You are viewing the site in preview mode

Skip to main content
Download PDF

Macular ganglion cell complex layer thickness measured with spectral-domain OCT in a large population-based cohort study

BMC Ophthalmology volume 25, Article number: 214 (2025) Cite this article

Abstract

Purpose

To establish the normal GCC thickness profile in the general population using SD-OCT in different macular sectors. Determining the systemic and ophthalmic factors associated with GCC thickness and further identifying the potential risk factors were the secondary objectives.

Methods

Participants in the population-based cohort study had to be at least thirty years old. Every participant had a routine ophthalmological examination. Using SD-OCT, the GCC thickness was determined. To assess the relationship between GCC thickness and systemic and ocular characteristics, mixed linear models were used. R V.4.1.1 was the statistical analysis program utilized.

Results

Two thousand four hundred ninety subjects, average age 56.60 ± 10.39 years, were collected in this analysis. GCC average thickness measured was 95.57 ± 7.47 μm. The GCC thickness of the superior (95.46 ± 7.87 μm) was the thinnest, and the inferior subfield (95.68 ± 7.66 μm) was thickest. In the multivariate regression analysis, a thinner GCC was majorly linked to being older (P < 0.001), current smoking (P < 0.001), no diabetes (P = 0.014), and a larger vertical cup disc ratio (VCDR) (P < 0.001). When the data was divided by age, a thinner GCC was also connected to female (P < 0.001), current smoking (P = 0.006), having high systolic blood pressure (SBP) (P = 0.002), lower low-density lipoprotein (LDL) (P = 0.002), higher triglycerides (TG) (P = 0.029), higher best corrected visual acuity (BCVA) (P < 0.001), and a larger vertical cup disc ratio (VCDR) (P = 0.002). Stratified by axial length (AL), a thinner GCC was associated with advanced age (P < 0.001), female (P = 0.005), and a larger VCDR (P = 0.001).

Conclusions

Our study provides benchmarks for GCC thickness, distribution, and linked ocular and systemic factors among middle-aged individuals in rural China, emphasizing the strong correlation between GCC thickness and multiple ocular and systemic variables. Additionally, our findings underscore the importance of establishing global normative databases to address ethnic variations in GCC thickness and the distinctiveness of related ocular and systemic factors.

Peer Review reports

Introduction

Around 8.4 million individuals worldwide suffer from bilateral blindness due to glaucoma, which is one of the main causes of blindness and affects up to 3% of the population over 40 [1, 2]. Glaucoma is characterized by progressive degeneration of retinal ganglion cells (RGC) and the axons, which results in thinning of the retinal nerve fiber layers (RNFL) and irreversible visual field (VF) defects [3,4,5]. The VF is considered the gold standard clinical test for glaucoma diagnosis [6]. Several studies have investigated that RGCs may lose prior to detectable VF defect; therefore, the sensitive and accurate measurement of RGCs was extremely indeed [7,8,9]. The RNFL, the ganglion cell layer (GCL), and the inner plexiform layer (IPL) made up the macular ganglion cell complex (GCC). Since numerous RGCs are within the macula section, the evaluations of GCC are beneficial for glaucoma early detection [10,11,12].

In recent years, spectral-domain OCT (SD-OCT) has been widely used in both scientific research and clinical practice, with high spatial resolution and acquisition speed in a non-invasive way [13,14,15]. The automated segmentation and measuring of specific retinal layers are becoming increasingly possible using SD-OCT technology. The assessment of GCC thickness has been widely used in the diagnosis and monitoring of glaucoma.

As the SD-OCT devices have evolved over time, quite a few changes have been made in OCT regarding reference databases. Previous studies have conducted the parameters are different among each ethnic population [16], yet most normative databases used in SD-OCT software are obtained in Caucasians and limited by a low representation of Asian subjects. Meanwhile, previous studies are usually derived from hospital-based or small sample populations, giving rise to potential selection bias. In addition, high-myopic individuals may also have the relevant optic disc structural changes, such as deformation and thinner pre-laminar tissue, which is like glaucoma; therefore, well-described RGC-specific assessment parameters will be essential to assist clinical diagnosis.

In the present study, the primary objective was to establish the normal GCC thickness profile in the general population using SD-OCT in different macular sectors. Determining the systemic and ophthalmic factors associated with GCC thickness and further identifying the potential risk factors was the secondary objective.

Methods

Study population

A population-based cohort study, the Handan Eye Study (HES), involved participants in Handan, Hebei Province, northern China, who were at least thirty years of age. The detailed methodologies of HES have been illustrated elsewhere [17]. Briefly, 6830 individuals (response rate: 90.4%) took part in HES at baseline phase in 2006–2007. While 5394 subjects (response rate: 85.3%) of the survivors participated in the follow-up study during 2012–2013. As the OCT was not conducted in the baseline visits, the data used in this study was from the follow-up visits. The study protocol was approved by the Beijing Tongren Hospital Ethics Committee (TREC2006-22), and written informed consent was obtained from all the participants, according to the Declaration of Helsinki.

Meanwhile, we exclude individuals with glaucoma (i.e., optic disc with hemorrhage, thinning retinal nerve fiber layer thickness, and any glaucomatous visual field defects) or intraocular pressure (IOP) > 21 mmHg, retinal disease (like diabetic retinopathy or diabetic macular edema), and other vitreoretinal diseases. Eyes having pathologic retinal lesions or spherical equivalents (SEs) less than −8.0 diopter (D) were further disqualified to further separate out the eyes with pathologic myopia. As a result, a total of 4548 eyes from 2552 patients were enrolled in this study.

Systemic examination

All participants underwent a standard systematic and ocular examination by well-trained and experienced ophthalmologists. Information on demographics, educational backgrounds, and medical histories (including smoking, drinking, diabetes, hypertension, and known significant systemic disorders) were gathered through the questionnaire. Weight divided by height was used to compute the body mass index, or BMI. Blood was drawn for biochemical analysis, which included measurements of blood urea nitrogen, serum creatinine, fasting glucose, and lipid indicators such as total cholesterol, total TG, LDL and HDL.

Ocular examination

The obedient participants had slit-lamp biomicroscopic (Topcon SL-2F; www.global.topcon.com) and had their IOP monitored using a Kowa applanation tonometer HA-2 (Kowa Company Ltd. Tokyo, Japan). Visual acuity was tested monocularly and then binocularly at 4 m using a log MAR chart. AL was examined with a 10 MHz A/B-mode ultrasound device (Cine Scan, Quantel Medical, Clermont-Ferrand, France). The refraction and corneal curvature were measured using an autorefractor (KR8800, Topcon, Tokyo, Japan) without pupil dilation.

OCT measurement procedures

A skilled operator performed GCC thickness scans with a spectral-domain OCT (RTVue 100–2, Optovue, Inc., Fremont, CA; version 4.0). The device used a near-infrared light source with a wavelength of 840 nm and obtained 26,000 A-scans per second. The optic disc was manually positioned in the middle of the scanned image, with 12 radial scans (ranging from 1.3 to 4.9 mm) and 6 concentric rings. The scan imaging covered a measurement area of 3.4 mm in diameter ring that included the optic nerve head and surroundings. The single scan with the best quality was selected from the 3-time scan repeats for each eye analysis. The completed cross-sectional images were acquired in individual radial scans, and without artifacts (boundary errors or decentration), they were acceptable. All GCC model images were manually checked to ensure that the optic nerve head was evident in the center of the scan. Various optic nerve head parameters were measured by algorithms native to RTVue OCT automatically. Results were considered credible if SSI (signal strength index) ≥ 45. Moreover, one glaucoma specialist reviewed those scans carefully for abnormalities. Those that did not meet the criteria were excluded further from the analysis.

Statistical methods

Statistics (macular paper)

The statistical analysis was carried out using the R software (version 4.1.1; R Core Team, 2021). Descriptive statistics were utilized to describe the demographic, RT, and other characteristics of the included and excluding groups. The normality distribution and variance homogeneity of continuous variables were assessed, and the t-test or Wilcoxon rank sum test was used. For ordered data, the chi-square test was used, whereas the non-parametric Wilcoxon rank sum test was used for disordered data. The relationships between RT (central macular subfield, average inner ring, and average outer ring) and biochemical traits were then examined using univariate and multivariate linear regression models. First, we used a single-factor mixed-effects regression model to estimate the relationship between behavioral traits, medical history, central macular thickness, inner and outer ring measurements, and ocular parameters. Secondly, variables that were clinically important or statistically significant (P ≤ 0.1) were included in the multi-factor mixed-effects regression model. The all-subset selection procedure was then used to select the most relevant variables. P values < 0.05 were considered statistically significant.

Results

Comparisons of demographic and biochemical characteristics between female and male

During the Handan eye study follow-up period, 4793 of 5394 underwent the SD-OCT examination. The demographic features of the research population were described in Table 1. In total, 2490 subjects with healthy and high-quality imaging incorporated into the analysis (1379 men and 1111 women). The average age was 56.60 ± 10.39 years, with a range of ages from 35 to 86. Compared the demographic parameters in two groups, the male tended to be significantly higher age (P < 0.001), higher level of education (P < 0.001), current smoking (P < 0.001), lower BMI (P < 0.001), less hypertension (P = 0.011), lower lipids (P < 0.001), less diabetes (P < 0.001), lower keratometry (P < 0.001), lower SE (P < 0.001), lower BCVA (P < 0.001), lower IOP (P < 0.001), longer AL (P < 0.001), deeper Anterior chamber depth (ACD) (P < 0.001), enlarged VCDR (P < 0.001). The differences in other features between the two groups are given in Table 1.

Table 1 Gender differences in demographics and biochemistry
Full size table

Distribution of GCC thickness in healthy eyes

Table 2 displayed the normative profile of overall, superior, and inferior subfields in 1%, 5%, 50%, 95%, and 99% percentiles. All parameters were represented as mean ± standard deviation (M ± SD). It was evident that the GCC thickness of the superior (95.46 ± 7.87 μm) was the thinnest, followed by the average thickness (95.57 ± 7.47 μm), and that of the inferior subfield (95.68 ± 7.66 μm) was thickest.

Table 2 Distribution of ganglion cell complex thickness in healthy eyes
Full size table

The correlation between gender and age in GCC

GCC thickness was expressed by the mean and standard deviation in each age group, and the changing trend with age for each measurement was significant for each measurement (Table 3).

Table 3 Distribution of ganglion cell complex thickness in healthy eyes by age
Full size table

Five age groups were created from individuals: under 39, 40 to 49, 50 to 59, 60 to 69, and over 70 years. All GCC thickness subfields were thickest in the under-39 age group and thinnest in the over-70 age group. Figure 1 depicts a general tendency toward thinner GCC thickness as age increases.

Fig. 1
figure 1

GCC thickness changes overall, inferior subfield and superior subfield along with age. The smooth curves in the figure represent the average GCC thickness in three subfields and the purple areas represent the 95% GCC thickness confidence interval

Full size image

The tendency for sex variation was demonstrated in Table 4. Compared with males, females had significantly thinner GCC thickness in the inferior subfield (P < 0.001).

Table 4 Distribution of ganglion cell complex thickness in healthy eyes by gender
Full size table

Average GCC thickness and its relationship to demographic and ocular characteristics

To estimate the independent relationships between demographic and ocular factors and average GCC thickness, univariate and multivariate regression models were employed (Table 5). The result demonstrated that thinner GCC thickness was substantially associated with older age (P < 0.001), female (P = 0.038), limited education (P < 0.001), current smoking (P = 0.006), higher SBP (P < 0.001) higher LDL (P = 0.015), without diabetes (P < 0.001), higher HbA1c (P = 0.007), and better VCDR (P < 0.001). In the multivariate model, the variables that were determined to be statistically significant in the univariate model or factors having clinical value were further included. Results from the multivariate model demonstrated that a thinner GCC was majorly linked to being older (P < 0.001), current smoking (P < 0.001), no Diabetes (P = 0.014), and a larger vertical cup disc ratio (VCDR) (P < 0.001) (Table 5). Generally, among systemic factors, age appears to have the strongest association with GCC thickness, while AL is most influential among ophthalmic factors. Accordingly, we performed univariate and multivariate analyses stratified by age and AL to account for potential confounding effects. When the data was divided by age, mean GCC thickness was also connected to female (P < 0.001), smoking (P = 0.006), SBP (P = 0.002), LDL (P = 0.002), TG (P = 0.029), BCVA (P < 0.001), and VCDR (P = 0.002) (Table S2). Stratified by AL, mean GCC thickness was associated with age (P < 0.001), female (P = 0.005), and VCDR (P = 0.001) (Table S3).

Table 5 The regression models for the relationship between demographic and biochemical characteristics and average GCC thickness
Full size table

Figure 2 has intuitively shown the relationships of these parameters. GCC thickness decreased with age, while male subjects had thicker GCC thickness than females. Subjects who smoked did not have diabetes; lower HbA1c had thicker GCC thickness than others. However, education and HbA1c also had an inconsistent effect on GCC thickness, and the details were shown in Fig. 2.

Fig. 2
figure 2

The relationship between demographic characteristics, medical history, behavior, ocular parameters, and GCC thickness using multivariate linear regression analysis in overall, inferior, and superior subfields

Full size image

Discussion

We evaluated the GCC thickness and demonstrated normative profile distribution by SD-OCT in a Chinese rural adult population. The results demonstrated that the GCC thickness in the inferior subfield was the thickness, followed by the average GCC thickness, and the superior was the thinnest. In addition, the GCC thickness thinned with age, while other factors such as gender, smoking, diabetes, HbA1c, SBP, TG, LDL, BCVA, and VCDR were demonstrated to be connected to the thickness of the GCC.

In this study, the average GCC thickness measured 95.57 ± 7.47 μm, with the lower and upper subfields at 95.68 ± 7.66 μm and 95.46 ± 7.87 μm respectively. These values were slightly thicker than those reported in Chansangpetch S’s [16] study (91.25 μm, 90.82 μm, 91.70 μm) but thinner compared to other previous studies [18, 19] and the normative database, which lists an average GCC thickness of 105.3 ± 7.0 μm for 3D-OCT and 98.28 ± 9.31 μm for RTVue (Table S1).The variations may result from the various SD-OCT equipment [20], as well as variations in the demographics of the subjects, ethnic groups, and study inclusion criteria. Measurements from different OCT devices are not transferable [21], due to SD-OCT instruments using a different segmentation algorithm of the posterior boundary for measuring GCC thickness, and every OCT evaluates measures of several retinal total surfaces.

Previous studies have revealed that age is a significant factor influencing the distribution of GCC thickness. Bloch et al. also indicated that the average GCC thickness and all quadrants dropped considerably with age [18]. The reason may be caused by the RNFL thickness decreasing by 2–4 μm per ten years of age in individuals over 50 years, while the histological analysis also discovered that age-related RNFL thinning [22]. The explanation for how aging affects the RNFL layer could be related to the fundus's declining blood supply and the optic nerve's senescence apoptosis as aging advances [23]. Furthermore, as age progresses, systemic factors influencing GCC thickness assessment should not be ignored. To improve the accuracy of GCC thickness measurement, it is essential to synthesize data from numerous studies and determine a pattern of GCC thickness degradation with age. However, GCC thickness for different age groups may need to be taken into consideration independently.

Different from the results of the UK Biobank study [24], there was no discernible variation in the average GCC thickness between males (95.79 ± 7.58) and females (95.39 ± 7.38), which was similar to the results that no gender differences were found in the GCC's mean thickness in a Chinese sample as well [25] and another study conducted in Europeans [18]. However, after conducting a multivariate analysis stratified by age and AL, we found that gender influences the average thickness of the GCC. Similar to a study conducted in animal models, because females may have a higher ratio of parvocellular to magnocellular retina ganglion cells than males do, the study showed that females have thinner retinas than males [26].

For systematic parameters, multiple studies have suggested that hypertension was linked with GCC thinning, which may point to a crucial concern when utilizing OCT measures to make diagnoses [18, 27,28,29]. Therefore, we investigated the relationship between GCC thickness and systolic and diastolic blood pressure. The results have shown that GCC thickness is thinner in individuals with SBP in the multivariate analysis.

There were also varieties of studies that have displayed that diabetes may affect the retina and lead to neurodegenerative disease, with thinning GCC and RNFL thickness [30,31,32,33,34]. Thangamathesvaran et al. [30] revealed that GCC loss was observed in people with diabetes for ten to twenty years. This suggested that ganglion cell loss may occur prior to the development of diabetic retinopathy, indicating the beginning of neuronal death prior to any alterations in the vasculature. We did find a correlation between a thinner GCC and HbA1c (P = 0.049) and increasing TG (P < 0.001), which was consistent with the previous study [35].

Other than age, gender, and systematic parameters, it was found in the multivariate regression analysis that other ocular factors were associated with GCC thickness. GCC increased with the decreased keratometry, BCVA and VCDR; the reason might be this study hasn’t included the ocular magnification in the analysis, which compounding elements have a crucial effect on the GCC thickness and how it relates to related factors. The data included in our study was not corrected prior to the picture collection of the 3000 participants since the Optovue-RTVue100 instrument was not intended to fix the magnification after imaging [36]. However, the pathogenesis of glaucoma is diverse and remains unclear to this day [37, 38]. Meanwhile, in line with earlier research, we did not find a correlation between IOP and GCC thickness in this investigation [18, 39, 40]. Although IOP beyond threshold will harm ganglion cells at the lamina cribrosa [18], there is growing evidence for the role of the translaminar cribrosa pressure differential (TLCPD, IOP minus cerebrospinal fluid pressure), and the relationship between IOP and intracranial may be associated with the pathogenesis of glaucoma [41].

Since normative profiles from several manufacturers are derived from samples that contained low proportions of Asian individuals, the RTVue, Cirrus, and Spectralis databases have 22%, 24%, and 7% Asians, respectively. Thus, the Chinese people may receive incorrect diagnoses because of the built-in deviation plot. In our study, we presented the distribution, including mean and standard deviation, 1, 5, 50, 95, and 99 percentiles of GCC thickness, and may assist ophthalmologists in understanding the racial differences. However, compared to a mixed-racial normative database, the accuracy of employing a race-specific normative database is still unclear, necessitating more research.

In contrast to earlier hospital-based research, the selection bias was reduced in the population-based HES study. Furthermore, the association of GCC thickness with a variety of systemic and ocular factors in a standardized pattern was analyzed. However, there were still some limitations to our study. Firstly, as our study was cross-sectional and population-based, it was not possible to determine a causal relationship between the characteristics examined and the GCC; therefore, we were unable to make any inferences. Secondly, although this study excluded the abnormal eyes with possible pathology on OCT, the inclusion of the individuals who underwent cataract surgery may be the potential confounders for the results. Thirdly, about 40% of the participants in our study were eliminated in the final analysis because of the rigorous inclusion criteria, which only comprised healthy eyes, which may lead to potential susceptibility to missing data. Thus, there may be limitations to the generalizability of the findings of our investigation.

In conclusion, our study provides normative profile data of GCC thickness, distribution patterns, and correlated systemic and ocular parameters in a middle-aged cohort of rural population in China. We demonstrated the association between GCC thickness and gender, smoking, diabetes, HbA1c, SBP, TG, LDL, BCVA, and VCDR. When using GCC to make clinical judgments about glaucoma and other visual neuropathies marked by the loss of retinal ganglion cells, age is a significant factor to consider. GCC thickness was not shown to be a characteristic that was independently correlated with BMI, IOP, and AL. To learn more about the associated components that underline GCC thickness, more research is needed.

Data availability

All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.

References

  1. Varma R, Lee PP, Goldberg I, Kotak S. An assessment of the health and economic burdens of glaucoma. Am J Ophthalmol. 2011;152(4):515–22.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Quigley HA, Broman AT. The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol. 2006;90(3):262–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Drexler W, Fujimoto JG. State-of-the-art retinal optical coherence tomography. Prog Retin Eye Res. 2008;27(1):45–88.

    Article  PubMed  Google Scholar 

  4. Wang WW, Wang HZ, Liu JR, Zhang XF, Li M, Huo YJ, Yang XG. Diagnostic ability of ganglion cell complex thickness to detect glaucoma in high myopia eyes by Fourier domain optical coherence tomography. Int J Ophthalmol. 2018;11(5):791–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Chen HS, Liu CH, Wu WC, Tseng HJ, Lee YS. Optical Coherence Tomography Angiography of the Superficial Microvasculature in the Macular and Peripapillary Areas in Glaucomatous and Healthy Eyes. Invest Ophthalmol Vis Sci. 2017;58(9):3637–45.

    Article  PubMed  Google Scholar 

  6. Quigley HA, Addicks EM, Green WR. Optic nerve damage in human glaucoma. III. Quantitative correlation of nerve fiber loss and visual field defect in glaucoma, ischemic neuropathy, papilledema, and toxic neuropathy. Arch Ophthalmol. 1982;100(1):135–46.

    Article  CAS  PubMed  Google Scholar 

  7. Quigley HA, Dunkelberger GR, Green WR. Retinal ganglion cell atrophy correlated with automated perimetry in human eyes with glaucoma. Am J Ophthalmol. 1989;107(5):453–64.

    Article  CAS  PubMed  Google Scholar 

  8. Kerrigan-Baumrind LA, Quigley HA, Pease ME, Kerrigan DF, Mitchell RS. Number of ganglion cells in glaucoma eyes compared with threshold visual field tests in the same persons. Invest Ophthalmol Vis Sci. 2000;41(3):741–8.

    CAS  PubMed  Google Scholar 

  9. Esporcatte BLB, Kara-José AC, Melo LAS Jr, Pinto LM, Tavares IM. The Estimates of Retinal Ganglion Cell Counts Performed Better than Isolated Structure and Functional Tests for Glaucoma Diagnosis. J Ophthalmol. 2017;2017:2724312.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Oddone F, Lucenteforte E, Michelessi M, Rizzo S, Donati S, Parravano M, Virgili G. Macular versus Retinal Nerve Fiber Layer Parameters for Diagnosing Manifest Glaucoma: A Systematic Review of Diagnostic Accuracy Studies. Ophthalmology. 2016;123(5):939–49.

    Article  PubMed  Google Scholar 

  11. Ustaoglu M, Solmaz N, Onder F. Discriminating performance of macular ganglion cell-inner plexiform layer thicknesses at different stages of glaucoma. Int J Ophthalmol. 2019;12(3):464–71.

    PubMed  PubMed Central  Google Scholar 

  12. Holló G. Optical coherence tomography angiography in glaucoma: analysis of the vessel density-visual field sensitivity relationship. Ann Transl Med. 2020;8(18):1203.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Rebolleda G, González-López JJ, Muñoz-Negrete FJ, Oblanca N, Costa-Frossard L, Álvarez-Cermeño JC. Color-code agreement among stratus, cirrus, and spectralis optical coherence tomography in relapsing-remitting multiple sclerosis with and without prior optic neuritis. Am J Ophthalmol. 2013;155(5):890–7.

    Article  PubMed  Google Scholar 

  14. Petzold A, de Boer JF, Schippling S, Vermersch P, Kardon R, Green A, Calabresi PA, Polman C. Optical coherence tomography in multiple sclerosis: a systematic review and meta-analysis. Lancet Neurol. 2010;9(9):921–32.

    Article  PubMed  Google Scholar 

  15. Tan ACS, Tan GS, Denniston AK, Keane PA, Ang M, Milea D, Chakravarthy U, Cheung CMG. An overview of the clinical applications of optical coherence tomography angiography. Eye (Lond). 2018;32(2):262–86.

    Article  CAS  PubMed  Google Scholar 

  16. Chansangpetch S, Huang G, Coh P, Oldenburg C, Amoozgar B, He M, Lin SC. Differences in Optic Nerve Head, Retinal Nerve Fiber Layer, and Ganglion Cell Complex Parameters Between Caucasian and Chinese Subjects. J Glaucoma. 2018;27(4):350–6.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Cao K, Hao J, Zhang Y, Hu AL, Yang XH, Li SZ, Wang BS, Zhang Q, Hu JP, Lin CX, et al. Design, methodology, and preliminary results of the follow-up of a population-based cohort study in rural area of northern China: Handan Eye Study. Chin Med J (Engl). 2019;132(18):2157–67.

    Article  PubMed  Google Scholar 

  18. Bloch E, Yonova-Doing E, Jones-Odeh E, Williams KM, Kozareva D, Hammond CJ. Genetic and Environmental Factors Associated With the Ganglion Cell Complex in a Healthy Aging British Cohort. JAMA Ophthalmol. 2017;135(1):31–8.

    Article  PubMed  Google Scholar 

  19. Holló G, Naghizadeh F, Vargha P. Accuracy of macular ganglion-cell complex thickness to total retina thickness ratio to detect glaucoma in white Europeans. J Glaucoma. 2014;23(8):e132-137.

    Article  PubMed  Google Scholar 

  20. Staurenghi G, Sadda S, Chakravarthy U, Spaide RF. International Nomenclature for Optical Coherence Tomography P: Proposed lexicon for anatomic landmarks in normal posterior segment spectral-domain optical coherence tomography: the IN*OCT consensus. Ophthalmology. 2014;121(8):1572–8.

    Article  PubMed  Google Scholar 

  21. Muñoz-Gallego A, De la Cruz J, Rodríguez-Salgado M, Torres-Peña JL. de-Lucas-Viejo B, Ortueta-Olartecoechea A, Tejada-Palacios P: Assessment of macular ganglion cell complex using optical coherence tomography: Impact of a paediatric reference database in clinical practice. Clin Exp Ophthalmol. 2019;47(4):490–7.

    Article  PubMed  Google Scholar 

  22. Alasil T, Wang K, Keane PA, Lee H, Baniasadi N, de Boer JF, Chen TC. Analysis of normal retinal nerve fiber layer thickness by age, sex, and race using spectral domain optical coherence tomography. J Glaucoma. 2013;22(7):532–41.

    Article  PubMed  Google Scholar 

  23. Hoffmann EM, Schmidtmann I, Siouli A, Schuster AK, Beutel ME, Pfeiffer N, Lamparter J. The distribution of retinal nerve fiber layer thickness and associations with age, refraction, and axial length: the Gutenberg health study. Graefes Arch Clin Exp Ophthalmol. 2018;256(9):1685–93.

    Article  PubMed  Google Scholar 

  24. Patel PJ, Foster PJ, Grossi CM, Keane PA, Ko F, Lotery A, Peto T, Reisman CA, Strouthidis NG, Yang Q. Spectral-Domain Optical Coherence Tomography Imaging in 67 321 Adults: Associations with Macular Thickness in the UK Biobank Study. Ophthalmology. 2016;123(4):829–40.

    Article  PubMed  Google Scholar 

  25. Wang J, Gao X, Huang W, Wang W, Chen S, Du S, Li X, Zhang X. Swept-source optical coherence tomography imaging of macular retinal and choroidal structures in healthy eyes. BMC Ophthalmol. 2015;15:122.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Salyer DL, Lund TD, Fleming DE, Lephart ED, Horvath TL. Sexual dimorphism and aromatase in the rat retina. Brain Res Dev Brain Res. 2001;126(1):131–6.

    Article  CAS  PubMed  Google Scholar 

  27. Lim HB, Lee MW, Park JH, Kim K, Jo YJ, Kim JY. Changes in Ganglion Cell-Inner Plexiform Layer Thickness and Retinal Microvasculature in Hypertension: An Optical Coherence Tomography Angiography Study. Am J Ophthalmol. 2019;199:167–76.

    Article  PubMed  Google Scholar 

  28. Sahin OZ, Sahin SB, Ayaz T, Karadag Z, Turkyilmaz K, Aktas E, Bostan M. The impact of hypertension on retinal nerve fiber layer thickness and its association with carotid intima media thickness. Blood Press. 2015;24(3):178–84.

    Article  CAS  PubMed  Google Scholar 

  29. Akay F, Gündoğan FC, Yolcu U, Toyran S, Tunç E, Uzun S. Retinal structural changes in systemic arterial hypertension: an OCT study. Eur J Ophthalmol. 2016;26(5):436–41.

    Article  PubMed  Google Scholar 

  30. Thangamathesvaran L, Kommana SS, Duong K, Szirth B, Khouri AS. Ganglion cell complex loss in patients with type 1 diabetes: A 36-month retrospective study. Oman J Ophthalmol. 2019;12(1):31–6.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Srivastav K, Saxena S, Mahdi AA, Kruzliak P, Khanna VK. Increased serum urea and creatinine levels correlate with decreased retinal nerve fibre layer thickness in diabetic retinopathy. Biomarkers. 2015;20(6–7):470–3.

    Article  CAS  PubMed  Google Scholar 

  32. Salvi L, Plateroti P, Balducci S, Bollanti L, Conti FG, Vitale M, Recupero SM, Enrici MM, Fenicia V, Pugliese G. Abnormalities of retinal ganglion cell complex at optical coherence tomography in patients with type 2 diabetes: a sign of diabetic polyneuropathy, not retinopathy. J Diabetes Complications. 2016;30(3):469–76.

    Article  PubMed  Google Scholar 

  33. Chhablani J, Sharma A, Goud A, Peguda HK, Rao HL, Begum VU, Barteselli G. Neurodegeneration in Type 2 Diabetes: Evidence From Spectral-Domain Optical Coherence Tomography. Invest Ophthalmol Vis Sci. 2015;56(11):6333–8.

    Article  PubMed  Google Scholar 

  34. Demir M, Oba E, Sensoz H, Ozdal E. Retinal nerve fiber layer and ganglion cell complex thickness in patients with type 2 diabetes mellitus. Indian J Ophthalmol. 2014;62(6):719–20.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Sinaga SR, Sari MD. The Role of High-Density Lipoprotein Cholesterol to Ganglion Cell Complex Thickness in Primary Open-Angle Glaucoma with a History of Type 2 Diabetes Mellitus. European Modern Studies Journal. 2021;5:105–13.

    Google Scholar 

  36. Hirasawa K, Shoji N, Yoshii Y, Haraguchi S. Comparison of Kang’s and Littmann’s methods of correction for ocular magnification in circumpapillary retinal nerve fiber layer measurement. Invest Ophthalmol Vis Sci. 2014;55(12):8353–8.

    Article  PubMed  Google Scholar 

  37. Jonas JB, Nangia V, Wang N, Bhate K, Nangia P, Nangia P, et al. Trans-lamina cribrosa pressure difference and open-angle glaucoma. The central India eye and medical study. PLoS One. 2013;8(12):e82284. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pone.0082284.

  38. Nongpiur ME, Khor CC, Jia H, Cornes BK, Chen LJ, Qiao C, et al. ABCC5, a gene that influences the anterior chamber depth, is associated with primary angle closure glaucoma. PLoS Genet. 2014;10(3):e1004089. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pgen.1004089.

  39. Wang X, Li SM, Liu L, Li S, Li L, Kang M, Wei S, Wang N. An analysis of macular ganglion cell complex in 7-year-old children in China: the Anyang Childhood Eye Study. Transl Pediatr. 2021;10(8):2052–62.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Cheng L, Wang M, Deng J, Lv M, Jiang W, Xiong S, Sun S, Zhu J, Zou H, He X, et al. Macular Ganglion Cell-Inner Plexiform Layer, Ganglion Cell Complex, and Outer Retinal Layer Thicknesses in a Large Cohort of Chinese Children. Invest Ophthalmol Vis Sci. 2019;60(14):4792–802.

    Article  PubMed  Google Scholar 

  41. Wang N, Xie X, Yang D, Xian J, Li Y, Ren R, Peng X, Jonas JB, Weinreb RN. Orbital cerebrospinal fluid space in glaucoma: the Beijing intracranial and intraocular pressure (iCOP) study. Ophthalmology. 2012;119(10):2065-2073.e2061.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Beijing Tongren Hospital, Handan Eye Hospital and all survey participants are greatly appreciated for their valuable contributions.

Funding

The study is supported by the National Natural Science Foundation of China (GZR-2012–009), the Beijing Traditional Chinese Medicine Technology Development Fund Project (JJ2018-50)and Beijing Municipal Public Welfare Development and Reform Pilot Project for Medical Research Institutes (PWD&RPP-MRI, JYY2023-6).

Author information

Author notes

  1. Caixia Lin and Lixia Ma contributed to the work equally and should be regarded as co-first authors.

  2. Jian Wu and Ningli Wang have made equal contributions and should be regarded as co-corresponding authors.

Authors and Affiliations

  1. Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Key Laboratory of Intelligent Diagnosis Technology and Equipment for Optic Nerve-Related Eye Diseases, Capital Medical University, Beijing, 100730, China

    Caixia Lin, Hongyi Liu, Kai Cao & Ningli Wang

  2. Henan Academy of Innovations in Medical Science, Zhengzhou Aviation Port Economic Zone, No. 1, Bio-Technology Street, Henan, 450008, China

    Lixia Ma, Hongyi Liu, Jian Wu & Ningli Wang

  3. Handan City Eye Hospital, Handan, 056001, China

    Aiguo Lv & Sujie Fan

  4. Department of Epidemiology and Biostatistics, West China, School of Public Health and West China Fourth Hospitalaq, Sichuan University, Chengdu, Sichuan, 610041, P. R. China

    Qing Pan

  5. State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, 510060, China

    Xu Jia

  6. The First Affiliated Hospital (College of Clinical Medical), Henan University of Science and Technology, Luoyang, 471000, China

    Lixia Ma

Authors
  1. Caixia Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Lixia Ma
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Aiguo Lv
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Hongyi Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Qing Pan
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Kai Cao
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Xu Jia
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Sujie Fan
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Jian Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Ningli Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

The design, data gathering, data analysis, and paper writing for this study were finished by CL, JW and ML. Participants in this study included CL, AL, and SF in the data collection and method design; HL in the research design and statistical analysis; QP and KC in the statistical analysis and manuscript grammar modification; and XJ and SF in the subsequent revision of the study. This study's corresponding author, NW, had complete responsibility for the work's completion and execution, had access to the data, and made the choice to publish. NW also had overall content responsibility as guarantor.

Corresponding author

Correspondence to Ningli Wang.

Ethics declarations

Ethics approval and consent to participate

In accordance with the Declaration of Helsinki, the Handan Eye Study was authorized by the Beijing Tongren Hospital Ethics Committee of Capital Medical University (TREC2006-22), and each participant signed an informed consent form.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, C., Ma, L., Lv, A. et al. Macular ganglion cell complex layer thickness measured with spectral-domain OCT in a large population-based cohort study. BMC Ophthalmol 25, 214 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12886-025-03989-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12886-025-03989-x

Keywords

BMC Ophthalmology

ISSN: 1471-2415

Contact us