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Editors Selection IGR 22-3

Risk Factors: Factors affecting IOP

Rupert Bourne

Comment by Rupert Bourne on:

66546 Associations with Intraocular Pressure in a Large Cohort: Results from the UK Biobank, Chan MP; Grossi CM; Khawaja AP et al., Ophthalmology, 2016; 123: 771-782


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This report by the UK Biobank Eye and Vision Consortium investigated the physical and demographic associations of intraocular pressure (IOP) in a large cohort of approximately 110,573 people aged 40-69 years living in England and Wales. The Ocular Response Analyzer (ORA; Reichert Corp., Philadelphia, PA) was used to take a single measurement of IOP from each eye of participants. The ORA calculates Goldmann-correlated and corneal-compensated IOP estimates (IOPg and IOPcc, respectively). IOPg is analogous to standard noncontact IOP measurements, whereas IOPcc is an IOP estimate that uses a mathematical correction to minimize its corneal dependence.1-3 Central corneal thickness (CCT) is correlated with IOPg but not IOPcc.

This instrument's outputs for IOPg and IOPcc were statistically tested for associations with sex, age, deprivation index, center of assessment, weight, height, waist circumference, systolic and diastolic blood pressure, body mass index, refractive error, smoking status, diabetes, glaucoma, macular degeneration, and season of IOP measurement using univariable linear regression and then in a multivariable regression model. The analyses confirm many of the known associations between IOP and demographic and systemic risk factors, although it must be cautioned that this is not a population-based study (only 5% the enumerated population were examined), and the age range ends younger than most population-based eye surveys, so direct comparisons are not possible. It is noteworthy that despite incorporating all these risk factors in the model, the model only explained a small proportion of IOPg and IOPcc variation (adjusted R2: 5.3% IOPg, 7.4% IOPcc) which is in keeping with low explanatory power reported by other studies. Of all the variables, self-reported glaucoma had the greatest effect on IOP, equivalent to a five- to ten-fold effect on IOP compared with a decade increase in age.

The relationship of the varying aspects of the ORA signal to their underlying ocular determinants is unknown

Differences in the association of IOPg and IOPcc with self-reported diabetes (positively and significantly associated with IOPg but not with IOPcc), height (positively associated with IOPcc, negatively associated with IOPg), smoking (positively associated with IOPg but negatively associated with IOPcc), and black ethnicity (negatively associated with IOPg, positively associated with IOPcc) are explored in the discussion in relation to possible corneal biomechanical influences. For example, the thinner CCT found by studies of African populations compared to white populations, supports this report's findings of higher IOPg in whites than blacks, while IOPcc has a converse relationship. Appropriately, the authors are cautious in being any more than speculative about the significance of these differential associations with IOPg and IOPcc, given that the relationship of the varying aspects of the ORA signal to their underlying ocular determinants is unknown.

References

  1. Luce DA. Methodology for corneal compensated IOP and corneal resistance factor for An Ocular Response Analyzer. Available at www.ocularresponseanalyzer. com/downloads/luce-2006-1.pdf. Accessed May 25, 2016.
  2. Luce DA, Taylor D. Reichert Ocular Response Analyzer measures corneal biomechanical properties and IOP. White Paper. Available at www. ocularresponseanalyzer.com/ocular%20response%20analyzer%20white%20 paper.pdf. Accessed May 25, 2016.
  3. Medeiros FA, Weinreb RN. Evaluation of the influence of corneal biomechanical properties on intraocular pressure measurements using the ocular response analyzer. J Glaucoma 2006;15:364-370.


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