To determine the distribution and normal values of macular thickness using Spectralis spectral-domain optical coherence tomography (OCT). This study is part of the third phase of the Shahroud Eye Cohort Study, with results presented cross-sectionally. The target population for this phase consisted of individuals aged 50 to 74 years. Samples were selected from Shahroud city using a multi-stage cluster sampling method. Following the presence of participants at the examination site, their uncorrected and best-corrected visual acuity was measured, and refractive assessments were conducted. Subsequently, an ophthalmologist performed a slit-lamp examination to evaluate the cornea, eyelids, and lens. Finally, after pupillary dilation, the retina was examined using a + 90 diopter lens. Retinal imaging was conducted using the Heidelberg Spectralis OCT, and biometry was performed with the Lenstar device. In this report, we analyzed 4,013 eyes from 2,205 individuals. The average central macular thickness was 264 ± 22 μm (95% CI 263 to 265) for all participants, 271 ± 22 μm (95% CI 269 to 272) for males, and 259 ± 20 μm (95% CI 258 to 260) for females. Within the inner macular region, the thickest part was found nasally, while the thinnest part was located temporally. In the outer region, the nasal region again showed the thickest part, with the temporal region being the thinnest. The average macular volume for the entire population was 8.52 ± 0.38 mm3 (95% CI 8.51 to 8.54). The 95th and 99th percentiles of central macular thickness were 301 and 318 μm, respectively. Central macular thickness was positively associated with male gender (β = 10.64; P < 0.001) and axial length (β = 2.39; P < 0.001), while it was negatively correlated with increased HbA1c levels (β = − 0.86; P < 0.001) and mean keratometry (β = − 6.87; P < 0.001). Additionally, individuals with a history of radiotherapy (β = − 1.99; P = 0.007) exhibited thinner macular thickness. Macular volume also showed an inverse relationship with increasing age (β = − 0.01; P = 0.002) and was lower in smokers (β = − 0.15; P = 0.013). This study highlights the importance of considering demographic factors, such as age, sex, and axial length, as well as underlying health conditions like diabetes and a history of radiotherapy, when interpreting OCT imaging results. The variations seen between different OCT devices, including differences in segmentation algorithms and measurement accuracy, underscore the necessity of developing normalized databases tailored to each specific device and population.
Hashemi et al. (Fri,) studied this question.