August 2012
Volume 53, Issue 9
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Glaucoma  |   August 2012
Determinants of Ganglion Cell–Inner Plexiform Layer Thickness Measured by High-Definition Optical Coherence Tomography
Author Affiliations & Notes
  • Victor T. Koh
    From the Singapore Eye Research Institute and Singapore National Eye Centre, Singapore; the
  • Yih-Chung Tham
    From the Singapore Eye Research Institute and Singapore National Eye Centre, Singapore; the
    Yong Loo Lin School of Medicine, and the
  • Carol Y. Cheung
    From the Singapore Eye Research Institute and Singapore National Eye Centre, Singapore; the
    Yong Loo Lin School of Medicine, and the
  • Wan-Ling Wong
    From the Singapore Eye Research Institute and Singapore National Eye Centre, Singapore; the
    Yong Loo Lin School of Medicine, and the
  • Mani Baskaran
    From the Singapore Eye Research Institute and Singapore National Eye Centre, Singapore; the
    Yong Loo Lin School of Medicine, and the
  • Seang-Mei Saw
    From the Singapore Eye Research Institute and Singapore National Eye Centre, Singapore; the
  • Tien Y. Wong
    From the Singapore Eye Research Institute and Singapore National Eye Centre, Singapore; the
    Yong Loo Lin School of Medicine, and the
    Centre for Eye Research Australia, University of Melbourne, Australia.
  • Tin Aung
    From the Singapore Eye Research Institute and Singapore National Eye Centre, Singapore; the
    Yong Loo Lin School of Medicine, and the
  • Corresponding author: Tin Aung, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore 168751; tin11@pacific.net.sg
Investigative Ophthalmology & Visual Science August 2012, Vol.53, 5853-5859. doi:10.1167/iovs.12-10414
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      Victor T. Koh, Yih-Chung Tham, Carol Y. Cheung, Wan-Ling Wong, Mani Baskaran, Seang-Mei Saw, Tien Y. Wong, Tin Aung; Determinants of Ganglion Cell–Inner Plexiform Layer Thickness Measured by High-Definition Optical Coherence Tomography. Invest. Ophthalmol. Vis. Sci. 2012;53(9):5853-5859. doi: 10.1167/iovs.12-10414.

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

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Abstract

Purpose.: To determine the distribution, variation, and determinants of ganglion cell–inner plexiform layer (GC-IPL) thickness in nonglaucomatous eyes measured by high-definition optical coherence tomography (HD-OCT).

Methods.: Six hundred twenty-three Chinese adults aged 40 to 80 years were consecutively recruited from a population-based study. All subjects underwent a standardized interview, ophthalmic examination, and automated perimetry. HD-OCT with macular cube protocol was used to measure the GC-IPL thickness. Univariate and multiple linear regression analyses were performed to examine the relationship between GC-IPL thickness with ocular and systemic factors.

Results.: The mean (±SD) age of study subjects was 52.84 ± 6.14 years, 50.1% were male, and all subjects had normal visual fields with no signs of glaucoma or glaucoma suspect. The mean overall, minimum, superior, and inferior GC-IPL thicknesses were 82.78 ± 7.01 μm, 79.67 ± 9.17 μm, 83.30 ± 7.89 μm, and 80.16 ± 8.31 μm, respectively. In multiple linear regression analysis, GC-IPL thickness was significantly associated with age (β = −0.202, P < 0.001), female sex (β = −2.367, P < 0.001), axial length (β = −1.279, P = 0.002), and mean peripapillary retinal nerve fiber layer (RNFL) thickness (β = 0.337, P < 0.001). IOP, central corneal thickness, disc area, serum glucose level, and history of diabetes mellitus had no significant influence on GC-IPL thickness.

Conclusions.: Thinner GC-IPL was independently associated with older age, female sex, longer axial length, and thinner RNFL thickness. These factors should be taken into account when interpreting GC-IPL thickness measurements with HD-OCT for glaucoma assessment.

Introduction
Glaucoma is a major cause of irreversible blindness and is expected to affect up to 80 million people worldwide by the year 2020, 50% of whom reside in Asia. 1 If diagnosed early, effective treatment can retard visual loss from glaucoma. Ocular imaging tools are widely used to aid in diagnosis and monitor progression of glaucomatous optic neuropathy. The major challenge in screening for glaucoma in the general population is the assessment and detection of early glaucoma accurately, 2,3 in particular to discriminate early glaucomatous damage from normal variability. 4  
High-definition optical coherence tomography (HD-OCT) is a widely used imaging device to assess the peripapillary retinal nerve fiber layer (RNFL) thickness, optic nerve head, and macula. HD-OCT is able to image the retina and its various anatomic layers with high resolution and good reproducibility, 5,6 Advances in segmentation algorithms have further allowed detailed separation and demarcation of individual retinal layers. 5 The retinal ganglion cell layer has been reported to be the early site of glaucomatous damage, as shown in experimental models. 7,8 Recent studies have also shown that the segregated ganglion cell complex (GCC), which is the sum of RNFL, ganglion cell layer, and inner plexiform layers at macular regions, has similar glaucoma discriminating performance compared with RNFL thickness. 9 Nonetheless, it was questioned if the inclusion of RNFL thickness in GCC thickness measurement (from OCT devices such as Fourier-domain RTVue OCT; Optovue Corp., Fremont, CA) may have falsely elevated the diagnostic performance of the GCC. 5,10 In contrast with this GCC measurement, the latest HD-OCT ganglion cell analysis (GCA) algorithm (Cirrus Version 6.0; Carl Zeiss Meditec, Dublin, CA) can successfully demarcate the macular ganglion cell–inner plexiform layer (GC-IPL) while excluding the RNFL. 
Studies on nonglaucomatous subjects using time or spectral-domain OCTs have shown that peripapillary RNFL thickness was influenced by age, sex, ethnicity, axial length, optic disc size, and signal strength. 1014 These findings have been valuable in the clinical interpretation and diagnosis of glaucoma using RNFL thickness measurements. Nonetheless, it remains unknown whether these factors implicated in RNFL measurements have any significant influence on macular GC-IPL thickness. 
The aim of our study was to examine the influences of demographic, ocular, and systemic factors on the measurements of macular GC-IPL thickness using HD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec) in nonglaucomatous Chinese subjects recruited from a population-based study in Singapore. 
Methods
Study Population
The data for this study were derived from the Singapore Chinese Eye Study (SCES), a population-based cross-sectional study of eye diseases in Chinese adults, aged between 40 and 80 years, residing in Singapore. The methodology of the SCES has been reported in detail elsewhere. 15,16 Written informed consent was obtained from each participant. The study adhered to the tenets of the Declaration of Helsinki, and ethics committee approval was obtained from the Singapore Eye Research Institute Institutional Review Board. 
In total, 3353 (72.8% response rate) subjects participated between February 2009 and December 2011. All participants underwent a standardized and comprehensive interview, systemic and ocular examination, and laboratory investigations. 
Study Subjects
The HD-OCT sub-study was conducted between June 2009 and June 2011, and participants were consecutively recruited during this period. 16 For our analyses, we excluded participants based on the following: logarithmic minimal-angle resolution (logMAR) visual acuity >0.5, evidence of macular or vitreoretinal diseases, previous retinal or refractive surgery, neurologic diseases or clinical features compatible with a diagnosis of a glaucoma suspect or glaucomatous visual field defect, and HD-OCT imaging with signal strength less than 6. A glaucoma suspect was defined as having any of the following criteria in the presence of normal visual field: (1) IOP >21 mm Hg, (2) signs consistent with pseudoexfoliation or pigment dispersion syndrome, (3) narrow angles (posterior trabecular meshwork visible for <180° during static gonioscopy), and (4) peripheral anterior synechiae or other findings consistent with secondary glaucoma. 17  
Visual Field Examination
Standardized visual field testing was performed with static automated white-on-white threshold perimetry (SITA Standard 24-2, Humphrey Field Analyzer II; Carl Zeiss Meditec). A visual field was defined as reliable when fixation losses were less than 20%, and false-positive and false-negative rates were less than 33%. A glaucomatous visual field defect was defined as the presence of three or more significant (P < 0.05) non-edge contiguous points with at least one at the P < 0.01 level on the same side of the horizontal meridian in the pattern deviation plot, and classified as “outside normal limits” in the Glaucoma Hemifield Test, confirmed on two consecutive visual field examinations. 
All subjects included for the final analysis had a reliable and normal visual field (without a visual field defect). 
Imaging with Cirrus HD-OCT
The Cirrus HD-OCT is a commercially available device with a scan speed of 27,000 axial scans per second and an axial resolution of 5 μm. 18 After pupil dilation using tropicamide 1% and phenylephrine hydrochloride 2.5%, Cirrus HD-OCT was used to acquire one macular scan using the macular cube 200 × 200 scan protocol in each study eye. The prototype GCA algorithm, incorporated in Cirrus HD-OCT software Version 6, was used to process and measure the thickness of macular GC-IPL within a 14.13 mm2 elliptical annulus area centered on the fovea (Fig. 1A). The GCA algorithm automatically segmented the GC-IPL based on the three-dimensional data generated from the macular cube 200 × 200 scan protocol. The outer boundary of RNFL and outer boundary of IPL at the macular region were segmented by the algorithm; the segmented layer thus led to the measurement of the GC-IPL thickness (Fig. 1B). The average, minimum, and six sectoral (superotemporal, superior, superonasal, inferonasal, inferior, inferotemporal) GC-IPL thicknesses were measured from the elliptical annulus centered on the fovea (Fig. 1C). The elliptical annulus has the following dimensions: vertical inner and outer radius of 0.5 mm and 2.0 mm, respectively, and horizontal inner and outer radius of 0.6 mm and 2.4 mm, respectively. Such size and shape of elliptical annulus were chosen as they conform closely to the real macular anatomy, and the annulus corresponds to the area where the retinal ganglion cell layer is thickest in normal eyes. 5,10 The minimum GC-IPL thickness was defined as the lowest GC-IPL thickness over a single meridian crossing the annulus. 
Figure 1. 
 
Cirrus HD-OCT images of the macula of the right eye. (A) Color-coded topographic map within a 14.13 mm2 elliptical annulus area centered on the fovea. (B) Single horizontal B scan of the macula showing a segmented GC-IPL (GC-IPL thickness was measured between the purple and yellow demarcated lines). (C) Division of central macula into six sectors.
Figure 1. 
 
Cirrus HD-OCT images of the macula of the right eye. (A) Color-coded topographic map within a 14.13 mm2 elliptical annulus area centered on the fovea. (B) Single horizontal B scan of the macula showing a segmented GC-IPL (GC-IPL thickness was measured between the purple and yellow demarcated lines). (C) Division of central macula into six sectors.
A detailed description of the GCA scanning has been reported elsewhere. 5 In brief, during image acquisition, the subject's pupil was first centered and focused in the iris viewport and the line-scanning ophthalmoscope with “auto focus” mode was then used to optimize the view of the retina. The “center” and “enhance” modes were used to optimize the Z-offset and scan polarization, respectively, for the OCT scan in order to maximize the OCT signal. After each capture, motion artifact was checked with the line-scanning ophthalmoscope image with the OCT en face overlaid. Rescanning was performed if a motion artifact (indicated by blood vessel discontinuity) was detected. The HD-OCT scans were excluded if there was presence of retinal layer algorithm segmentation failure. All the HD-OCT scans included in the study had signal strength of at least 6, and we selected one eye from each participant randomly for final analysis. 
Measurement of Ocular Factors
All subjects underwent an ophthalmic examination, including measurement of logMAR best-corrected visual acuity testing, refraction, axial length measurement, IOP measurement, gonioscopy, visual field, and fundus examination. IOP was measured with a Goldmann applanation tonometer (GAT; Haag-Streit, Bern, Switzerland) before pupil dilation. The static refraction of each eye was measured using an autorefractor (Canon RK 5 Auto Ref-Keratometer; Canon Inc., Ltd., Tochigiken, Japan). Spherical equivalent refraction was calculated as the sum of the value of the spherical value and half of the cylindrical value. Central corneal thickness (CCT) was measured with an ultrasound pachymeter (Advent; Mentor O & O, Norwell, MA), and the mean of five measurements was used in the analysis. Axial length (AL) and anterior chamber depth (ACD) were measured with a noncontact partial coherence laser interferometry (IOLMaster Version 3.01; Carl Zeiss Meditec AG, Jena, Germany), and the mean of five measurements was used in the analysis. Lens opacity was assessed by two trained ophthalmologists during the visit, using Lens Opacities Classification System (LOCS) III 19 with a Haag-Streit slit-lamp microscope (Model BQ-900) in comparison with standard photographic slides for nuclear opalescence (NO), nuclear color (NC), cortical and posterior subcapsular (PSC) cataract. 
Measurement of Systemic Factors
Systolic and diastolic blood pressures were measured using a digital automatic blood pressure monitor (Dinamap Model Pro Series DP110X-RW, 100V2; GE Medical Systems Information Technologies, Inc., Milwaukee, WI), after subjects were seated for at least five min. Body mass index (BMI) was calculated as body weight (in kilograms) divided by body height (in meters) squared. Smoking status was defined as those currently smoking, ex-smokers and non-smokers. The number of packs smoked per week was recorded. Nonfasting venous blood samples were analyzed at the National University Hospital Reference Laboratory for biochemical testing of serum total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides, glycosylated hemoglobin (HbA1c), glucose, and creatinine. 
Statistical Analysis
Statistical analysis was performed using SPSS Version 17.0 (SPSS, Inc., Chicago, IL). The mean, SD, and interquartile range of the following parameters were calculated: ocular variables (IOP, spherical equivalent, AL, ACD, CCT, LOCS III score, disc area, vertical cup-to-disc ratio, and RNFL thickness); systemic variables (age, sex, systolic blood pressure, diastolic blood pressure, BMI, smoking, serum glucose, HbA1c, HDL cholesterol, LDL cholesterol, triglycerides, and creatinine); and GC-IPL thickness measured with Cirrus HD-OCT. Univariate and multiple regression analyses were performed to determine ocular and systemic factors (independent variables) association with GC-IPL thickness measurements (dependent variables). For multiple regression model, ocular and systemic parameters with P < 0.2, found in univariate linear regression analysis, were included. 
Results
Of the total 3353 subjects included in SCES, 623 nonglaucomatous Chinese subjects (2437 fulfill the exclusion criteria, and 293 had SD-OCT signal strength of less than 6) met the inclusion criteria and were included for this study. Table 1 shows the subjects' demographics, ocular, systemic, disc area, vertical cup- to-disc ratio, RNFL thickness, and macular GC-IPL thickness. The mean ± SD age was 52.84 ± 6.14 years, and 312 (50.1%) were male. The mean spherical equivalent was −0.96 ± 2.30 diopters (D) and mean axial length was 24.10 ± 1.21 mm. The mean average, superior, and inferior GC-IPL thicknesses (normal distribution) were 82.78 ± 7.01, 83.30 ± 7.89, and 80.16 ± 8.31 μm, respectively. 
Table 1. 
 
Demographic, Systemic, and Ocular Factors and Cirrus HD-OCT Parameters of Study Participants
Table 1. 
 
Demographic, Systemic, and Ocular Factors and Cirrus HD-OCT Parameters of Study Participants
All (n = 623)
Mean SD Interquartile Range
Age (y) 52.84 6.14 (47.88–56.45)
Sex (% male) 50.1
IOP (mm Hg) 14.34 2.72 (12.00–16.00)
Spherical equivalent (D) −0.96 2.30 (−2.13–0.63)
Best corrected visual acuity (logMAR) 0.172 0.180 (0.00–0.400)
AL (mm) 24.10 1.21 (23.25–24.76)
ACD (mm) 3.35 0.34 (3.13–3.57)
CCT (μm) 556.47 32.97 (534.0–580.0)
LOCS III nuclear opalescence 1.71 0.75 (1.20–2.00)
LOCS III nuclear color 1.80 0.73 (1.20–2.00)
LOCS III cortical 0.69 0.77 (0.10–1.00)
LOCS III PSC 0.19 0.32 (0.10–0.10)
MD in HVF −1.33 2.07 (−2.19–0.07)
PSD in HVF 2.34 1.49 (1.49–2.50)
Systolic blood pressure (mm Hg) 129.3 16.8 (118.0–140.0)
Diastolic blood pressure (mm Hg) 77.4 9.7 (70.1–83.0)
BMI (kg/m2) 23.41 3.35 (21.17–25.27)
Serum glucose (mM) 6.07 2.18 (4.90–6.60)
HbA1c (%) 5.94 0.70 (5.60–6.10)
HDL cholesterol (mM) 1.31 0.39 (1.01–1.56)
LDL cholesterol (mM) 3.43 0.88 (2.78–4.00)
Triglycerides (mM) 1.80 1.31 (0.97–2.29)
Creatinine (mM) 71.54 17.63 (57.00–85.00)
Current smoking (%) 14.8 14.8
Diabetes mellitus (%) 5.5  5.5
OCT parameters
Disc area (mm2) 1.68 0.28 (1.68–2.15)
Vertical cup-to-disc ratio 0.387 0.11 (0.420–0.570)
RNFL thickness (μm) 98.02 9.22 (92.05–103.99)
GC-IPL thickness
Signal strength 9.27 0.89 (9.00–1.00)
Superior (μm) 83.30 7.89 (79.00–88.00)
Inferior (μm) 80.16 8.31 (77.00–85.00)
Superonasal (μm) 85.31 7.70 (81.00–90.00)
Inferonasal (μm) 83.21 8.22 (79.00–88.00)
Superotemporal (μm) 81.92 6.96 (78.00–86.00)
Inferotemporal (μm) 82.68 6.88 (79.00–87.00)
Minimum (μm) 79.67 9.17 (76.00–84.00)
Average (μm) 82.78 7.01 (79.00–87.00)
Table 2 shows the univariate analysis between ocular parameters with GC-IPL thickness. A thinner average GC-IPL thickness was significantly correlated with longer axial length (β = −2.06, P < 0.001), more myopic spherical refraction (β = 1.09, P < 0.001), longer average anterior chamber depth (β = −5.04, P < 0.001), lower OCT signal strength (β = 1.53, P < 0.001), higher LOCS III PSC grade (β = −1.91, P = 0.038), and smaller disc area (β = 3.78, P < 0.001). A subanalysis examining the association between GC-IPL thickness and myopia categories was performed. Eyes with spherical equivalent of more than −1.00 D, between −1.00 D and −6.00 D, and less than −6.00 D were classified as group 1, 2, and 3 respectively. Our results showed that both global and sectorial GC-IPL thickness were significantly thinner for eyes in group 3 compared with group 1 or 2. There was no significant association between GC-IPL thickness with IOP, CCT, LOCS III nuclear opalescence, and LOCS III cortical cataract. Figure 2 shows the scatter plots and the linear relationships between mean GC-IPL thickness and ocular parameters. 
Figure 2. 
 
Scatter plots of linear regression between mean GC-IPL thickness and age (A), axial length (B), and average retinal nerve fiber layer thickness (C).
Figure 2. 
 
Scatter plots of linear regression between mean GC-IPL thickness and age (A), axial length (B), and average retinal nerve fiber layer thickness (C).
Table 2. 
 
Univariate Analysis between Ocular Factors with GC-IPL Thickness
Table 2. 
 
Univariate Analysis between Ocular Factors with GC-IPL Thickness
Mean Difference (P Value)
Average Minimum Superior Inferior Superonasal Inferonasal Superotemporal Inferotemporal
IOP (mm Hg) 0.072 (P = 0.523) 0.116 (P = 0.420) 0.100 (P = 0.430) 0.117 (P = 0.381) 0.096 (P = 0.436) 0.145 (P = 0.270) −0.010 (P = 0.929) −0.040 (P = 0.722)
AL (mm) −2.06 (P < 0.001) −1.87 (P < 0.001) −1.99 (P < 0.001) −2.50 (P < 0.001) −2.18 (P < 0.001) −2.54 (P < 0.001) −1.44 (P < 0.001) −1.70 (P < 0.001)
CTT (μm) 0.007 (P = 0.482) 0.008 (P = 0.475) −0.004 (P = 0.733) 0.017 (P = 0.126) 0.000 (P = 0.979) 0.011 (P = 0.290) 0.001 (P = 0.926) 0.011 (P = 0.218)
Spherical equivalent (D) 1.09 (P < 0.001) 1.07 (P < 0.001) 1.04 (P < 0.001) 1.30 (P < 0.001) 1.16 (P < 0.001) 1.33 (P < 0.001) 0.81 (P < 0.001) 0.917 (P < 0.001)
Myopia categories −3.56 (P < 0.001) −3.35 (P < 0.001) −3.50 (P < 0.001) −4.12 (P < 0.001) −3.79 (P < 0.001) −4.45 (P < 0.001) −2.62 (P < 0.001) −2.76 (P < 0.001)
Average ACD (mm) −5.04 (P < 0.001) −5.24 (P < 0.001) −5.30 (P < 0.001) −5.94 (P < 0.001) −6.07 (P < 0.001) −5.81 (P < 0.001) −3.91 (P < 0.001) −3.26 (P < 0.001)
OCT signal strength 1.537 (P < 0.001) 1.99 (P < 0.001) 1.54 (P < 0.001) 1.65 (P < 0.001) 1.76 (P < 0.001) 1.90 (P < 0.001) 1.12 (P = 0.003) 1.23 (P = 0.001)
LOCS III nuclear opalescence −0.010 (P = 0.982) −0.216 (P = 0.713) −0.261 (P = 0.617) 0.192 (P = 0.718) −0.165 (P = 0.714) −0.200 (P = 0.708) 0.134 (P = 0.780) 0.095 (P = 0.843)
LOCS III cortical −0.215 (P = 0.641) −0.154 (P = 0.794) −0.377 (P = 0.471) −0.006 (P = 0.991) −0.238 (P = 0.634) 0.255 (P = 0.633) −0.494 (P = 0.305) −0.525 (P = 0.273)
LOCS III PSC −1.91 (P = 0.038) −2.56 (P = 0.026) −1.37 (P = 0.186) −1.98 (P = 0.065) −1.32 (P = 0.187) −1.28 (P = 0.237) −1.85 (P = 0.052) −3.29 (P = 0.001)
Disc area (mm2) 3.78 (P < 0.001) 3.53 (P = 0.011) 3.58 (P = 0.003) 4.52 (P < 0.001) 3.95 (P = 0.001) 3.42 (P = 0.007) 2.36 (P = 0.032) 4.38 (P < 0.001)
RNFL thickness (μm) 0.401 (P < 0.001) 0.390 (P < 0.001) 0.412 (P < 0.001) 0.399 (P < 0.001) 0.395 (P < 0.001) 0.412 (P < 0.001) 0.402 (P < 0.001) 0.370 (P < 0.001)
Table 3 shows the univariate analysis between systemic parameters with GC-IPL thickness. Age was the only factor that consistently showed a significant relationship with mean (β = −0.23, P < 0.001) and sectoral GC-IPL thickness. This relationship was also shown to be linear in the scatter plots (Fig. 2). Female sex was significantly associated with thinner superotemporal and inferotemporal GC-IPL thickness only. Serum glucose level also significantly influenced average, inferior, and inferonasal GC-IPL but not other sectors. However, GC-IPL was not significantly influenced by HbA1C or presence of diabetes mellitus. Furthermore, GC-IPL thickness did not have a significant relationship with systolic and diastolic blood pressure, total cholesterol (including HDL, LDL, and triglycerides; results not shown), and blood creatinine levels. 
Table 3. 
 
Univariate Analysis between Systemic Factors with GC-IPL Thickness
Table 3. 
 
Univariate Analysis between Systemic Factors with GC-IPL Thickness
Mean Difference (P Value)
Average Minimum Superior Inferior Superonasal Inferonasal Superotemporal Inferotemporal
Age (y) −0.230 (P < 0.001) −0.318 (P < 0.001) −0.236 (P < 0.001) −0.248 (P < 0.001) −0.248 (P < 0.001) −0.244 (P < 0.001) −0.216 (P < 0.001) −0.212 (P < 0.001)
Sex (female vs. male) −0.952 (P = 0.091) −1.277 (P = 0.083) −0.720 (P = 0.255) −0.100 (P = 0.881) −0.756 (P = 0.222) −0.264 (P = 0.690) −2.24 (P < 0.001) −1.849 (P = 0.001)
BMI −0.076 (P = 0.500) −0.097 (P = 0.500) −0.051 (P = 0.686) −0.059 (P = 0.650) −0.110 (P = 0.367) −0.152 (P = 0.245) −0.069 (P = 0.558) −0.015 (P = 0.900)
Systolic blood pressure (mm Hg) −0.005 (P = 0.796) 0.001 (P = 0.971) 0.001 (P = 0.974) 0.000 (P = 0.982) −0.009 (P = 0.695) −0.009 (P = 0.700) −0.006 (P = 0.781) −0.006 (P = 0.770)
Diastolic blood pressure (mm Hg) −0.031 (P = 0.381) −0.016 (P = 0.725) −0.030 (P = 0.460) −0.031 (P = 0.446) −0.031 (P = 0.414) −0.049 (P = 0.231) −0.023 (P = 0.532) −0.014 (P = 0.697)
Smoking status 0.725 (P = 0.143) 1.169 (P = 0.063) 0.606 (P = 0.279) 0.720 (P = 0.206) 0.581 (P = 0.278) 0.537 (P = 0.349) 0.940 (P = 0.069) 0.971 (P = 0.059)
Presence of diabetes mellitus −1.295 (P = 0.396) −0.489 (P = 0.801) −1.513 (P = 0.380) −2.416 (P = 0.168) −1.188 (P = 0.471) −1.472 (P = 0.405) −0.204 (P = 0.898) −1.433 (P = 0.366)
Serum blood glucose (mM) 0.359 (P = 0.038) 0.251 (P = 0.252) 0.343 (P = 0.079) 0.476 (P = 0.016) 0.345 (P = 0.064) 0.407 (P = 0.042) 0.240 (P = 0.183) 0.311 (P = 0.083)
Glycosylated hemoglobin (HbA1C) (%) 0.812 (P = 0.150) 0.339 (P = 0.636) 0.901 (P = 0.158) 0.811 (P = 0.212) −0.310 (P = 0.413) −0.785 (P = 0.063) 0.135 (P = 0.540) −0.156 (P = 0.473)
Total cholesterol (mM) 0.114 (P = 0.735) 0.105 (P = 0.806) 0.039 (P = 0.919) 0.191 (P = 0.624) −0.016 (P = 0.964) −0.082 (P = 0.834) 0.220 (P = 0.532) 0.231 (P = 0.508)
Blood creatinine (mM) 0.018 (P = 0.359) 0.030 (P = 0.231) 0.011 (P = 0.631) 0.002 (P = 0.943) 0.027 (P = 0.205) 0.003 (P = 0.889) 0.034 (P = 0.100) 0.037 (P = 0.070)
Table 4 summarizes the multivariate linear regression analysis showing the independent relationships between ocular and systemic parameters with average GC-IPL thickness. Thinner average GC-IPL thickness was independently related to older age (β = −0.202, P < 0.001), female sex (β = −2.367, P < 0.001), longer axial length (β = −1.279, P = 0.002), and thinner RNFL thickness (β = 0.337, P < 0.001). Average RNFL thickness had the strongest influence on GC-IPL thickness among the ocular parameters (standardized β coefficient, 0.448). 
Table 4. 
 
Multiple Regression Analysis for the Association between Average GC-IPL Thickness with Ocular and Systemic Parameters
Table 4. 
 
Multiple Regression Analysis for the Association between Average GC-IPL Thickness with Ocular and Systemic Parameters
β (Mean Difference) 95% Confidence Interval P Value
Age (per year) −0.202 (−0.304, −0.100) <0.001
Sex (female vs. male) −2.367 (−3.520, −1.214) <0.001
AL (mm) −1.279 (−2.087, −0.472) 0.002
ACD (mm) 0.821 (−1.395, 3.038) 0.467
Serum blood glucose (mM) −0.057 (−0.523, 0.172) 0.321
HbA1C (%) 0.318 (−0.730, 1.367) 0.551
OCT signal strength 0.623 (−0.060, 1.507) 0.074
LOCS III PSC −1.20 (−3.24, 0.843) 0.249
Disc area (mm2) 1.138 (−0.844, 3.119) 0.260
RNFL thickness (μm) 0.337 (0.267, 0.407) <0.001
Discussion
Our study provides the first population-based data of GC-IPL thickness and its determinants in normal nonglaucomatous eyes measured with Cirrus HD-OCT. Our results showed that thinner GC-IPL thickness in nonglaucomatous eyes was independently and significantly associated with older age, female sex, longer axial length, and thinner RNFL thickness. We did not find any significant association between GC-IPL thickness and ocular parameters such as IOP, CCT, ACD, and disc area among the nonglaucomatous group of subjects. Our study also showed that the average GC-IPL layer thickness measured using Cirrus HD-OCT was 82.78 ± 7.01 μm. Sectorial analyses showed that the superonasal and the inferior sectors were the thickest and thinnest sectors, respectively. Compared with females, male subjects had significantly thicker superotemporal and inferotemporal sectors, but the other sectors were not significantly different. 
HD-OCT is now used widely to image the inner retinal layer, which includes the ganglion cell layer, RNFL, and inner plexiform layer. 5,6 The retinal ganglion cell layer is the first cell layer within the retina to be affected by early glaucoma. 7,20 As histologic measurement of the actual number of ganglion cells is impossible in living subjects, GC-IPL thickness provides an alternative and noninvasive surrogate measurement. Previous studies have shown that GCC layer thickness was well correlated with RNFL thickness and visual field sensitivity. 2123 The GCC is usually a single layer in the peripheral retina; but within the macula, the GCC is multilayered and has the highest concentration of ganglion cells. 24 It has been shown that GCC layer measurements showed higher diagnostic ability than peripapillary RNFL parameters in early glaucoma and comparable diagnostic ability in moderate and severe glaucoma. 9,23 However, these aforementioned studies measured GCC layer thickness, which comprised the RNFL, ganglion cell layer, and inner plexiform layers. Our study also showed a significant positive correlation between GC-IPL thickness and RNFL thickness. More studies are needed to determine if incorporating the GC-IPL thickness measurement into classification algorithms may help to improve the sensitivity of Cirrus HD-OCT for early glaucoma detection. 
The neurosensory retina comprises several layers, and the GC-IPL is a deeper layer than the RNFL. Most of the previous studies on the determinants of normal RNFL thickness, measured by spectral-domain OCT, consistently showed that thinner RNFL is associated with increasing age, 2528 being white, 13,26 having a more myopic refraction, 26 and longer axial length. 29 Other predictors of normal RNFL, which were less consistent in the literature, include sex and disc area. 27,28,30 Our results showed that predictors of normal GC-IPL thickness were somewhat similar to normal RNFL thickness. Both GC-IPL and RNFL were significantly influenced by age, sex, and axial length. Histologic studies examining senescent changes in cadaveric human retina were consistent with our results in showing a linear relationship between thinner ganglion cell layer with increasing age. 31,32 The study by Gao and Hollyfield 31 suggested that the ganglion cell layer is more vulnerable to cell loss during aging compared with cone photoreceptors and retinal pigment epithelium. Compared with the European and African eyes, 33 the mean GC-IPL thickness of Chinese eyes measured by Cirrus HD-OCT was comparable (82.1 ± 6.2 μm and 82.78 ± 7.0 μm, respectively). Of interest, when comparing the sectors, the GC-IPL thickness measurements were also similar, and the thickest and thinnest sectors were also superonasal and inferior sectors, respectively. This is in contrast to RNFL thickness, which showed significant ethnic-specific differences. 13,26,34 GC-IPL thickness may thus be a useful parameter that is minimally influenced by ethnicity, although further studies are needed to confirm this. Our results showed that the superonasal sector had the thickest GC-IPL, which is supported by Curcio and Allen 24 in a histologic study that shows greater ganglion cell density in the nasal and superior retinal regions. This finding was also consistent with histologic studies that show that the nasal retina has the highest density of photoreceptors and retinal pigment epithelium cells. 35,36  
Recently, a study by Mwanza et al., 33 which assessed 282 nonglaucomatous subjects who underwent imaging with Cirrus HD-OCT, showed that thinner GC-IPL thickness is significantly associated with thinner RNFL thickness, older age, male sex, and longer axial length. The study shared similarities with ours—same retinal imaging tool, imaging algorithm, and definitions. Subjects from different ethnic groups (43.4% European, 11.7% Hispanics, 18% Africans, 27% Asians) were included, but no interethnic differences were found in GC-IPL thickness. However, the study only examined the association between GC-IPL and ocular parameters in a clinic-based setting. In contrast, our study is a population-based study comprising a large number of nonglaucomatous subjects of a single ethnic Asian group in which both ocular and systemic factors were analyzed. Among these factors, average RNFL thickness showed the strongest effect on GC-IPL thickness. Our results also concurred with the findings by Kim et al., 37 which show a significant relationship between mean GCC layer thickness with age and axial length in 182 Korean healthy subjects. However, the aforementioned study used the RTVue-100 (software Version 4.0.5.39; Optovue, Inc.) OCT machine, which measures the macular GCC layer thickness from the internal limiting membrane to the inner plexiform boundary, and which included the RNFL. In addition, our results showed that the average GC-IPL thickness was not significantly correlated with disc area. This finding is in contrast with studies that found peripapillary RNFL thickness, neural rim area, and vertical cup-to-disc ratio measurements are influenced by disc area. 10,11,16,38  
In multivariate regression analysis, we found that GC-IPL thickness was not significantly influenced by systemic factors such as BMI, systolic and diastolic blood pressure, total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, creatinine, blood glucose levels, HbA1C, presence of diabetes mellitus, or a history of cigarette smoking in this selected group of subjects with clinically normal–looking fundus. Thus, these measurements appear to be robust in subjects with a wide range of systemic factors. In contrast to Van Dijk et al. 39 who reported significant ganglion cell layer thinning in type1 diabetic subjects with no or minimal diabetic retinopathy compared to control subjects, our study did not find any significant association between the presence of diabetes mellitus, HbA1C or blood glucose level with GC-IPL thickness. The inconsistent results may be partly due to differences in inclusion criteria including proportion of type 1 and 2 diabetes mellitus, subject demographics, ocular characteristics, OCT imaging tools, retinal layer segmentation, or levels of diabetic control (Van Dijk et al. 39 did not account for the influence of HbA1C). 
The strengths of the present study include the large unselected population-based sample, standardized assessment of systemic and ocular factors, and the inclusion of laboratory investigations. Our study included only subjects of Chinese ethnicity, which meant our results were unlikely to be biased by ethnicity compared with other studies that included multiple ethnic groups. However, our study had some limitations. First, GC-IPL thickness measurements were obtained from subjects with an age range between 40 and 80 years. Second, as our study is cross-sectional in nature, the causal relationships between GC-IPL thickness and the parameters studied cannot be established. Third, the study population only consisted of subjects with clinically normal–looking fundus. The lack of an association between systemic parameters and the GC-IPL thickness may not reflect in the general population. Further studies are required to investigate the relationship of GC-IPL thickness with systemic conditions. Last, there may be residual confounding factors that we have not controlled for such as other systemic diseases. 
In conclusion, our study showed that thinner GC-IPL thickness in nonglaucomatous Chinese eyes were independently associated with older age, female sex, longer axial length, and thinner RNFL thickness. These factors should be taken into account when interpreting Cirrus HD-OCT–based GC-IPL thickness measurements for glaucoma assessment. 
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Footnotes
 Supported in part by a grant from Carl Zeiss Meditec (TYW, TA).
Footnotes
 Disclosure: V.T. Koh, None; Y.-C. Tham, None; C.Y. Cheung, None; W.-L. Wong, None; M. Baskaran, None; S.-M. Saw, None; T.Y. Wong, Carl Zeiss Meditec (F); T. Aung, Carl Zeiss Meditec (F, R)
Figure 1. 
 
Cirrus HD-OCT images of the macula of the right eye. (A) Color-coded topographic map within a 14.13 mm2 elliptical annulus area centered on the fovea. (B) Single horizontal B scan of the macula showing a segmented GC-IPL (GC-IPL thickness was measured between the purple and yellow demarcated lines). (C) Division of central macula into six sectors.
Figure 1. 
 
Cirrus HD-OCT images of the macula of the right eye. (A) Color-coded topographic map within a 14.13 mm2 elliptical annulus area centered on the fovea. (B) Single horizontal B scan of the macula showing a segmented GC-IPL (GC-IPL thickness was measured between the purple and yellow demarcated lines). (C) Division of central macula into six sectors.
Figure 2. 
 
Scatter plots of linear regression between mean GC-IPL thickness and age (A), axial length (B), and average retinal nerve fiber layer thickness (C).
Figure 2. 
 
Scatter plots of linear regression between mean GC-IPL thickness and age (A), axial length (B), and average retinal nerve fiber layer thickness (C).
Table 1. 
 
Demographic, Systemic, and Ocular Factors and Cirrus HD-OCT Parameters of Study Participants
Table 1. 
 
Demographic, Systemic, and Ocular Factors and Cirrus HD-OCT Parameters of Study Participants
All (n = 623)
Mean SD Interquartile Range
Age (y) 52.84 6.14 (47.88–56.45)
Sex (% male) 50.1
IOP (mm Hg) 14.34 2.72 (12.00–16.00)
Spherical equivalent (D) −0.96 2.30 (−2.13–0.63)
Best corrected visual acuity (logMAR) 0.172 0.180 (0.00–0.400)
AL (mm) 24.10 1.21 (23.25–24.76)
ACD (mm) 3.35 0.34 (3.13–3.57)
CCT (μm) 556.47 32.97 (534.0–580.0)
LOCS III nuclear opalescence 1.71 0.75 (1.20–2.00)
LOCS III nuclear color 1.80 0.73 (1.20–2.00)
LOCS III cortical 0.69 0.77 (0.10–1.00)
LOCS III PSC 0.19 0.32 (0.10–0.10)
MD in HVF −1.33 2.07 (−2.19–0.07)
PSD in HVF 2.34 1.49 (1.49–2.50)
Systolic blood pressure (mm Hg) 129.3 16.8 (118.0–140.0)
Diastolic blood pressure (mm Hg) 77.4 9.7 (70.1–83.0)
BMI (kg/m2) 23.41 3.35 (21.17–25.27)
Serum glucose (mM) 6.07 2.18 (4.90–6.60)
HbA1c (%) 5.94 0.70 (5.60–6.10)
HDL cholesterol (mM) 1.31 0.39 (1.01–1.56)
LDL cholesterol (mM) 3.43 0.88 (2.78–4.00)
Triglycerides (mM) 1.80 1.31 (0.97–2.29)
Creatinine (mM) 71.54 17.63 (57.00–85.00)
Current smoking (%) 14.8 14.8
Diabetes mellitus (%) 5.5  5.5
OCT parameters
Disc area (mm2) 1.68 0.28 (1.68–2.15)
Vertical cup-to-disc ratio 0.387 0.11 (0.420–0.570)
RNFL thickness (μm) 98.02 9.22 (92.05–103.99)
GC-IPL thickness
Signal strength 9.27 0.89 (9.00–1.00)
Superior (μm) 83.30 7.89 (79.00–88.00)
Inferior (μm) 80.16 8.31 (77.00–85.00)
Superonasal (μm) 85.31 7.70 (81.00–90.00)
Inferonasal (μm) 83.21 8.22 (79.00–88.00)
Superotemporal (μm) 81.92 6.96 (78.00–86.00)
Inferotemporal (μm) 82.68 6.88 (79.00–87.00)
Minimum (μm) 79.67 9.17 (76.00–84.00)
Average (μm) 82.78 7.01 (79.00–87.00)
Table 2. 
 
Univariate Analysis between Ocular Factors with GC-IPL Thickness
Table 2. 
 
Univariate Analysis between Ocular Factors with GC-IPL Thickness
Mean Difference (P Value)
Average Minimum Superior Inferior Superonasal Inferonasal Superotemporal Inferotemporal
IOP (mm Hg) 0.072 (P = 0.523) 0.116 (P = 0.420) 0.100 (P = 0.430) 0.117 (P = 0.381) 0.096 (P = 0.436) 0.145 (P = 0.270) −0.010 (P = 0.929) −0.040 (P = 0.722)
AL (mm) −2.06 (P < 0.001) −1.87 (P < 0.001) −1.99 (P < 0.001) −2.50 (P < 0.001) −2.18 (P < 0.001) −2.54 (P < 0.001) −1.44 (P < 0.001) −1.70 (P < 0.001)
CTT (μm) 0.007 (P = 0.482) 0.008 (P = 0.475) −0.004 (P = 0.733) 0.017 (P = 0.126) 0.000 (P = 0.979) 0.011 (P = 0.290) 0.001 (P = 0.926) 0.011 (P = 0.218)
Spherical equivalent (D) 1.09 (P < 0.001) 1.07 (P < 0.001) 1.04 (P < 0.001) 1.30 (P < 0.001) 1.16 (P < 0.001) 1.33 (P < 0.001) 0.81 (P < 0.001) 0.917 (P < 0.001)
Myopia categories −3.56 (P < 0.001) −3.35 (P < 0.001) −3.50 (P < 0.001) −4.12 (P < 0.001) −3.79 (P < 0.001) −4.45 (P < 0.001) −2.62 (P < 0.001) −2.76 (P < 0.001)
Average ACD (mm) −5.04 (P < 0.001) −5.24 (P < 0.001) −5.30 (P < 0.001) −5.94 (P < 0.001) −6.07 (P < 0.001) −5.81 (P < 0.001) −3.91 (P < 0.001) −3.26 (P < 0.001)
OCT signal strength 1.537 (P < 0.001) 1.99 (P < 0.001) 1.54 (P < 0.001) 1.65 (P < 0.001) 1.76 (P < 0.001) 1.90 (P < 0.001) 1.12 (P = 0.003) 1.23 (P = 0.001)
LOCS III nuclear opalescence −0.010 (P = 0.982) −0.216 (P = 0.713) −0.261 (P = 0.617) 0.192 (P = 0.718) −0.165 (P = 0.714) −0.200 (P = 0.708) 0.134 (P = 0.780) 0.095 (P = 0.843)
LOCS III cortical −0.215 (P = 0.641) −0.154 (P = 0.794) −0.377 (P = 0.471) −0.006 (P = 0.991) −0.238 (P = 0.634) 0.255 (P = 0.633) −0.494 (P = 0.305) −0.525 (P = 0.273)
LOCS III PSC −1.91 (P = 0.038) −2.56 (P = 0.026) −1.37 (P = 0.186) −1.98 (P = 0.065) −1.32 (P = 0.187) −1.28 (P = 0.237) −1.85 (P = 0.052) −3.29 (P = 0.001)
Disc area (mm2) 3.78 (P < 0.001) 3.53 (P = 0.011) 3.58 (P = 0.003) 4.52 (P < 0.001) 3.95 (P = 0.001) 3.42 (P = 0.007) 2.36 (P = 0.032) 4.38 (P < 0.001)
RNFL thickness (μm) 0.401 (P < 0.001) 0.390 (P < 0.001) 0.412 (P < 0.001) 0.399 (P < 0.001) 0.395 (P < 0.001) 0.412 (P < 0.001) 0.402 (P < 0.001) 0.370 (P < 0.001)
Table 3. 
 
Univariate Analysis between Systemic Factors with GC-IPL Thickness
Table 3. 
 
Univariate Analysis between Systemic Factors with GC-IPL Thickness
Mean Difference (P Value)
Average Minimum Superior Inferior Superonasal Inferonasal Superotemporal Inferotemporal
Age (y) −0.230 (P < 0.001) −0.318 (P < 0.001) −0.236 (P < 0.001) −0.248 (P < 0.001) −0.248 (P < 0.001) −0.244 (P < 0.001) −0.216 (P < 0.001) −0.212 (P < 0.001)
Sex (female vs. male) −0.952 (P = 0.091) −1.277 (P = 0.083) −0.720 (P = 0.255) −0.100 (P = 0.881) −0.756 (P = 0.222) −0.264 (P = 0.690) −2.24 (P < 0.001) −1.849 (P = 0.001)
BMI −0.076 (P = 0.500) −0.097 (P = 0.500) −0.051 (P = 0.686) −0.059 (P = 0.650) −0.110 (P = 0.367) −0.152 (P = 0.245) −0.069 (P = 0.558) −0.015 (P = 0.900)
Systolic blood pressure (mm Hg) −0.005 (P = 0.796) 0.001 (P = 0.971) 0.001 (P = 0.974) 0.000 (P = 0.982) −0.009 (P = 0.695) −0.009 (P = 0.700) −0.006 (P = 0.781) −0.006 (P = 0.770)
Diastolic blood pressure (mm Hg) −0.031 (P = 0.381) −0.016 (P = 0.725) −0.030 (P = 0.460) −0.031 (P = 0.446) −0.031 (P = 0.414) −0.049 (P = 0.231) −0.023 (P = 0.532) −0.014 (P = 0.697)
Smoking status 0.725 (P = 0.143) 1.169 (P = 0.063) 0.606 (P = 0.279) 0.720 (P = 0.206) 0.581 (P = 0.278) 0.537 (P = 0.349) 0.940 (P = 0.069) 0.971 (P = 0.059)
Presence of diabetes mellitus −1.295 (P = 0.396) −0.489 (P = 0.801) −1.513 (P = 0.380) −2.416 (P = 0.168) −1.188 (P = 0.471) −1.472 (P = 0.405) −0.204 (P = 0.898) −1.433 (P = 0.366)
Serum blood glucose (mM) 0.359 (P = 0.038) 0.251 (P = 0.252) 0.343 (P = 0.079) 0.476 (P = 0.016) 0.345 (P = 0.064) 0.407 (P = 0.042) 0.240 (P = 0.183) 0.311 (P = 0.083)
Glycosylated hemoglobin (HbA1C) (%) 0.812 (P = 0.150) 0.339 (P = 0.636) 0.901 (P = 0.158) 0.811 (P = 0.212) −0.310 (P = 0.413) −0.785 (P = 0.063) 0.135 (P = 0.540) −0.156 (P = 0.473)
Total cholesterol (mM) 0.114 (P = 0.735) 0.105 (P = 0.806) 0.039 (P = 0.919) 0.191 (P = 0.624) −0.016 (P = 0.964) −0.082 (P = 0.834) 0.220 (P = 0.532) 0.231 (P = 0.508)
Blood creatinine (mM) 0.018 (P = 0.359) 0.030 (P = 0.231) 0.011 (P = 0.631) 0.002 (P = 0.943) 0.027 (P = 0.205) 0.003 (P = 0.889) 0.034 (P = 0.100) 0.037 (P = 0.070)
Table 4. 
 
Multiple Regression Analysis for the Association between Average GC-IPL Thickness with Ocular and Systemic Parameters
Table 4. 
 
Multiple Regression Analysis for the Association between Average GC-IPL Thickness with Ocular and Systemic Parameters
β (Mean Difference) 95% Confidence Interval P Value
Age (per year) −0.202 (−0.304, −0.100) <0.001
Sex (female vs. male) −2.367 (−3.520, −1.214) <0.001
AL (mm) −1.279 (−2.087, −0.472) 0.002
ACD (mm) 0.821 (−1.395, 3.038) 0.467
Serum blood glucose (mM) −0.057 (−0.523, 0.172) 0.321
HbA1C (%) 0.318 (−0.730, 1.367) 0.551
OCT signal strength 0.623 (−0.060, 1.507) 0.074
LOCS III PSC −1.20 (−3.24, 0.843) 0.249
Disc area (mm2) 1.138 (−0.844, 3.119) 0.260
RNFL thickness (μm) 0.337 (0.267, 0.407) <0.001
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