Associations with Intraocular Pressure in a Large Cohort: Results from the UK Biobank

Abstract

Purpose: To describe the associations of physical and demographic factors with Goldmann-correlated intraocular pressure (IOPg) and corneal-compensated intraocular pressure (IOPcc) in a British cohort.

Design: Cross-sectional study within the UK Biobank, a large-scale multisite cohort study in the United Kingdom.

Participants: We included 110 573 participants from the UK Biobank with intraocular pressure (IOP) measurements available. Their mean age was 57 years (range, 40-69 years); 54% were women, and 90% were white.

Methods: Participants had 1 IOP measurement made on each eye using the Ocular Response Analyzer noncontact tonometer. Linear regression models were used to assess the associations of IOP with physical and demographic factors.

Main outcome measures: The IOPg and IOPcc.

Results: The mean IOPg was 15.72 mmHg (95% confidence interval [CI], 15.70-15.74 mmHg), and the mean IOPcc was 15.95 mmHg (15.92-15.97 mmHg). After adjusting for covariates, IOPg and IOPcc were both significantly associated with older age, male sex, higher systolic blood pressure (SBP), faster heart rate, greater myopia, self-reported glaucoma, and colder season (all P < 0.001). The strongest determinants of both IOPg and IOPcc were SBP (partial R(2): IOPg 2.30%, IOPcc 2.26%), followed by refractive error (IOPg 0.60%, IOPcc 1.04%). The following variables had different directions of association with IOPg and IOPcc: height (-0.77 mmHg/m IOPg; 1.03 mmHg/m IOPcc), smoking (0.19 mmHg IOPg, -0.35 mmHg IOPcc), self-reported diabetes (0.41 mmHg IOPg, -0.05 mmHg IOPcc), and black ethnicity (-0.80 mmHg IOPg, 0.77 mmHg IOPcc). This suggests that height, smoking, diabetes, and ethnicity are related to corneal biomechanical properties. The increase in both IOPg and IOPcc with age was greatest among those of mixed ethnicities, followed by blacks and whites. The same set of covariates explained 7.4% of the variability of IOPcc but only 5.3% of the variability of IOPg.

Conclusions: This analysis of associations with IOP in a large cohort demonstrated that some variables clearly have different associations with IOPg and IOPcc, and that these 2 measurements may reflect different biological characteristics.

Figure 1

Graphs showing that Goldmann-correlated intraocular pressure (IOPg) and corneal-compensated intraocular pressure (IOPcc) increase linearly with (A) age, (B) systolic blood pressure, (C) pulse rate, (D) body mass index (BMI). (E) The IOPg and IOPcc show an inverse relationship with refractive error. (F) Height has an insignficant relationship with IOPg and IOPcc in univariable regression, but a differential relationship with IOPg and IOPcc in multivariable regression. The error bars represent the 95% confidence intervals (CIs).

Figure 2

Beta regression coefficients for each ethnic group in the multivariable model, showing the differences between each group's Goldmann-correlated intraocular pressure (IOPg) and corneal-compensated intraocular pressure (IOPcc) compared with the baseline group of white ethnicity. CI = confidence interval.

Figure 3

Variation of Goldmann-correlated intraocular pressure (IOPg) and corneal-compensated intraocular pressure (IOPcc) with age for each ethnic group. The error bars represent the 95% confidence intervals (CIs).