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Article

Preliminary Results of Noninvasive Ocular Rigidity in Diabetic Retinopathy Using Optical Coherence Tomography

by
Yanhui Ma
1,*,
Matthew P. Ohr
1 and
Cynthia J. Roberts
1,2
1
Department of Ophthalmology and Visual Sciences, The Ohio State University, 915 Olentangy River Rd, Suite 5000, Columbus, OH 43212, USA
2
Department of Biomedical Engineering, The Ohio State University, Columbus, OH 43210, USA
*
Author to whom correspondence should be addressed.
Photonics 2022, 9(9), 598; https://doi.org/10.3390/photonics9090598
Submission received: 29 May 2022 / Revised: 4 August 2022 / Accepted: 19 August 2022 / Published: 24 August 2022
(This article belongs to the Special Issue Optical Elastography: Current Status and Future Applications)
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Abstract

:
The pathologic mechanism of diabetic retinopathy is directly related to the underlying hyperglycemia associated with diabetes. Hyperglycemia causes non-enzymatic cross-linking of collagen fibrils which contributes mechanistically to tissue stiffening. However, investigations on diabetic retinopathy-associated alteration in ocular biomechanics remain scarce, especially in living human eyes. Ocular rigidity is classically defined as a measure of the change in intraocular pressure produced by a change in ocular volume. We recently implemented an approach for the direct in-vivo non-invasive estimate of ocular rigidity using optical coherence tomography, allowing for the evaluation of the biomechanical behavior in eyes with diabetic retinopathy. Our preliminary results showed that diabetic retinopathy exhibited higher ocular rigidity and higher scleral stiffness compared to normal controls, which may possibly be attributed to hyperglycemia-induced collagen cross-linking in the ocular tissues. Knowledge of diabetic retinopathy-associated biomechanical changes will equip us with new quantitative tools to identify diagnostic markers in diabetic retinopathy.

1. Introduction

Diabetic retinopathy (DR) is the leading cause of vision impairment and blindness among working-age adults [1,2] and represents a significant health and financial burden on society and economy. DR is a progressive complication of diabetes caused by prolonged hyperglycemia over time that damages the blood vessels within the retina. Hyperglycemia causes collagen cross-linking through a series of biochemical reactions known as the Maillard reaction [3,4]. The Maillard reaction was first described in 1912 in the context of food science about the non-enzymatic browning due to the chemical reaction between reducing sugars and amino acids. Later, it was reported that the Maillard reaction has important pathogenic effects on human health and disease with the generation and accumulation of advanced glycation end products (AGEs) that modify the extracellular and intercellular structure and function in many cell types [5,6]. Further observations were made on the relationship between diabetes and keratoconus by investigating whether hyperglycemia-induced cross-linking in the cornea may compensate for the weakening process during the development of keratoconus [7,8,9]. In the presence of hyperglycemia, ocular tissues may become stiffer in response to the increased nonenzymatic cross-linking (or AGEs) of collagen fibrils in connective tissues such as the cornea and sclera. There is a critical gap in knowledge on the relationship between ocular biomechanics and diabetes especially by using direct measures to reveal DR-associated biomechanical changes in a clinical setting.
Ocular rigidity is a biomechanical parameter that describes the combined behaviors of the entire ocular shell by measuring the change in intraocular pressure (IOP) in response to the change in intraocular volume [10]. The coefficient of ocular rigidity by Friedenwald is based on a logarithmic pressure-volume relation derived from experimental data on enucleated eyes [10]. While studies of ocular rigidity were mostly focused on ex vivo experiments, very limited methods are available for clinical measurement mainly due to the challenges of estimating the ocular volume change. Synchronous pulsations of the eye fundus can be used to measure the cornea–retina distance changes which is an indirect way of measuring the ocular volume change [11]. Differential Schiotz tonometry involves indentation depth changes using two different weights placed on the surface of the cornea to generate changes in ocular volume [12]. Those indirect methods, however, may lead to errors in estimating ocular rigidity, due to the preexisting volume of choroidal circulation and the preexisting IOP level [13,14]. By contrast, manometric methods can be used to directly measure the IOP change by injecting a known volume of saline solution into the anterior chamber. However, despite accuracy, due to its invasive nature, manometric measurement of ocular rigidity has been limited to patients undergoing cataract surgery in the setting of the operating room [15]. There is a great clinical need for noninvasive direct measurement of ocular rigidity. With recent advances in optical coherence tomography (OCT) and enhanced depth imaging, we have implemented an open-source algorithm for automatically segmenting and quantifying the choroidal layer based on graph search [13,16]. Dynamic OCT videos were utilized to quantify the pulsatile choroidal volume change produced by blood vessel flux within each cardiac cycle, providing a direct noninvasive estimate of ocular rigidity. This approach has been validated with excellent repeatability and has shown consistency with the invasive manometric method [13,17].
Using this OCT-based non-invasive approach for ocular rigidity estimation, we aimed to (1) evaluate the ocular rigidity in non-proliferative DR patients with mild or moderate grade in the absence of macular edema, and (2) investigate how ocular rigidity correlates with the corneoscleral response parameters. It is our hypothesis that DR has a stiffer biomechanical response compared to normal controls. This knowledge will provide insights into the role of ocular biomechanics in diabetic retinopathy.

2. Methods

2.1. Subject Participants and Ophthalmic Examination

Enrollments of patients with DR were limited to those with non-proliferative diabetic retinopathy of mild to moderate grade in the absence of macular edema based on the modified Airlie House/Early Treatment Diabetic Retinopathy Study (ETDRS). Enrollment for the normal controls was based on the self-reported absence of ocular history and diabetes mellitus. All participants were recruited from the Department of Ophthalmology and Visual Sciences at The Ohio State University, as well as from the community to establish a normal control cohort. All participants have provided consent in adherence to the tenets of the Declaration of Helsinki. This study was approved by the Institutional Review Board of The Ohio State University. Inclusion criteria for both DR patients and normal controls were age 18 years or greater; absence of prior intraocular surgery (except for cataract), corneal pathology and retinal pathology; ability to comprehend, agree, and sign the subject informed consent form; and willingness to comply with the prescribed schedule at the time of enrollment. Exclusion criteria for participants included any history of ocular injury and ocular diseases, such as age-related macular degeneration, glaucoma, ocular hypertension, keratoconus, or proliferative diabetic retinopathy. Participants with a diagnosis of retinal detachment, retinal tear, retinal degeneration, or retinal hole were excluded. Participants were excluded if they were pregnant, less than 12 weeks postpartum, or less than 12 weeks after breastfeeding.
All participants underwent a complete ophthalmic examination during the same visit using multiple diagnostic devices including the Corvis ST (OCULUS, Wetzlar, Germany), Pentacam (OCULUS, Wetzlar, Germany), Pascal dynamic contour tonometer (DCT) (Ziemer, Port, Switzerland), Goldmann applanation tonometry (Haag-Streit, Bern, Switzerland), and Spectralis OCT (Heidelberg Engineering, Heidelberg, Germany). Specifically, Corvis ST was used to measure central corneal thickness, and to characterize the corneoscleral biomechanical properties, such as the stiffness parameter at the first applanation (SP-A1), and the stiffness parameter at the highest concavity (SP-HC) based on the dynamic reaction of the cornea to an air impulse [18]. It has been suggested that SP-A1 and SP-HC are indicative of corneal stiffness and scleral stiffness, respectively [19]. Pentacam was used to measure the radius of corneal curvature, and anterior chamber volume. With the radius of corneal curvature and refractive error, the axial length was calculated using the Gullstrand-Emsley model [13,20]. Pascal DCT was used to measure the IOP and ocular pulse amplitude (OPA). Pascal DCT has been reported to be relatively independent of corneal biomechanics, and therefore provides more accurate estimates of IOP than Goldmann applanation tonometry [21,22,23,24]. Lastly, Spectralis OCT was used to scan the posterior segment of the eye centered at the optic nerve head. Ocular rigidity describes the change in IOP in response to a change in ocular volume, accounting for the aggregate mechanical response of the eye. The ocular volume fluctuates due to the pulsatile vascular filling that occurs with each heartbeat, and for a given volume change, stiffer eyes will have a correspondingly larger increase in IOP, and vice versa for eyes that are less stiff [10]. Ocular rigidity was estimated using OCT videos along with the pulsatile IOP change measured from Pascal DCT, as described in the next section. In addition, blood pressure and heart rate were recorded using an automatic blood pressure monitor.

2.2. Non-Invasive Ocular Rigidity Using Optical Coherence Tomography

Since 85% of total ocular blood flow passes through the choroid [25], pulsatile fluctuations in the ocular volume can be estimated by the changes in choroidal volume during each cardiac cycle. Per the definition of ocular rigidity (change in pressure in response to change in volume), the calculation in ocular rigidity comes down to characterizing the pulsatile change in choroidal volume while the pulsatile change in IOP was easily measured by DCT (i.e., OPA). High-speed OCT with dense temporal sampling enables us to capture the dynamic response and detect the change in the retina and choroid. Images of the posterior eye were acquired using the Spectralis OCT device (with spectral domain OCT system) which uses the beam of a super luminescence diode with a center wavelength of 870 nm to produce a cross-sectional B-scan image. The acquisition was set to high-speed mode with a scan angle of 15°. Spectralis OCT has a scan rate of 40,000 A-lines per second and an axial resolution of 3.9 µm per pixel and a lateral resolution of 11.4 µm per pixel. Each OCT B-scan was composed of 496 × 384 pixels (axial × lateral), or 1.9 mm × 4.4 mm in size. In addition, active eye-tracking was used during the acquisition of OCT video to correct eye motion by initiating the reacquisition of OCT images at the same retinal location. Each scan automatically generates a quality score of 0–40 dB, and only scans with a score above 20 dB were kept in the time series for image processing. Figure 1 shows the en face view and sequential OCT B-scans of the posterior eye in a normal subject and a DR subject. We have implemented an automated open-source algorithm [16] for the segmentation of the choroidal–scleral interface and the retinal-choroidal interface in sequential OCT images with 599 B-scan frames. This allows for the assessment of pulsatile choroidal thickness change deriving the pulsatile change in choroidal volume [13,26]. A detailed description of the techniques for automatic choroid segmentation using OCT can be found in Mazzaferri et al. [16]. Briefly, although structurally the Bruch’s membrane is the innermost layer of the choroid, due to the difficulties of distinguishing Bruch’s membrane from the retinal pigment epithelium (RPE) in OCT images, the posterior RPE was segmented out as the retinal-choroidal interface. RPE in OCT images exhibits high-intensity contrast. After finding the two highest local maxima of intensity gradient in the axial direction, the posterior one was assigned to the interface of RPE on each A-scan. Then all A-scans were shifted independently to have a flattened RPE (see Figure 2) rendered on the B-scan. For the segmentation of the choroidal-sclera interface, the graph node search was based on the second derivative of the intensity along the A-scan. The intensity transition from dark to bright marking the location passing from the choroidal vessel to the sclera can be indicated by the sign change of the second derivative. The average distance between the retinal-choroidal interface and the choroidal–scleral interface was calculated as the choroidal thickness for a certain frame as shown in Figure 2. Compiling the choroidal thickness of all frames presents fluctuation of choroidal thickness over time. Because the built-in eye-tracking feature introduces pauses into the acquisition when the scanning beam could not be held in place due to eye movement, the time interval between frames is not usually uniform. To process the waveform data in choroidal thickness, outliers were first removed before the waveform was resampled and downsampled by incorporating an anti-aliasing filter and a fixed rate of 50 frames per second. With equal-spaced data, a band-pass filter was applied to only pass frequencies within the range of 0.5 to 3 times the heart rate. The inverse Fourier transform was then used to retrieve the filtered signal, from which the average peak-to-valley distance was calculated as the pulsatile change in choroidal thickness, Δ t . The pulsatile change in choroidal volume was simplified as Δ V = 4 π R 2 Δ t , where R was approximated by half of the axial length [13]. In short, the estimation of pulsatile fluctuations in the ocular volume requires the dynamic OCT images for choroidal thickness extraction, the heart rate for Δ t filtering, and axial length to convert thickness change into volume change with simplification.
The ocular rigidity based on Friedenwald’s empirical equation reveals the pressure-volume relationship in the eye that considers the fluctuation, as specified by ln ( I O P + O P A ) − ln ( I O P ) = k Δ V , where k denotes the ocular rigidity [10]. In addition to this dynamic pressure-volume relationship, the static pressure-volume ratio calculated simply as IOP divided by anterior chamber volume was also examined in this study.

2.3. Statistical Analysis

Data were presented as mean ± standard deviation. Differences in the clinical characteristics and biomechanical parameters between DR and normal cohorts were evaluated by a two-sample t-test if the normal distribution could be assumed or by the nonparametric Mann–Whitney U-test if normality was not valid. Normality was checked using the Shapiro-Wilk test. Clinical characteristics included age, sex, IOP, axial length, central corneal thickness, radius of corneal curvature, anterior chamber volume, and blood pressures. Biomechanical parameters included parameters related to ocular rigidity, SP-A1, SP-HC, and static pressure-volume ratio. The correlations of ocular rigidity with other biomechanical parameters of the eye were evaluated using Pearson correlation with groups of healthy and DR subjects combined (n = 30). Statistical significance threshold was p < 0.05. All data analysis was conducted using SAS software (V9.4; SAS Institute Inc., Cary, NC, USA).

3. Results

Twenty-two normal controls (age: 20–64 years) and 8 DR subjects (age: 24–63 years) with processable OCT videos and air puff tonometer measurements were included. Four DR patients suffered from diabetes type I and 4 from diabetes type II. Between DR patients and normal controls, there was no significant difference in age (p = 0.59). Only one eye (right eye) per subject was analyzed. Clinical characteristics data are presented in Table 1, in which there was no significant difference between DR patients and normal controls. There was no significant correlation of ocular rigidity with age in normal controls (p = 0.89) nor in the combined groups with both normal and DR subjects (p = 0.69).
Pulsatile change in choroidal thickness, as extracted from OCT video with automated segmentation of choroidal–scleral interface and retinal-choroidal interface, was significantly smaller in DR patients compared to normal controls (4.6 ± 1.2 µm vs. 6.5 ± 1.9 µm; p = 0.023). Axial length was similar between those two groups (p = 0.20, Table 1). As described in the Methods, the pulsatile change in choroidal volume was calculated based on the axial length and choroidal thickness change. The mean pulsatile change in choroidal volume in the DR group was 8.6 ± 2.3 µL, lower than that in normal controls (12.6 ± 3.8 µL; p = 0.009). OPA, as the pulsatile change in IOP, was not significantly different between DR patients and normal controls (p = 0.18). The mean ocular rigidity in the 8 DR patients was 0.016 µL−1 (95% confidence interval, 0.011 to 0.022 µL−1), and the mean ocular rigidity in the 22 normal controls was 0.011 µL−1 (95% confidence interval, 0.009 to 0.013 µL−1), as shown in Figure 3. Ocular rigidity demonstrated a significant difference between the DR patients and healthy controls (p = 0.016).
The difference in SP-A1 between DR and normal controls was not significant (p = 0.57). However, despite the small sample size, DR showed a stiffer scleral response indicated by SP-HC (p = 0.039; Figure 4). In addition, the static pressure-volume ratio, as characterized by IOP (measured from DCT) divided by anterior chamber volume, was significantly higher in DR than in normal controls (0.14 ± 0.04 mmHg/µL vs. 0.10 ± 0.02 mmHg/µL; p = 0.013).
With DR and normal cohorts combined (n = 30), ocular rigidity was negatively correlated with axial length (Pearson R = −0.40, p = 0.027) and anterior chamber volume (R = −0.49, p = 0.006), and positively correlated with OPA (R = 0.66, p < 0.0001). Ocular rigidity was not correlated with central corneal thickness, radius of corneal curvature, or blood pressures. For the biomechanical parameters, ocular rigidity was shown to be positively correlated with SP-HC (R = 0.43, p = 0.018, Figure 5A) and static pressure-volume ratio (R = 0.57, p = 0.001, Figure 5B), while there was no significant correlation of ocular rigidity with SP-A1 (p = 0.28).

4. Discussion

Clinical and scientific evidence has confirmed the critical roles of biomechanics in the causes or consequences of eye diseases [27,28,29,30]. Ocular rigidity describes the combined mechanical behavior of ocular tissues including the sclera, cornea, choroid, and retina. Despite the relevance of ocular rigidity in ophthalmology, its clinical significance was limited by the lack of accurate in-vivo non-invasive measurement techniques. Studies in ocular rigidity in living human eyes and disease were mainly based on direct invasive manometric methods, which have shown to be less prone to errors compared to indirect measurements such as differential Schiotz tonometry [31]. Manometric measurement for ocular rigidity involves the injection of a given volume in the eye during cataract surgery under retrobulbar anesthesia [15], and because of its invasive nature, the clinical use of manometric technique in investigating the role of ocular rigidity in ophthalmology is restricted. Herein we implemented an approach for direct non-invasive measurement of ocular rigidity using high-speed OCT that incorporates time series [13]. This approach has been validated against manometry [17] and applied to investigate the ocular rigidity in glaucoma [13,32]. To the best of our knowledge, noninvasive measurement of ocular rigidity has not been evaluated in diabetic retinopathy.
Hyperglycemia leads to the accumulation of advanced glycation end products (AGEs) that are not only associated with dysfunctional vessels in the retina, but also cause stiffening in the connective tissues such as the cornea and sclera [6]. Our preliminary results showed that ocular rigidity in DR patients is significantly higher than in normal controls which is likely due to hyperglycemia-induced collagen crosslinking. Panagiotoglou et al. reported no significant difference in ocular rigidity measured from manometric devices between non-proliferative diabetic retinopathy and normal controls, but a clear trend of lower ocular rigidity was shown in patients with mild diabetic retinopathy compared to patients with severe non-proliferative diabetic retinopathy [33]. Note that the participants in our study are on average younger than those in Panagiotoglou et al. (average age 40 s vs. 70 s) [33]. In addition, diabetic patients in our study were limited to those with non-proliferative diabetic retinopathy of mild to moderate grade in the absence of macular edema. It would be interesting to investigate the association of ocular rigidity with the severity of DR in the future with our non-invasive approach which allows for estimation of ocular rigidity in participants of a wide range of ages without limiting to cataract surgery candidates.
Note that ocular rigidity accounts for the combined behaviors of the whole eye globe and cannot be attributed to the stiffness of any tissue alone. Because corneoscleral shell is the eye’s main load-bearing connective tissue, the corneoscleral shell predominantly contributes to ocular rigidity. Between the cornea and sclera, ocular rigidity is driven to a greater extent by the scleral stiffness than the corneal stiffness as illustrated in our previous study [13]. In this study, with subjects of DR and normal controls combined, ocular rigidity was shown to be positively correlated with SP-HC, whereas no significant correlation with SP-A1 was observed. Again, the ocular rigidity is driven mainly by the scleral stiffness, and this could be explained by the fact that the sclera has a higher modulus of elasticity and covers a greater surface area compared to the cornea. Noninvasive clinical measurement of pulsatile choroidal volume using dynamic OCT videos focuses on the posterior segment of the eye to examine the mechanical behavior of ocular tissues, whereas SP-A1 and SP-HC are stiffness parameters extracted from the dynamic response of the anterior segment of the eye. Interestingly, both anterior and posterior approaches reached similar results in the DR-associated alteration in ocular biomechanics, that is, DR has a stiffer response than normal controls, particularly in the sclera. We hypothesize that hyperglycemia-induced cross-linking causes tissue stiffening in diabetic retinopathy. Whether a stiffer sclera would impart resistance to the interfacial interactions between the retina and sclera warrants future investigations, which may in turn shed light on the parallel pathologic mechanisms of vascular deterioration and tissue stiffening with diabetic retinopathy in relation to hyperglycemia.
As a dynamic pressure-volume relationship, it is worth noting that ocular rigidity represents a structural mechanical parameter that depends on both the material properties and morphological features of ocular tissues. In other words, ocular rigidity does not reflect the material properties. Tissues with the same material properties, but different sizes or shapes could have different ocular rigidity. Specifically, the rigidity of larger eyes is lower when all other biomechanical and morphological factors are equal. Our results showed a negative correlation between ocular rigidity and axial length, consistent with manometric data that increasing axial length (or ocular volume) is associated with decreased ocular rigidity in cataract patients [34]. The relation of ocular rigidity with axial length may provide insights into the pathophysiology of myopia since axial length is the main determinant of non-syndromic myopia. Furthermore, a recent systematic review and meta-analysis has suggested that individuals with myopia exhibit a decreased risk for developing diabetic retinopathy, and an increased axial length contributes to this protective relationship [35]. Future studies are needed to elucidate the role of ocular rigidity in the association of myopia and diabetic retinopathy.
Limitations of this study include the relatively small number of subjects included. The small sample size was partially due to the difficulties in acquiring high image quality OCT video from patients with diabetic retinopathy and the high dependence on the image quality for the estimation of pulsatile choroidal volume change. With the advancement in OCT and image-processing techniques, a larger longitudinal study will be conducted to provide greater insights into DR-associated changes in ocular rigidity.
In conclusion, ocular rigidity was estimated based on the pulsatile change in choroidal volume characterized by dynamic OCT videos and pulsatile change in IOP measured by dynamic contour tonometry. Our approach provides a direct, non-invasive way to investigate the ocular rigidity in diabetic retinopathy. Compared to normal controls, diabetic retinopathy demonstrated higher ocular rigidity and higher scleral stiffness in this preliminary study. Further studies are needed to verify the stiffer response in diabetic retinopathy and how it is associated with increased nonenzymatic cross-linking of collagen fibrils in the presence of hyperglycemia. Understanding hyperglycemia-driven ocular alterations has the potential to improve early diagnosis in diabetic retinopathy, as well as monitoring of progression.

Author Contributions

Conceptualization, Y.M., M.P.O. and C.J.R.; methodology, Y.M.; formal analysis, Y.M.; writing—original draft preparation, Y.M.; writing—review and editing, Y.M., M.P.O. and C.J.R.; funding acquisition, C.J.R. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Institutes of Health under Grant R01EY027399 and the Ohio Lions Eye Research Foundation under Grant W. R. Bryan Diabetic Eye Disease.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of The Ohio State University (Protocol No. 2016H0327 and 2017H0417).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Requests to access the datasets should be directed to Cynthia Roberts, roberts.8@osu.edu.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The en face view and sequential OCT B-scans of the posterior eye in (A) a normal subject and (B) a patient with diabetic retinopathy.
Figure 1. The en face view and sequential OCT B-scans of the posterior eye in (A) a normal subject and (B) a patient with diabetic retinopathy.
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Figure 2. The framework of estimating the pulsatile change in choroidal thickness. The optic nerve region was excluded from the region of interest, and the posterior retinal pigment epithelium (RPE) of each side of the optic nerve was flattened before the segmentation of the choroidal–scleral interface. The average distance between the posterior RPE (i.e., the retinal-choroidal interface) and the choroidal–scleral interface was calculated as the choroidal thickness for a certain frame. The average peak-to-valley distance in the filtered choroidal thickness waveform was calculated as the pulsatile change in choroidal thickness.
Figure 2. The framework of estimating the pulsatile change in choroidal thickness. The optic nerve region was excluded from the region of interest, and the posterior retinal pigment epithelium (RPE) of each side of the optic nerve was flattened before the segmentation of the choroidal–scleral interface. The average distance between the posterior RPE (i.e., the retinal-choroidal interface) and the choroidal–scleral interface was calculated as the choroidal thickness for a certain frame. The average peak-to-valley distance in the filtered choroidal thickness waveform was calculated as the pulsatile change in choroidal thickness.
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Figure 3. The distribution of ocular rigidity in 8 subjects with diabetic retinopathy (DR) and 22 healthy controls. DR patients show a higher ocular rigidity than normal controls. The mean difference between these two groups is about 0.005 µL−1 (95% confidence interval, 0.0011 to 0.0096 µL−1, as indicated by the error bar).
Figure 3. The distribution of ocular rigidity in 8 subjects with diabetic retinopathy (DR) and 22 healthy controls. DR patients show a higher ocular rigidity than normal controls. The mean difference between these two groups is about 0.005 µL−1 (95% confidence interval, 0.0011 to 0.0096 µL−1, as indicated by the error bar).
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Figure 4. Comparison between diabetic retinopathy (DR) subjects and normal controls in (A) SP-A1 and (B) SP-HC. Asterisk * indicates statistically significant (p < 0.05) between normal controls and DR cohorts.
Figure 4. Comparison between diabetic retinopathy (DR) subjects and normal controls in (A) SP-A1 and (B) SP-HC. Asterisk * indicates statistically significant (p < 0.05) between normal controls and DR cohorts.
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Figure 5. Ocular rigidity was positively correlated with (A) SP-HC (R = 0.43; p = 0.018) and (B) static pressure-volume ratio (R = 0.57; p = 0.001).
Figure 5. Ocular rigidity was positively correlated with (A) SP-HC (R = 0.43; p = 0.018) and (B) static pressure-volume ratio (R = 0.57; p = 0.001).
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Table
Table 1. The clinical characteristics of all participants (n = 30) and their statistical distribution.
Table 1. The clinical characteristics of all participants (n = 30) and their statistical distribution.
CharacteristicsDiabetic Retinopathy
(n = 8)		Normal Controls
(n = 22)		p Value
Age (y)44.1 ± 15.941.0 ± 12.90.59
Sex (female)4 (50%)12 (54%)0.85
GAT (mmHg)16.0 ± 3.115.3 ± 2.70.55
Axial Length (mm)24.3 ± 0.924.8 ± 0.90.20
CCT (µm)564.5 ± 53.8560.0 ± 31.20.78
Radius of Corneal Curvature (mm)7.9 ± 0.37.7 ± 0.20.07
Anterior Chamber Volume (µL)149.5 ± 34.2175.2 ± 30.80.06
Systolic BP (mmHg)125.4 ± 7.6117.4 ± 19.50.08
Diastolic BP (mmHg)88.7 ± 13.082.2 ± 13.10.22
Patients with diabetic retinopathy were limited to those with non-proliferative diabetic retinopathy of mild to moderate grade in the absence of macular edema. GAT = Goldmann applanation tonometer measured intraocular pressure; CCT = central corneal thickness; BP = blood pressure. Difference in systolic BP, and diastolic BP was assessed by the nonparametric Mann–Whitney U-test, and difference in age, GAT, axial length, CCT, radius of corneal curvature, and anterior chamber volume was evaluated by the two-sample t-test. Statistical significance threshold was p < 0.05. None of the characteristics were significantly different between diabetic retinopathy patients and normal controls.
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Ma, Y.; Ohr, M.P.; Roberts, C.J. Preliminary Results of Noninvasive Ocular Rigidity in Diabetic Retinopathy Using Optical Coherence Tomography. Photonics 2022, 9, 598. https://doi.org/10.3390/photonics9090598

AMA Style

Ma Y, Ohr MP, Roberts CJ. Preliminary Results of Noninvasive Ocular Rigidity in Diabetic Retinopathy Using Optical Coherence Tomography. Photonics. 2022; 9(9):598. https://doi.org/10.3390/photonics9090598

Chicago/Turabian Style

Ma, Yanhui, Matthew P. Ohr, and Cynthia J. Roberts. 2022. "Preliminary Results of Noninvasive Ocular Rigidity in Diabetic Retinopathy Using Optical Coherence Tomography" Photonics 9, no. 9: 598. https://doi.org/10.3390/photonics9090598

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