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Retinal
nerve fiber layer and ganglion cell-inner plexiform layer thickness in children
with obesity
Selim Demir1, Samet Özer2, Sait Alim1, Alper Güneş1, Hüseyin Ortak1, Resul Yılmaz2
1Department of Ophthalmology, Faculty of Medicine, Gaziosmanpasa University, Tokat 60030, Turkey
2Department of Pediatrics, Faculty of Medicine, Gaziosmanpasa University, Tokat 60030, Turkey
Correspondence to: Selim Demir. Department of Ophthalmology, Faculty of Medicine,
Gaziosmanpasa University, Tokat 60030, Turkey. dr.selimdemir@yahoo.com
Received: 2014-11-17
Accepted: 2016-01-07
Abstract
AIM:
To evaluate retinal nerve fiber layer (RNFL) thickness analysis of
peripapillary optic nerve head (PONH) and macula as well as ganglion cell-inner
plexiform layer (GCIPL) thickness in obese children.
METHODS: Eighty-five children
with obesity and 30 controls were included in the study. The thicknesses of the
PONH and macula of each subject’s right eye were measured by high-resolution
spectral-domain optic coherence tomography (OCT).
RESULTS: The RNFL thicknesses of central macular and PONH were similar between
the groups (all P>0.05). The GCIPL
thickness was also similar between the groups. However, the RNFL thickness of
temporal outer macula were 261.7±13.7 and 268.9±14.3 µm for the obesity and the
control group, respectively (P=0.034).
CONCLUSION: Obesity may cause a reduction in temporal outer
macular RNFL thickness.
KEYWORDS: ganglion
cell-inner plexiform layer;
retinal nerve fiber layer thickness; optical coherence tomography; obesity
DOI:10.18240/ijo.2016.03.19
Citation: Demir S, Özer S, Alim S, Güneş A, Ortak
H, Yılmaz R. Retinal nerve fiber layer and ganglion cell-inner plexiform layer
thickness in children with obesity. Int J Ophthalmol 2016;9(3):434-438
INTRODUCTION
Obesity,
one of today's leading health concerns in the community, is a low-grade chronic
inflammatory disease. The disease is closely associated with life-threatening
diseases such as hypertension, diabetes mellitus, stroke, metabolic syndrome, etc.
The most possible mechanism involved in the development of obesity-related
co-morbidities is the imbalance between the reactive oxygen radicals generation
and the antioxidant activity in cells, generally called “oxidative stress”[1]. Obesity-related cardiometabolic
disorders are known to be associated with visual impairment as well[2-3]. For instance,
increased body mass index (BMI)
was associated with early age-related macular degeneration in female
non-smokers[4]. Moreover,
it is well known that diabetes mellitus, closely related to obesity, is one of
the leading causes of blindness. Further, obesity may lead increased
intraocular pressure and glaucoma[3,5-6].
Retrobulbar adipose tissue volume may be an effect upon intraocular pressure in
obesity[7].
Outside
of concomitant diseases, obesity itself directly impairs the function of many
organ systems via obesity-related oxidative stress and lipotoxicity[8]. The disease is
associated with low grade chronic inflammation, a common feature of many complications
of obesity that appears to emanate in part from adipose tissue. In obese
rodents adipose tissue macrophage accumulation is a critical component in the
development of obesity-induced inflammation[9-10].
It was recently described that a novel macrophage cell death pathway that
occurs when toll-like receptor 4 is activated under lipotoxic conditions[11]. The mechanism of this
response involves the intersection between mediators which are modulating
toll-like receptor signaling pathways and impaired lysosome function and
integrity. High fat diet-induced obesity is accompanied by increased hepatic,
heart, and renal tissues oxidative stress, which is characterized by reduction
in the antioxidant enzymes activities and glutathione levels, that correlate
with the increase in some oxidant factors levels including malondialdehyde and
protein carbonyl in most tissues[12].
Optical
coherence tomography (OCT), which uses near infrared light to provide
cross-sectional images of the retinal architecture, enables physicians to
noninvasively and objectively quantify the measurement of retinal nerve fiber
layer (RNFL) and ganglion
cell layer (GCL) thickness[13]. Recent advances in OCT
technology have enabled an automatic segmentation between the RNFL and GCL in
the macula[14].
Retinal
ganglion cells (RGCs) are particularly vulnerable to metabolic and oxidative
damage in the eye[15]. To
the best of our knowledge, there are no studies in the literature evaluating
these macular thickness parameters as well as macular GCL in obese children by OCT. The goal of this study is
to evaluate RNFL and RGC thickness and reveal to their association with
obesity in pediatric population.
SUBJECTS
AND METHODS
Subjects The pediatric subjects were recruited at the
Gaziosmanpaşa University Hospital (Tokat, Turkey) for this observational
cross-sectional study. This study was approved by the Ethics committee of the Gaziosmanpaşa University, which adhered to the
tenets of the Declaration of Helsinki. Participation in the study was voluntary
and written informed consent for participation in the study was obtained from
the parents or guardians as well as the participants.
Consecutive
eighty-five obese
children who attending pediatric obesity clinic in our hospital age 7 to 15y were included in this study and 30 age-sex-matched
non-obese children who attending eye clinic of our hospital with non-specific
ocular complaints, such as conjunctivitis, burning, itching, or refractive
errors were selected randomly as a control group.
Determination of the Groups
and Calculation
of the Body Mass Index Height was measured without socks and shoes using a
calibrated vertical portable stadiometer, to the nearest millimeter. Weight was
measured with light clothing using a digital electronic weighing scale, to the
nearest decimal fraction of a kilogram. BMI was calculated as weight in
kilograms divided by the square of height in meters. Then, BMI for age
categories and corresponding percentiles are: 1) healthy weight: the 5th percentile to less than 85th
percentile included in the control group; 2) equal to or greater than 95th
percentile included in the obesity group.
The Eye
Examination
and the Procedure
of Optical Coherence
Tomography
Measurements Each participant underwent an ophthalmologic
evaluation that included best-corrected visual acuity measurement, slit-lamp
evaluation, indirect ophthalmoscopy, intraocular pressure measurement, and
spectral-domain optic coherence tomography (SD-OCT) scanning.
OCT
scanning was performed only for right eyes of each participant using the Cirrus
high-resolution SD-OCT system (Carl Zeiss Meditec, Dublin, CA, USA). Scans were performed by one trained technician. An
internal fixation target was used to improve reproducibility, and a patch was
placed over the left eye. Scans were performed without flash photography to
optimize patient comfort. Pupils were dilated with 1% tropicamide before at
least 30min to measurements. Good quality scans were defined according to
specifications in the user manual; criteria included signal strength ≥7 (maximum 10), centering of the scan, and uniform brightness.
Measurements were repeated until obtaining sufficient quality.
Third
OCT scanning protocols were performed on the right eye. First, the optic nerve
head (ONH) cube protocol computes the RNFL thickness along 2.4-mm diameter
circles around the optic disc. In the ONH cube measurements, the following
software-provided parameters were evaluated: average RNFL thickness in the 4
quadrants, and global average RNFL thickness and optic disc diameter size.
Second, the macular cube 512×128 scanning protocol was used to image a 6×6×2-mm3 cube of macular tissue centered on the
fovea. The macular cube protocol consists of 128 horizontally oriented B-scans,
each 6 mm in length and composed of 512 equally spaced transverse sampled
locations. All 128 OCT B-scans are acquired in a continuous, automated sequence
and cover a 6×6-mm2 area. This scan protocol provides a pixel by pixel significance
map and nine parameters from a circular grid based on the Early Treatment
Diabetic Retinopathy Study. The map is composed of sectoral thickness
measurements in three concentric circles with diameters of 1, 3 and 6 mm. Each
ring is divided into superior, nasal, inferior and temporal quadrants. The
retinal thickness of each of the nine subfields of the Early Treatment Diabetic
Retinopathy Study like map was
recorded. Third, ganglion cell-inner plexiform layer (GCIPL) scanning protocol[16]. Briefly, the ganglion
cell analysis algorithm identifies the outer boundary of the RNFL and the outer
boundary of the inner plexiform layer, which contains the retinal ganglion cell
dendrites. In the image data, the boundary between these two layers is anatomically
indistinct so that they are difficult to distinguish from each other, but the
combined thickness is considered to be indicative of the health of RGCs. The average and sectoral (superotemporal, superior,
superonasal, inferonasal, inferior, inferotemporal) thicknesses of the GCIPL
are measured in an elliptical annulus (dimensions: vertical inner and outer
radius of 0.5 mm and 2.0 mm, horizontal inner and outer radius of 0.6 and 2.4
mm, respectively) around the fovea[16].
RESULTS
A
total of 115 subjects (115 eyes) were examined with the SD-OCT: 85 eyes with
obese children and 30 eyes with non-obese children. The mean ages were 10.8±2.9 (6-15)
and 11.1±2.8 (6-16) years old, for the obesity and the normal group,
respectively. The male/female ratio was 42/43 in the obese group and 16/14 in
the normal group (P>0.05 for age
and gender). The spherical refraction and intraocular pressure values of
patients were similar in both groups (P>0.05).
The demographic characteristics of patients are presented in Table 1.
Table
1 Demographic
characteristics of children with obesity and control subjects 
|
Obesity (n=85) |
Control (n=30) |
P |
|
|
Age (a) |
10.8±2.9 |
11.1±2.8 |
0.623 |
|
Gender |
0.713 |
||
|
M |
42 |
16 |
|
|
F |
43 |
14 |
|
|
Spherical refraction |
-0.40±0.65 |
-0.25±0.57 |
0.194 |
|
Intraocular pressurea (mm Hg) |
14.22±3.27 |
13.15±2.5 |
0.421 |
aGoldmann applanation tonometer value of intraocular pressure
adjusted for central corneal thickness.
Table
2 shows macular thickness measurements estimates in right eyes of the two
groups. Average RNFL thickness of temporal outer macula (TOM) were 261.7±13.7 µm and 268.9±14.3 µm for the obesity
and the normal group, respectively (P=0.034).
There were no significant differences in the other macular thickness
measurements of the subfield areas of the Early Treatment Diabetic Retinopathy
Study-like map between
the two groups (P>0.05).
Table 2 Average RNFL thicknesses measurements of the subfield areas of the Early
Treatment Diabetic Retinopathy Study-like map of macula in obese and non-obese children
Parameters (µm)
|
Obesity (n=85) |
Control (n=30) |
P |
|
CSF |
245.3±20.6 |
239.3±18.8 |
0.197 |
|
SIM |
319.9±16.7 |
318.2±13.7 |
0.625 |
|
NIM |
320.6±17.1 |
319.2±16.1 |
0.719 |
|
IIM |
317.3±15.7 |
318.7±13.2 |
0.669 |
|
TIM |
307.1±13.9 |
311.7±26.4 |
0.266 |
|
SOM |
280.5±14.4 |
286.9±18.6 |
0.087 |
|
NOM |
298.1±17.9 |
300.3±17.1 |
0.601 |
|
IOM |
272.1±19.4 |
277.1±11.1 |
0.121 |
|
TOM |
261.7±13.7 |
268.9±14.3 |
0.034 |
CSF: Central subfield; SIM:
Superior
inner macula; NIM: Nasal inner macula; IIM:
Inferior
inner macula; TIM: Temporal inner macula; SOM: Superior outer macula; NOM:
Nasal outer
macula; IOM: Inferior outer macula; TOM: Temporal outer macula.
The
average GCIPL thickness were 84.9±5.2 µm and 84.9±5.7 µm in the groups of
obesity and control, retrospectively (P=0.976).
There was also no difference in other subfield GCIPL thickness between the
studied groups (P> 0.05). The
GCIPL subfield parameters are presented in Table 3.
Table 3 Average ganglion cell-inner plexiform layer thickness
in obese and non-obese children
|
Parameters
(µm) |
Obesity (n=85) |
Control (n=30) |
P |
|
Average thickness |
84.9±5.2 |
84.9±5.7 |
0.976 |
|
Nasal superior |
85.6±6.1 |
85.5±8.3 |
0.937 |
|
Nasal inferior |
85.9±5.9 |
85.6±7.3 |
0.887 |
|
Inferior |
84.9±5.8 |
84.7±6.4 |
0.847 |
|
Temporal inferior |
84.3±5.7 |
84.9±6.1 |
0.642 |
|
Temporal superior |
84.9±5.9 |
84.8±6.6 |
0.921 |
The
average optic disc size were 1.98±0.35 µm and 1.97±0.37 µm and the average
peripapillary RNFL thickness were 97.45±10.28 µm and 97.49±9.06 µm in the
groups of obesity and control, respectively (P>0.05 in both parameters). Table 4 shows the other subfield
peripapillary RNFL thickness between the studied groups and there was also no
significant difference (P> 0.05).
Table 4 Mean peripapillary retinal nerve fiber layer thickness in eyes of
children with obesity and control subjects
|
Parameters
(µm) |
Obesity (n=85) |
Control (n=30) |
P |
|
Optic disc size |
1.98±0.35 |
1.97±0.37 |
0.907 |
|
Superior quadrant |
123.64±15.07 |
125.90±13.39 |
0.540 |
|
Nasal quadrant |
70.51±10.26 |
73.00±12.95 |
0.356 |
|
Inferior quadrant |
127.53±18.84 |
124.05±17.75 |
0.465 |
|
Temporal quadrant |
68.13±10.84 |
67.00±8.57 |
0.664 |
|
Average thickness |
97.45±10.28 |
97.49±9.06 |
0.504 |
DISCUSSION
Obesity,
low-grade chronic inflammatory disease, affects almost all organs. It has been
hypothesized that the state of chronic low-grade inflammation associated with
excess adipose tissue may explain the development of the obesity-related
pathologies, such as type 2 diabetes mellitus and cardiovascular disease[17-18]. Suppressors of
cytokine signaling caused by chronic inflammation or cellular stress can induce
insulin resistance and inhibit neurotrophic factors, such as ciliary
neurotrophic factor, leukemia inhibitory factor, and insulin, that are
essential for retinal cell survival[19].
Moreover, it is argued that obesity in children may cause increased intraocular
pressure, which may affect the RNFL thickness[6]. It is shown in the a recent study that elevated
intraocular pressure may be caused by changes in ocular blood flow, affected by
the physical pressure exerted by higher retrobulbar adiposity, and/or by
internal vascular changes secondary to complications of obesity[7]. Therefore, the present
study was designed to examine for the first time in the literature whether RNFL
thicknesses of macular and peripapillary optic nerve head (PONH)
as well as GCIPL thickness in children with obesity differed from those of age-
and sex-matched healthy controls. We found that, in this study, RNFL thickness
of TOM
was decreased in children
with obesity. However, the GCIPL thickness of children with obesity was similar
with control subjects.
The
obese children might have elevated levels of oxidative stress products, which
may contribute to long-term complications[20].
It is postulated that obesity related low-grade inflammation can cause
neurological diseases such as Parkinson’s and Alzheimer’s [21-22]. Neuronal membranes are rich in polyunsaturated
fatty acids (PUFAs), particularly arachidonic acid, decosahexaenoic acid, and
eicosapentaenoic acid[23].
The neural cells are more susceptible to oxidative damage due to their
possession of unsaturated double bounds[24].
In spontaneous obese rat model, Reddy et al[25] have documented that altered ubiquitin-proteasome
system one of the underlying mechanisms for the neuronal cell death. This
system is essential in regulating a host of cell signaling pathways involved in
proliferation, adaptation to stress, regulation of cell size, and cell death[26]. These cellular changes
induced by obesity are also observed in retinal cells. Mancini et al[27] showed in diabetic
neonatal rats fed on a high-fat-diet that there is a significantly higher
frequency of vessel abnormalities in the form of acellular capillaries and loss
of pericytes as well as ganglion cells. Besides its well-known effects on the
retinal cells through its oxidative stress products, obesity also has an effect
on visual system via its effect on central nervous system[28].
Increased
intraocular pressure can lead to visual loss via death of ganglion
cells. There are many studies claimed that obesity can cause IOP elevation and
glaucoma[3,6-7,29]. In obese
people, elevated intraocular pressure may be caused by changes in ocular blood
flow, affected by the physical pressure exerted by higher retrobulbar
adiposity, and/or by internal vascular changes secondary to complications of
obesity[7]. In a cross-sectional
and longitudinal study, it is suggested that blood pressure and BMI are
positively associated with IOP in middle-aged and older Japanese[5]. In a study of Akinci
et al[6] performed
on obese children, it is documented that obesity is also an independent risk
factor for increased IOP besides to its indirect effect on IOP via blood
pressure change. The choroidal blood flow is important for the health of the
retinal cell besides the increase in intraocular pressure. Decreased ocular
blood flow and choroidal perfusion may also be an effect on retinal cells in
the obese individuals[30].
Although many of studies have shown that obesity is associated with increased
IOP, some studies have failed to show this relationship[31-32].
The
ability to maintain adequate nutrient supply to retina such as ganglion cells,
despite variations in metabolic demand, the driving pressure for blood flow, or
the oxygen or carbon dioxide content of blood, is critical to maintenance of
normal function[33].
Therefore, conditions such as obesity which lead to changes in ocular blood
flow may have a detrimental effect on retinal cells. Li et al[34] found that higher BMI
was associated with narrower retinal arteriolar, wider venular caliber, and
increased retinal venular tortuosity. In an experimental study[35], retinal blood flow
reduction was noticed in obese mice. Moreover, retinal thickness of the nerve
fiber layer to inner plexiform layer was also significantly reduced in obese
mice compared to that in wild-type mice. However, no obvious differences in
capillary vessel densities of the intermediate and deep capillary layers were
detected between normal and obese mice in that study.
Measurement
of the RNFL thickness by OCT, provides additional useful information in the
diagnosis and management of retinal pathologies including some of inflammatory
illnesses in children[36].
However, measurement of the RNFL thickness of retina by OCT is affected by both
refractive status and age of children[37-38]. We have eliminated these potential confusing factors
by using age and sex matched controls and it was demonstrated that the 2 groups
had statistically similar refractive errors.
The
study was limited by the small sample size and the lack of statistical
significance on essentially most of the investigated comparisons except for the
TOM thickness. Further research, including large-case
series, is needed to clarify whether obesity have an effect on GCL thickness.
In
conclusion, it was found in this study that RNFL thickness of TOM was decreased in children with obesity. However, RNFL
thickness of outer macular subfield areas did not differ. The RNFL thicknesses
of PONH and GCIPL were not significantly different in children with obesity
from those of controls.
ACKNOWLEDGEMENTS
This work was partially presented
at the 7th Mediteretina Club International Meeting, April 17-20,
2014, Istanbul.
Conflicts
of Interest: Demir
S, None; Özer S, None; Alim S,
None; Güneş A, None; Ortak H, None; Yılmaz R, None.
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