Method Article

Determining Gender-Based Differences in Retinal and Choroidal Thickness in Underweight Individuals via Swept-Source Optical Coherence Tomography

DOI:

10.3791/65918

⸱

December 1st, 2023

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1Department of Cardiology, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 2Department of Ophthalmology, Huashan Hospital, Fudan University, 3Eye Institute of Xiamen University, Xiamen University School of Medicine

In This Article

Summary

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Here, swept-source optical coherence tomography (SS-OCT) is used to compare retinal and choroidal thickness in adults with and without malnutrition, contributing to a better understanding of the pathogenesis of ocular diseases in malnourished individuals.

Abstract

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Despite improvements in reducing hunger in recent years, undernutrition remains a global public health problem. This study utilizes the swept-source optical coherence tomography (SS-OCT) technique to assess changes in retinal and choroidal thickness in underweight subjects. Ophthalmic examinations were conducted on all adults participating in this cross-sectional research. Depending on their body mass index (BMI), the participants were divided into two groups: the underweight group and the normal group. The study included the right eyes of the underweight adults and an equal number of age- and gender-matched normal-weight subjects. The retinal thickness showed no significant difference between the underweight and normal groups (P > 0.05 for all). In males, the retina of the center and inner ring in the underweight group was significantly thinner than that in the normal group, while no such results were found in females. The choroid in the underweight group was significantly thinner compared to that in the normal group (all P < 0.05). Being underweight may affect choroidal thickness in both males and females. In comparison with underweight females, underweight males may experience more retinal damage. These findings contribute to a better understanding of the pathogenesis underlying specific ocular diseases in malnourished individuals.

Introduction

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Despite the Health Organization's successful efforts to combat hunger in recent years, undernutrition remains a significant global public health concern. Globally, it was estimated that 9.8% of the population was undernourished in 20221. The incidence of undernutrition varies across regions, with higher prevalence among individuals with lower socioeconomic status2,3,4. Additionally, some individuals, especially young people, lose weight excessively in pursuit of a perfect body shape. Malnutrition, in all its various forms, affects every country in the world5.

Being underweight is associated with negative clinical outcomes, including infections, immune dysfunction, delayed wound healing, and growth and developmental retardation6,7,8,9. A malnourished state is one of the leading risk factors for premature death and the loss of disability-adjusted life years10,11,12. Studies have shown that the lowest body mass index (BMI) is associated with the poorest binocular ability13. Furthermore, research has demonstrated that undernutrition is linked to various ocular issues, such as macular degeneration, decreased dark adaptation, optic atrophy, keratitis, dry eye, and retinoblastoma14,15,16,17,18.

The retina, with its multiple layers and cell types, is a complex tissue, while the choroid is a highly vascularized structure that provides nutrients to the outer layer of the retina and removes metabolic waste19. The retina and choroid, as critical structures of the eyeball, can be affected by systemic pathologies or physiological conditions20,21. They have been found to play a significant role in the pathogenesis of specific ocular diseases, including macular degeneration, polypoidal choroidal vasculopathy, uveitis, glaucoma, and myopia-related chorioretinal atrophy22,23,24,25,26. Therefore, ocular function depends on both anatomically and functionally normal retinas and choroids.

While undernutrition has various effects on the eye, there is limited information available on the relationships between malnutrition and retinal or choroidal thickness in different genders. This study aims to assess potential changes in retinal or choroidal thickness in malnourished adults using the swept-source optical coherence tomography (SS-OCT) technique, which represents a significant advancement in retinal and choroidal imaging27. This technology is particularly effective in accurately identifying the choroidal scleral interface (CSI) in eyes with thicker choroids, thanks to its high penetration capabilities through the retinal pigment epithelium (RPE).

In this study, participants were categorized into two groups based on their BMI: the underweight group (BMI < 18.50 kg/m2) and the normal group (18.50 ≤ BMI < 25.00 kg/m2). The study included 996 right eyes of 996 underweight adults and an equal number of age- and gender-matched normal-weight subjects. The average BMI was 17.48 ± 0.75 kg/m2 in the underweight group and 21.30 ± 1.75 kg/m2 in the normal group.

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Protocol

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This research was conducted at Huashan Hospital of Fudan University from January 2020 to October 2020. The study was approved by the Institutional Review Board of Huashan Hospital (No. KY2016-274), and all participating adults provided written informed consent.

1. Selection of participants

  1. Record all participants' demographic characteristics, such as age, gender, and a history of systemic diseases. Consider the following as the exclusion criteria: (1) age < 18 years or > 70 years old and (2) a history of systemic diseases related to retinal or choroidal thickness, including diabetes mellitus, hypertension, and thyroid disease.
    ​NOTE: The elderly population, particularly those aged over 70 years, frequently experienced severe cataracts that could affect the quality of OCT images.
  2. Let all adult participants involved in the research undergo ophthalmic examinations. Consider the following as the exclusion criteria: (1) intraocular pressure (IOP) >21 mmHg; (2) best-corrected visual acuity (BCVA) worse than 0.1 LogMAR; (3) spherical equivalent more than ± 6 diopters; (4) a history of ocular diseases, including retinal disease, choroidal disease, and glaucoma; and (5) any previous ocular surgery.

2. Body mass index calculation

  1. Measure the participants' height and weight using a height-weight measuring instrument (see Table of Materials).
  2. Calculate the BMI using the formula: weight / (height x height) (kg/m2).
  3. Classify the subjects into two groups based on the World Health Organization International Classification28: the underweight group (BMI <18.50 kg/m2) and the normal group (18.50 ≤ BMI < 25.00 kg/m2).

3. Swept-source optical coherence tomography scan

  1. Turn on the Power switch on the SS-OCT device (see Table of Materials) with a 1050 nm wavelength.
    NOTE: This SS-OCT system, capable of 1,00,000 scans/s, has recently undergone significant improvements, enhancing the visualization of the retina and choroid.
  2. Click on the Radial Dia.6.0mm Macula Overlap 4 button to access the scanning interface.
  3. Capture high-quality images for each eye of the participants during the scanning process.
    NOTE: The OCT scans were performed by experienced ophthalmologists between 8 and 10 a.m. daily to minimize diurnal variations29.
  4. Generate a thickness map following the standard early treatment diabetic retinopathy study (ETDRS) grid.
  5. Define retinal thickness (Figure 1A,B) and choroidal thickness (Figure 2A,B) as previously described27,30.
    NOTE: To ensure accurate measurements, it was imperative to manually review the segmented lines within the OCT scans27,30.
  6. Exclude poor OCT images resulting from media opacity or unstable fixation.

4. Statistical analysis

  1. Launch the SPSS software (see Table of Materials). The analysis exclusively considered the right eye of the participants31.
    NOTE: Present continuous data as mean ± standard deviation (SD) and categorical data as frequency (percentage).
  2. Perform group comparison using a t-test for continuous variables and a Chi-square test for categorical variables. Conduct correlation analyses employing Pearson's correlation.
    NOTE: A significance level of P < 0.05 (two-tailed) was used to determine statistical significance.

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Results

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A total of 996 right eyes from 996 underweight adults were evaluated in this study, with 1:1 age- and gender-matched normal-weight subjects. The demographic characteristics of both groups are summarized in Table 1. The underweight group had an average BMI of 17.48 ± 0.75 kg/m2 (range: 14.60-18.40 kg/m2), while the normal-weight group had an average BMI of 21.30 ± 1.75 kg/m2 (range: 18.50-24.90 kg/m2).

Table 2 present...

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Discussion

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In this study, SS-OCT was employed to compare retinal and choroidal thickness in adults with and without malnutrition. The outcomes of the study showed that, among males, individuals in the underweight group had significantly thinner retinas in the central and inner ring regions compared to those in the normal group. However, no such differences were observed among females. Additionally, the choroid was found to be significantly thinner in the underweight group compared to the normal group in both males and females. Thes...

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Disclosures

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None of the authors has a financial or proprietary interest in any material or method mentioned.

Acknowledgements

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This study was funded by grants from the National Natural Science Foundation of China (No. 81900879) and the Science and Technology Commission of Shanghai Municipality (No. 20Y11910800).

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Height and weight meterDKi, Beijing, ChinaHC01000209
Ophthalmoscope66 Vision-Tech, Suzhou, ChinaV259204
Slit-lamp microscopeTopcon, Tokyo, Japan6822
SPSS softwareIBM, Chicago, USA ECS000143
Swept-source optical coherence tomographyTopcon, Tokyo, Japan185261
Visual chartYuejin, Shanghai, ChinaH24104

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Retinal ThicknessChoroidal ThicknessUnderweight IndividualsSwept Source OCTGender DifferencesOptical Coherence TomographyBody Mass IndexOphthalmic ExaminationOcular DiseasesCross Sectional Study
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