Spectral-domain OCT (SD-OCT) provides high resolution images enabling identification of individual retinal layers. We included 32,923 participants aged 40–69 years old from UK Biobank. Questionnaires, physical examination, and eye examination including SD-OCT imaging were performed. SD OCT measured photoreceptor layer thickness includes photoreceptor layer thickness: inner nuclear layer-retinal pigment epithelium (INL-RPE) and the specific sublayers of the photoreceptor: inner nuclear layer-external limiting membrane (INL-ELM); external limiting membrane-inner segment outer segment (ELM-ISOS); and inner segment outer segment-retinal pigment epithelium (ISOS-RPE). In multivariate regression models, the total average INL-RPE was observed to be thinner in older aged, females, Black ethnicity, smokers, participants with higher systolic blood pressure, more negative refractive error, lower IOPcc and lower corneal hysteresis. The overall INL-ELM, ELM-ISOS and ISOS-RPE thickness was significantly associated with sex and race. Total average of INL-ELM thickness was additionally associated with age and refractive error, while ELM-ISOS was additionally associated with age, smoking status, SBP and refractive error; and ISOS-RPE was additionally associated with smoking status, IOPcc and corneal hysteresis. Hence, we found novel associations of ethnicity, smoking, systolic blood pressure, refraction, IOPcc and corneal hysteresis with photoreceptor thickness.
These authors jointly supervised this work: Paul J. Foster and Praveen J. Patel.
A comprehensive list of consortium members appears at the end of the paper.
Optical coherence tomography (OCT) imaging has transformed our understanding of diseases affecting the vitreous, retina and choroid. It is a non-invasive,
Photoreceptors play a central role in vision function and are responsible for phototransduction
As OCT becomes more widely used to diagnose and monitor retinal pathologies, it is important to report normative data for photoreceptor layer thickness and identify major determinants of the photoreceptor layer thickness in populations. Although studies have distinguished the retinal layers of the photoreceptors in a normal population
UK Biobank is a large-scale multisite cohort study of UK residents aged 40–69 years who were registered with the National Health Service. The UK Biobank data resource was set up to allow detailed investigation of genetic and environmental determinants of major diseases of later life
Spectral-domain OCT imaging was performed using the Topcon 3D OCT 1000 Mk2 (Topcon Corp., Tokyo, Japan) after visual acuity, autorefraction and IOP measurements were collected. OCT images were obtained under mesopic conditions, without pupillary dilation, using the 3D macular volume scan (512 A-scans per B-scan; 128 horizontal B-scans in a 6 × 6-mm raster pattern)
All SD OCT images were stored as Topcon proprietary .fds image files on the UK Biobank supercomputers in Oxford, United Kingdom, with no prior analysis of macular thickness. The inner and outer retinal surfaces were segmented using the Topcon Advanced Boundary Segmentation (TABS) Algorithm (Version 1.6.1.1)
The TABS segmentation algorithm was used to segment the photoreceptor layer: inner nuclear layer/outer plexiform layer boundary to retinal pigment epithelium (INL-RPE) and the specific sublayers of the photoreceptor: inner nuclear layer/outer plexiform layer boundary to external limiting membrane (INL-ELM); external limiting membrane-inner segment outer segment (ELM-ISOS); and inner segment outer segment-retinal pigment epithelium (ISOS-RPE). (Fig. Spectral-domain optical coherence tomography images with schematic showing representative of Inner nuclear layer–Retinal pigment epithelium (INL-RPE); Inner nuclear layer- External limiting membrane (INL-ELM); External limiting membrane-Inner and outer segments (ELM-ISOS); and Inner and outer segments-Retinal pigment epithelium thickness (ISOS-RPE).
All participants who underwent SD OCT as part of the UK Biobank were included in the initial analysis. Exclusion criteria included participants who withdrew their consent, had poor SD OCT signal strength, missing thickness values from any Early Treatment Diabetic Retinopathy Study (ETDRS) subfield, image quality score <45, poor centration certainty, or poor segmentation certainty using TABS software
For this analysis, if both eyes of a patient were eligible for inclusion, one eye was randomly selected using STATA software. Mean, standard deviations (SD) and 95% confidence intervals of the INL-RPE, INL-ELM, ELM-ISOS and ISOS-RPE thickness by ETDRS subfield were calculated for all participants, subsets of demographic and ocular variables. The ETDRS subfields consisted of the “central subfield”, 1 mm diameter from the centre of the fovea; “inner subfield”, 1 to 3 mm from the centre of the fovea and the “outer subfield”, 3 to 6 mm from the centre of the fovea. Both the inner and outer subfields were measured in four sectors (superior, inferior, nasal and temporal).
First, univariate models were constructed examining the association of risk factors (age, sex, race, smoking status, systolic blood pressure (SBP), refraction, IOPcc and corneal hysteresis) with INL-RPE, INL-ELM, ELM-ISOS and ISOS-RPE thicknesses among the sectors (outcomes). Subsequently, multivariate models were constructed additionally adjusting for other confounders. Both unstandardized (
The North West Multi-center Research Ethics Committee approved the study (reference no., 06/MRE08/65), in accordance with the tenets of the Declaration of Helsinki. Detailed information about the study is available at the UK Biobank web site (
Of the 502,656 participants in the whole UK Biobank cohort, 133,668 underwent eye examination. Of these, 67,321 participants had SD OCT macular imaging available for analysis at the time of this report. Of the 67,321, there were 51,974 participants with high-quality images. Of these, 19,051 people with high refractive error (>6D or <−6D), visual acuity worse than 0.1 logMAR, IOPcc of <6 mmHg or >21 mmHg, self-reported eye diseases, neurological disorders and diabetes, and participants <40 years or >69 years old were excluded. Thus, the final sample size for the current analysis was 32,923 (Fig. Flowchart showing photoreceptor inclusion and exclusion criteria. D = dioptre; EDTRS = Early Treatment Diabetic Retinopathy Study; IOP = intraocular pressure; logMAR = logarithm of the minimum angle of resolution; OCT = optical coherence tomography; SD = spectral-domain.
Table Demographics of the participants included in the study. Characteristic N Mean ± SD/(%) Age at recruitment (years) 32,923 55.2 ± 8.2 Sex Male 15063 45.8 Females (%) 17860 54.2 Race (%) White 30263 92.3 Chinese 108 0.3 Asian 807 2.5 Black 889 2.7 Mixed/other 737 2.2 Education level (%) Less than ‘O’ level 1942 6.7 ‘O’ level 7030 24.3 ‘A’ level 7740 26.8 Degree and above 12213 42.2 Townsend deprivation index 32,886 −1.1 ± 2.9 Height (cm) 32,821 169.2 ± 9.2 Smoking status (%) Never 18351 55.9 Former 11334 34.5 Current 3145 9.6 Eye laterality (%) Right eye 16325 49.6 Left eye 16598 50.4 Visual acuity (logMAR) 32,923 −0.07 ± 0.09 Refraction (D) 32,923 −0.07 ± 1.91 IOPcc (mmHg) 32,923 15.2 ± 2.9 Systolic blood pressure (mmHg) 32,730 138.3 ± 19.3 Diastolic blood pressure (mmHg) 32,729 81.7 ± 10.6 IOP = intraocular pressure; logMAR = logarithm of the minimum angle of resolution; SD = standard deviation.
The mean and SDs of the INL-RPE thickness in the 9 ETDRS subfields are shown in Fig. Diagrams showing Inner nuclear layer–Retinal pigment epithelium (INL-RPE) thickness (µm) at the central, inner and outer subfields across the 4 sectors.
Figure Graphs showing the mean Inner nuclear layer–Retinal pigment epithelium (INL-RPE) thickness (µm) in the ( Graphs showing the mean Inner nuclear layer–Retinal pigment epithelium (INL-RPE) thickness (µm) in the ( Graphs showing the mean Inner nuclear layer–Retinal pigment epithelium (INL-RPE) thickness (µm) in the (
Compared to younger participants, the mean overall thickness of INL-ELM is higher, while the thickness of ELM-ISOS is lower in older participants. A higher ISOS-RPE thickness was observed among participants aged 40–54 years compared to participants aged 55–69 years. (Figs.
Table Multivariate analysis of demographics and risk factors with the thickness of INL-RPE layer. Central subfield Average Inner Subfield Average Outer Subfield Total Average β 95% CI P β 95% CI P β 95% CI P β 95% CI P Age −0.011 −0.023 (−0.048, 0.002) 0.076 −0.020 −0.028 (−0.045, −0.011) 0.001 −0.053 −0.058 (−0.071, −0.045) <0.001 −0.041 −0.048 (−0.063, −0.034) <0.001 Sex Male Ref Ref Ref Ref Female −1.944 (−2.479, −1.408) <0.001 −1.704 (−2.060, −1.347) <0.001 −1.655 (−1.938, −1.373) <0.001 −1.684 (−1.988, −1.381) <0.001 Race White Ref Ref Ref Ref Chinese −3.903 (−7.081, −0.726) 0.016 −0.831 (−2.959, 1.297) 0.444 −0.442 (−2.127, 1.243) 0.607 −0.28 (−2.093, 1.534) 0.762 Asian −5.868 (−7.070, −4.665) <0.001 −3.581 (−4.382, −2.780) <0.001 −2.804 (−3.437, −2.170) <0.001 −3.01 (−3.691, −2.328) <0.001 Black −11.438 (−12.603, −10.273) <0.001 −7.688 (−8.465, −6.911) <0.001 −6.122 (−6.738, −5.505) <0.001 −6.501 (−7.164, −5.837) <0.001 Mixed/Others −4.286 (−5.530, −3.042) <0.001 −2.826 (−3.657, −1.996) <0.001 −2.426 (−3.084, −1.768) <0.001 −2.6 (−3.308, −1.892) <0.001 Smoking status Never Ref Ref Ref Ref Previous 0.588 (0.187, 0.988) 0.004 0.279 (0.012, 0.546) 0.041 0.093 (−0.118, 0.304) 0.388 0.157 (−0.070, 0.384) 0.176 Current −0.08 (−0.729, 0.568) 0.808 −0.298 (−0.729, 0.134) 0.176 −0.558 (−0.899, −0.217) 0.001 −0.54 (−0.907, −0.173) 0.004 SBP (per 10 mmHg) −0.162 −0.14 (−0.25, −0.04) 0.007 −0.105 −0.06 (−0.13, 0.01) 0.081 −0.208 −0.1 (−0.15, −0.04) 0.001 −0.185 −0.09 (−0.15, −0.03) 0.002 Refraction (per 1D) 0.011 0.121 (0.004, 0.238) 0.042 0.087 0.619 (0.541, 0.697) <0.001 0.130 0.729 (0.667, 0.791) <0.001 0.113 0.683 (0.617, 0.750) <0.001 IOP cornea compensated (mmHg) 0.023 0.135 (0.065, 0.206) <0.001 0.013 0.049 (0.002, 0.096) 0.039 0.012 0.037 (−0.000, 0.074) 0.052 0.013 0.043 (0.003, 0.083) 0.033 Corneal hysteresis −0.003 −0.029 (−0.137, 0.080) 0.606 0.008 0.046 (−0.026, 0.118) 0.213 0.016 0.075 (0.018, 0.132) 0.01 0.014 0.072 (0.010, 0.133) 0.022 *Adjusted for age, gender, ethnicity, townsend deprivation index, height, smoking status, systolic blood pressure, refraction, IOP corneal compensated, and corneal hysteresis. β = standardised beta; Summary of our findings compared to results of other studies. Source Results of our study Other studies Effect of age Thinner INL-RPE, INL-ELM, ELM-ISOS layers with older age Ooto Thicker central INL-ELM layer with older age Harris Thinner central ISOS-RPE layer with older age Effect of sex Men had significantly thicker INL-RPE, INL-ELM, ELM-ISOS layers than women Ooto Won Effect of race Black Britons had thinner INL-RPE, INL-ELM, ELM-ISOS and ISOS-RPE layers compared to Whites NIL Asians and Mixed/Others showed thinner INL-RPE and INL-ELM layers compared to Whites Effect of smoking Current smokers had thinner INL-RPE, ELM-ISOS and ISOS-RPE layers compared to non-smokers Harris Previous smokers had thicker INL-RPE and ISO-RPE layers compared to never smokers Effect of blood pressure Higher blood pressure was associated with a thinner INL-RPE and ELM-ISOS NIL Effect of refractive error Higher positive refractive error was associated with thicker INL-RPE and INL-ELM layers. Ooto Effect of IOP Higher IOPcc was associated with thicker INL-RPE, INL-ELM, ELM-ISOS and ISOS-RPE layers. NIL Inner nuclear layer/outer plexiform layer boundary to external limiting membrane (INL-ELM); external limiting membrane-inner segment outer segment (ELM-ISOS); and inner segment outer segment-retinal pigment epithelium (ISOS-RPE). INL-ELM is a proxy measure of the synaptic terminals, axons and the nucleus of the photoreceptors (OPL_ONL). The ELM-ISOS and ISOS-RPE are proxy measures of the inner segment and outer segment of the photoreceptors respectively. NIL = No other others have been reported and IOP = Intraocular pressure.
Table
The multivariate analysis between ocular and systemic variables and the ELM-ISOS thickness are shown in Table
Table
We report normal photoreceptor thickness metrics for 4 distinct photoreceptor related layers (INL-RPE, INL-ELM, ELM-ISOS and ISOS-RPE) using analysis of the largest known macular SD OCT dataset collected as part of the UK Biobank data resource. Estimating normal retinal photoreceptor layer thickness
Our results showed that, in older UK Biobank participants, the INL-RPE, INL-ELM, ELM-ISOS layers were thinner, except for an increased thickness in central INL-ELM, and no significant association for central INL-RPE. These findings were consistent with a study by Ooto
Our finding that men had significantly thicker INL-RPE, INL-ELM, ELM-ISOS than women is consistent with previous study by Ooto
Previous studies that examined race-related differences were only performed for total macular thickness
Our study showed that compared to participants who did not smoke, the total average of the INL-RPE, ELM-ISOS and ISOS-RPE layers were thinner in current smokers, but there was no significant difference in the INL-ELM (although a similar trend was evident). This finding supports the observation from histology that cigarette smoke exposure leads to disorganized photoreceptor anatomy and thinner photoreceptor layers
Our study showed that higher blood pressure was associated with a thinner INL-RPE and ELM-ISOS. Although oxygenation of the avascular outer retina is mainly from the choriocapillaris
Our study showed that more positive refractive error was associated with thicker photoreceptor thickness (INL-RPE and INL-ELM). There were no studies which examined the association between refractive error and photoreceptor thickness, but Ooto
In this study of participants with IOPcc in the 10–21 mmHg range, we showed that higher IOPcc is associated with thicker INL-RPE and ISOS-RPE. In addition, higher IOPcc results in thicker INL-ELM and ELM-ISOS. A possible explanation could be offered by our previous finding that higher IOP is associated with a thinner RPE-BM complex in the UK Biobank
Our study’s strengths include a large sample size of 32,923 participants, which is 125 times larger than previous studies reporting OCT imaging derived photoreceptor layer thickness
In conclusion, our study provides data for photoreceptor thickness measures and examines the associations of various demographic and risk factors with photoreceptor thickness in a large, multi-ethnic sample (UK Biobank). In addition to confirming known associations between age and sex with photoreceptor layer thickness, we report novel findings of the associations of ethnicity, smoking, systolic blood pressure, refraction, IOPcc and corneal hysteresis with photoreceptor thickness measures. Although future prospective studies are needed, our findings may suggest a predisposition of the effect of these risk factors with photoreceptor thickness. Therefore, clinicians may consider the impact of demographics, systemic and ocular factors when evaluating SD OCT derived measures of photoreceptor thickness.
The research supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. This analysis was supported by the Eranda Foundation via the International Glaucoma Association, UCL ORS & GRS programmes. S.Y.L.C., P.J.F. and P.J.P. received salary support from the NIHR BRC at Moorfields Eye Hospital. P.J.F. received support from the Richard Desmond Charitable Trust, via Fight for Sight, London. P.A.K. is supported by a Clinician Scientist award (CS-2014-14-023) from the National Institute for Health Research. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health. The UK Biobank Eye and Vision Consortium is supported by grants from Moorfields Eye Charity, The NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology and the Alcon Research Institute. SYLC had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. This research used data from the UK Biobank Resource, under data access request number 2112. This analysis was supported by the Eranda Foundation via the International Glaucoma Association in the design and conduct of the study. S.Y.L.C., P.T.K., P.J.F. and P.J.P. received salary support from the NIHR BRC at Moorfields Eye Hospital. PJF received support from the Richard Desmond Charitable Trust, via Fight for Sight, London. No funders had a direct role in the collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; nor in the decision to submit the manuscript for publication.
P.J.P. and P.J.F. contributed to the conception and design of the study. S.Y.L.C., P.J.P. and P.J.F. contributed to the data analyses, data interpretation and wrote the draft of the manuscript. All authors reviewed the results, read and critically revised the manuscript. All authors approved the final manuscript. U.K. Biobank Eye and Vision Consortium members.
S.Y.L.C. and T.A. report no conflict of interest. K.B. reports speaker fees/travel grants/research grants from Novartis, Bayer, Heidelberg, Topcon, Alimera. Q.Y. reports employment by Topcon Medical Systems, Inc. outside the submitted work. P.A.K. reports personal fees from Allergan, personal fees from Topcon, personal fees from Heidelberg Engineering, personal fees from Haag-Streit, personal fees from Novartis, personal fees from Bayer, personal fees from Optos, personal fees from DeepMind, grants from National Institute for Health Research (NIHR), outside the submitted work. C.R. reports employment by Topcon Medical Systems Inc., outside submitted work. P.J.F. reports personal fees from Allergan, Carl Zeiss, Google/DeepMind and Santen, a grant from Alcon, outside the submitted work; P.J.P. reports grants from Topcon Inc, outside the submitted work.
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