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Corneal Nerve Migration Rate in a Healthy Control Population

Al Rashah, Khaled, MScOptom, PhD1; Pritchard, Nicola, PhD1; Dehghani, Cirous, PhD1,2; Ruggeri, Alfredo, PhD3; Guimaraes, Pedro, MSc3; Russell, Anthony, MBBS, PhD4,5; Malik, Rayaz A., MD, PhD6,7; Efron, Nathan, PhD, DSc1; Edwards, Katie, PhD1*

doi: 10.1097/OPX.0000000000001254
Original Investigations

PURPOSE The purpose of this study was to establish an age-dependent normative range and factors affecting the migration rate of the corneal subbasal nerve plexus in a healthy control population.

METHODS Corneal nerve migration rate was measured in 60 healthy participants grouped by age: A, aged 20 to 39 years (n = 20); B, 40 to 59 years (n = 20); and C, 60 to 79 years (n = 20). Laser-scanning corneal confocal microscopy was performed on the right eye of all participants at baseline and again after 3 weeks. Fully automated software was used to montage the frames. Distinctive nerve landmarks were manually reidentified between the two montages, and a software program was developed to measure the migration of these landmark points to determine corneal nerve migration rate in micrometers per week (μm/wk).

RESULTS The mean ± SD age of all participants in the study was 47.5 ± 15.5 years; 62% of participants were male. The average corneal nerve migration rates of groups A, B, and C were 42.0 ± 14.0, 42.3 ± 15.5, and 42.0 ± 10.8 μm/wk, respectively (P = .99). There was no difference in corneal nerve migration rate between male (41.1 ± 13.5 μm/wk) and female (43.7 ± 13.2 μm/wk) participants (P = .47). There was no significant correlation between age (P = .97), smoking (P = .46), alcohol use (P = .61), and body mass index (P = .49, respectively) with corneal nerve migration rate. However, exercise frequency correlated significantly (P = .04) with corneal nerve migration rate.

CONCLUSIONS Corneal nerve migration rate varies in healthy individuals and is not affected by age, sex, or body mass index but is related to physical activity.

1Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Queensland, Australia

2CSIRO Health and Biosecurity, Melbourne, Victoria, Australia

3Department of Information Engineering, University of Padova, Padova, Italy

4Department of Diabetes and Endocrinology, Princess Alexandra Hospital, Brisbane, Queensland, Australia

5School of Medicine, University of Queensland, Brisbane, Queensland, Australia

6Cardiovascular Medicine, Division of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom

7Weill Cornell Medicine-Qatar, Doha, Qatar *katie.edwards@qut.edu.au

Submitted: November 1, 2017

Accepted: May 22, 2018

Funding/Support: JDRF (8-2008-362; to NE) and JDRF (3-2013-211; to KE).

Conflict of Interest Disclosure: None of the authors have reported a financial conflict of interest.

Author Contributions: Conceptualization: KAR, NP, A Russell, RAM, NE, KE; Data Curation: KAR, KE; Formal Analysis: KAR, CD, KE; Investigation: KAR, KE; Methodology: KAR, KE; Project Administration: KAR, KE; Software: A Ruggeri, PG; Supervision: NP, NE, KE; Writing – Original Draft: KAR; Writing – Review & Editing: KAR, NP, CD, A Ruggeri, PG, A Russell, RAM, NE, KE.

© 2018 American Academy of Optometry