)
    70–79 638 16.07 (3.86) 16.89 (4.07) 9.95 (1.68) 9.66 (1.73)
    80+ 220 15.86 (4.21) 16.87 (4.28) 9.62 (1.71) 9.35 (1.67)
    48–91 1831 15.96 (3.89) 16.71 (3.95) 10.02 (1.77) 9.79 (1.69)
Women
    48–59 321 15.64 (3.58) 15.88 (3.42) 10.55 (1.76) 10.51 (1.61)
    60–69 1127 16.28 (3.60) 16.60 (3.52) 10.57 (1.67) 10.31 (1.56)
    70–79 712 15.99 (3.53) 16.64 (3.66) 10.15 (1.62) 9.93 (1.52)
    80+ 193 15.39 (3.22) 16.25 (3.28) 9.80 (1.77) 9.74 (1.62)
    48–91 2353 16.03 (3.55) 16.49 (3.54) 10.38 (1.69) 10.18 (1.58)
Men and women
    48–91 4184 16.00 (3.68) 16.58 (3.72) 10.22 (1.74) 10.00 (1.64)
Table 3.
 
Differences between IOPg and IOPcc
Table 3.
 
Differences between IOPg and IOPcc
Age (y) Sex IOPg − IOPcc* SD (IOPg − IOPcc) 95% CI Difference (IOPg − IOPcc)
48–59 M: 189 −0.37 (−4.33 to 3.93) 1.23 −0.54 to −0.19
F: 321 −0.23 (−5.47 to 7.70) 1.32 −0.37 to −0.85
60–69 M: 783 −0.70 (−35.57 to 5.13) 1.86 −0.83 to −0.57
F: 1127 −0.32 (−5.73 to 7.87) 1.21 −0.39 to −0.25
70–79 M: 638 −0.82 (−32.27 to 5.90) 1.39 −1.19 to −0.82
F: 712 −0.65 (−23.83 to 3.03) 1.45 −0.76 to −0.54
80+ M: 220 −1.01 (−8.27 to 8.67) 1.39 −1.19 to −0.82
F: 193 −0.86 (−12.57 to −0.64) 1.54 −1.07 to −0.64
48–91 M: 1831 −0.74 (−35.57 to 8.67) 1.72 −0.82 to −0.66
F: 2353 −0.45 (−23.83 to 7.87) 1.34 −0.51 to −0.40
48–91 M & F: 4184 −0.57 (−35.57 to 8.67) 1.53 −0.62 to −0.53
Table 4.
 
Univariate Regression Coefficients (β) of Sociodemographic, Systemic, Physical Factors Plausibly Associated with IOP and Corneal Biomechanics
Table 4.
 
Univariate Regression Coefficients (β) of Sociodemographic, Systemic, Physical Factors Plausibly Associated with IOP and Corneal Biomechanics
IOPg IOPcc CRF CH
Age, decade −0.043 (0.54) 0.202 (0.004) −0.306 (<0.001)* 0.340 (<0.001)*
Sex† 0.064 (0.57) −0.224 (0.053) 0.351 (<0.001)* 0.387 (<0.001)*
Social class‡ −0.008 (0.18) −0.004 (0.57) 0.006 (0.048) −0.004 (0.18)
Classes I–V
Nonmanual versus manual workers§ 0.30 (0.012) 0.292 (0.016) −0.075 (0.19) 0.016 (0.77)
Education‖ −0.032 (0.55) −0.030 (0.57) −0.002 (0.92) 0.006 (0.80)
At least O-level, versus no O-levels −0.065 (0.57) −0.036 (0.76) −0.032 (0.56) −0.020 (0.70)
Height, m −0.006 (0.37) 0.005 (0.42) 0.013* (<0.001)* 0.014* (<0.001)*
Weight, kg 0.003 (0.40) 0.006 (0.16) −0.002 (0.26) 0.004 (0.039)
BMI, kg/m2 0.006 (0.57) 0.005 (0.62) 0.001 (0.81) −0.001 (0.86)
Waist, cm 0.002 (0.66) 0.007 (0.14) 0.005 (0.020) 0.007 (0.001)*
Hip/waist ratio −0.335 (0.62) 0.936 (0.18) 1.474 (<0.001)* 1.584 (<0.001)*
sBP, per 10 mm Hg 0.37 (<0.001)* 0.38 (<0.001)* 0.06 (0.001)* 0.06 (<0.001)*
dBP, per 10 mm Hg 0.48 (<0.001)* 0.49 (<0.001)* 0.09 (0.003) 0.07 (0.011)
MAP, per 10 mm Hg 0.55 (<0.001)* 0.56 (<0.001)* 0.09 (<0.001)* 0.08 (=0.001)
P-AMP, per 10 mm Hg 0.32 (<0.001)* 0.33 (<0.001)* 0.05 (0.032) 0.06 (0.005)
Birth weight, kg 0.017 (0.88) 0.124 (0.26) −0.096 (0.072) 0.120 (0.014)
Smoking status, ever vs. never¶ −0.043 (0.72) −0.071 (0.56) −0.038 (0.51) −0.024 (0.66)
Anterior chamber depth, mm 0.091 (0.41) 0.159 (0.16) −0.065 (0.22) 0.106 (0.033)
Axial length, mm 0.258 (<0.001)* 0.366 (<0.001)* 0.052 (0.011) 0.153 (<0.001)*
Refractive error, D # 0.130 (<0.001)* 0.141 (<0.001)* −0.016 (0.17) 0.027 (0.015)
Table 5.
 
Multiple Regression of Predictor Variables on Goldmann-Equivalent IOPg
Table 5.
 
Multiple Regression of Predictor Variables on Goldmann-Equivalent IOPg
Regression Coefficents (SE) 95% CI Standardized β P
sBP, per 10 mm Hg 0.396 (0.038) 0.171 <0.001
0.322 to 0.470
Axial length, mm 0.271 (0.044) 0.100 <0.001
0.185 to 0.358
Sex, M = 1, F = 2 0.251 (0.120) 0.034 0.035
0.018 to 0.488
Age, per decade −0.153 (0.073) −0.038 0.035
−0.295 to −0.010
Nonmanual versus manual workers −0.251 (0.122) −0.033 0.039
−0.489 to −0.013
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×