July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Refining models characterising age-related changes in the human cornea
Author Affiliations & Notes
  • Janelle Tong
    Centre for Eye Health, Kensington, New South Wales, Australia
    School of Optometry and Vision Science, University of New South Wales, New South Wales, Australia
  • Jack Phu
    Centre for Eye Health, Kensington, New South Wales, Australia
    School of Optometry and Vision Science, University of New South Wales, New South Wales, Australia
  • Michael Kalloniatis
    Centre for Eye Health, Kensington, New South Wales, Australia
    School of Optometry and Vision Science, University of New South Wales, New South Wales, Australia
  • Barbara Zangerl
    Centre for Eye Health, Kensington, New South Wales, Australia
    School of Optometry and Vision Science, University of New South Wales, New South Wales, Australia
  • Footnotes
    Commercial Relationships   Janelle Tong, None; Jack Phu, Australian Government Research Training Program (R), Guide Dogs NSW/ACT (F); Michael Kalloniatis, Guide Dogs NSW/ACT (F); Barbara Zangerl, None
  • Footnotes
    Support  Guide Dogs NSW/ACT (salary support)
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 4232. doi:
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      Janelle Tong, Jack Phu, Michael Kalloniatis, Barbara Zangerl; Refining models characterising age-related changes in the human cornea. Invest. Ophthalmol. Vis. Sci. 2019;60(9):4232.

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

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Abstract

Purpose : Inherent individual variability has contributed to difficulties identifying patterns of normal age-related change in corneal parameters. We performed a retrospective cohort study utilising techniques aimed at minimising variability to refine models describing age-related changes in corneal thickness and topography. We also sought to identify other demographic factors that contribute to normal variation in these parameters.

Methods : Corneal thickness (CT), front surface sagittal curvature (FSSC) and back surface sagittal curvature (BSSC) measurements were extracted at 57 points across the central 8.00mm from Pentacam HR scans from one eye in participants without corneal abnormalities (age range 8-60 years). Correlations between measurements and gender, ethnicity, spherical equivalent refractive error and astigmatism were analysed using Pearson’s correlations. Hierarchical and k-means clustering algorithms were applied to identify corneal locations showing similar change with age, and measurements were pooled based on these clusters. Sliding window analyses using decade windows were performed and polynomial models were applied to the data to identify regression patterns in the investigated parameters with age.

Results : Data from 117 participants were included. Significant correlations were observed between CT in the inferior paracentral points and astigmatism (p=0.030-0.048), FSSC at all locations and gender (p=<0.001-0.049) and BSSC in the temporal cornea and gender (p=0.001-0.040). A fourth degree polynomial model provided the most suitable fit to the CT data (R2=0.53-0.75), and third degree polynomial models appeared to best fit the FSSC and BSSC data (R2=0.42-0.74 and R2=0.30-0.72 respectively). While polynomial curves were similar across clusters for CT and FSSC measurements, there was increased variation between clusters for BSSC measurements (Figure 1).

Conclusions : By identifying corneal locations showing similar age-related change, we are able to characterise how several corneal parameters alter with ageing in a normal population. This work also indicates that gender and astigmatism should be considered when developing models describing age-related regression in these parameters.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

Figure 1. Regression models derived from central and superonasal peripheral clusters for A. corneal thickness B. front surface curvature and C. back surface curvature. Note in C. the central cluster contained peripheral locations.

Figure 1. Regression models derived from central and superonasal peripheral clusters for A. corneal thickness B. front surface curvature and C. back surface curvature. Note in C. the central cluster contained peripheral locations.

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