To examine subtle differences in the structure of diabetic vs. control retinas.
Spectral-domain optical coherence tomography (SD-OCT) images were compared for the retinas of 33 diabetic subjects who did not have clinical evidence of diabetic macular edema and age-matched controls, with central macular thicknesses of 275 and 276 microns, respectively. Cross-sectional retinal images through the fovea, called B-scans, were analyzed for spatial frequency content. The B-scans were processed to remove and smooth the portions of the retinal image not within regions of interest in the retina. The remaining retinal images were then quantified using a Fast Fourier Transform (FFT) approach that provided amplitude as a function of spatial frequency.
The FFT analysis showed that diabetic retinas had spatial frequency content with significantly higher power compared to control retinas particularly for a deeper fundus layer at mid-range spatial frequencies, ranging from p = 0.0030 to 0.0497 at 16.8 to 18.2 microns/cycle. There was lower power at higher spatial frequencies, ranging from p = 0.0296 and 0.0482 at 27.4 and 29.0 microns/cycle. The range of mid-range frequencies corresponds to the sizes of small blood vessel abnormalities and hard exudates. Retinal thickness did not differ between the two groups.
Diabetic retinas, although not thicker than controls, had subtle but quantifiable pattern changes in SD-OCT images particularly in deeper fundus layers. The size range and distribution of this pattern in diabetic eyes were consistent with small blood vessel abnormalities and leakage of lipid and fluid. Feature-based biomarkers may augment retinal thickness criteria for management of diabetic eye complications, and may detect early changes.
Data are held in a public repository (Harvard Dataverse,
Neural and vascular changes in the retina are a main feature of diabetic retinopathy and diabetic macular edema, the sight threatening complications in the retina of many diabetics [
It has long been known that there are sight-threatening retinal complications that could not be detected with older imaging and clinical examination methods [
The measurement of retinal thickening provides a widely adopted method of detecting and managing diabetic retinopathy and diabetic macular edema, often comparing one or more metrics of a patient’s retina to a normative database, and interpreting the data in terms of ruling out other reasons for the thickening [
(A) Typical diabetic macular edema, showing a thickened retina that results in a high value of central macular thickness. There are fluid-filled cystic spaces that appear dark, hyper-reflective lipid and protein deposits (hard exudates), and disrupted photoreceptor layers that lie beneath retina blood vessels. The large areas of fluid produce multiply scattered light, instead of leading to interference, and therefore appear dark. (B) Significant pathological changes, but a low value of retinal thickness typically attributed due to damage to neurons and their support cells. There are numerous hard exudates, and significant disruption to retinal layers including photoreceptors. (C) Traction along with a detaching vitreous, the topmost reflective layer that is tilted, leading to retinal thickening, but not very severe diabetic changes. The large black areas within the retina demonstrate the fluid built up by the traction and are consistent with a high value of central macular thickness. (D) Diabetic retina with normal retinal thickness and minimal diabetic changes. These data are from the large dataset collected in a group of clinics for the underserved in Alameda County, CA, as described in ref [
An increase in retinal thickness may not be a sensitive measure of retinal pathology for a number of reasons. A common pathological change in diabetic patients’ retinas is the damage and loss of neural elements [
An alternative is to consider a combination of methods that provide thickness and features. At high magnification, we have shown that hard exudates can be seen in great numbers in eyes not classified within the more severe categories of diabetic retinopathy [
Subjects for the OCT computations were recruited from the Indiana University School of Optometry clinic. The diabetic subjects were diagnosed as not having diabetic retinopathy or macular edema during a comprehensive ophthalmological exam by a faculty member. The duration of diabetes was self-reported for all but one subject, ranging from 1–25 yr, mean = 6.78 ± 6.06 yr. The HbA1c was self-reported as < 7 by 18 of 33 subjects, as > 7 by 7 subjects, and unreported by 8 subjects. Thirty-three subjects with diabetes and 33 age- and sex-matched controls were recruited. There were 15 males and 18 females in each group. One of the diabetic subjects had Type 1 diabetes, with the rest having Type 2.
Written informed consent was obtained from all of the subjects, and the experiments conformed to the principles expressed in the Declaration of Helsinki. This research was approved by the Indiana University Institutional Review Board for all subjects. We selected subjects to illustrate the problem with limiting the diagnosis of pathological changes in diabetic patients to only central macular thickness values, shown in
There was no statistical difference in age between the diabetics and controls, with the diabetics having a mean age of 58.9 yr and the controls having a mean age of 58.1 yr (p = 0.703). There was no statistical difference between males and females for age (p = 0.413), with males having a mean age of 57.5 yr and females having a mean age of 59.4 yr. There was also no statistical difference in the refractive errors between the groups (p = 0.220). The diabetics had a mean spherical equivalent error of -0.86D, with a standard deviation of 1.70D, and had a range from -4.25D to +2.00D. Control subjects had a mean spherical equivalent error of -0.36D, with a standard deviation of 1.58D, and ranged from -4.50D to +1.86D. As most of the subjects were not highly myopic, alterations in retinal layers could not be attributed to myopic degeneration or errors in the samples between groups due to magnification.
Subjects were imaged within one year of exam using spectral domain optical coherence tomography (SD-OCT) (Spectralis, Heidelberg Engineering, Heidelberg, Germany). The data were analyzed in several ways. To investigate differences in retinal thickness on a coarse scale between diabetics and controls, retinal thickness was obtained for each region of the ETDRS grids, as given by vendor software. For 12 of the 132 (9%) ETDRS outer grid values data were unavailable, and analysis proceeded with these data missing rather than using imputation.
To examine differences on a finer scale than the whole thickness of the retina, individual retinal layers were quantified. The horizontal, foveal centered B-scan was selected for each subject, from a volume scan that was collected from the subject, with measurements from a 15 deg region. Images were exported using the option that allows each pixel to have the same axial and lateral resolution. The B-scans were automatically segmented using custom MATLAB software (Mathworks, Natick, MA), with the segmentation reviewed and corrected manually if necessary. The retinal segmentation algorithm was based on a method previously used for OCT images from patients with retinal disease [
Retinal thicknesses for five domains (
A) The full retinal domain. B) The domain from the ISOS junction to the ILM. C) The domain from the ISOS to the boundary between the IPL and INL. D) The domain from the ISOS junction to the boundary between the NFL and GCL. E) The domain from the boundary between the RPE and CH to the ISOS junction.
A third analysis was performed to investigate the spatial detail that was not limited to thickness changes, by performing 2-dimensional Fourier analyses on these same five domains. A separate image was created for each domain, and then processed to reduce spurious frequencies, as follows. The images, 768 pixels wide, and always having a smaller height, were placed into a new image of 800x800 pixels, to reduce the complexity of frequency computations. All areas outside of the domain were set to have the same intensity as the mean intensity of the domain. The domains were all then flattened with respect to the ISOS junction, to avoid frequencies that could be introduced due to retinal shape [
A) Representative b-scan from a diabetic subject, showing the full retinal domain, with the areas outside of the retina being set to the average intensity of the retina, and showing the ramping effects for edge smoothing on the sides of the retina. B) B-scan of the control subject. C) Average pixel intensity through the retina, with 0 as the inner-most position of the retina for the diabetic and control subjects in A and B, with the diabetic in blue and control in orange. D) Log transform of the power spectra for the subjects in A and B.
Power spectra from the Fourier transforms were computed to analyze frequency content. Frequency was computed by measuring the fiduciary marks given on the images, which was 33 pixels per 200 microns, both laterally and axially. The number of microns per cycle for DC was computed as 4848.49, with the remaining microns per cycle being derived from that number. Paired one-tailed t-tests were used for comparison, because it was hypothesized that for the higher spatial frequencies, the diabetic subjects would have more power, due to small scale reflection or tissue property changes due to diabetes, such as to small blood vessels and other fine features. For lower spatial frequencies, the normal subjects were hypothesized to have more power due to the more regular layer structure, i.e. the grosser features. To examine the trends for individual subjects, z-scores of the amplitude distribution were computed at each frequency for each subject.
There were no statistical differences in retinal thickness from the ETDRS regions between the groups using paired two-tailed t-tests (
Location 0 is the fovea, negative locations are in degree steps temporal to the fovea, and positive locations are in degree steps nasal to the fovea. There were no statistically significant differences in thickness between the diabetic and control subjects at any location, with diabetics in blue and controls in orange. That is, the trends seen in the figure are not significant, for diabetic subjects on average being thicker at all locations temporal to the fovea, and the control subjects being thicker on average at the fovea and for all locations nasal to the fovea.
Diabetics | Controls | ||||
---|---|---|---|---|---|
Region | Mean (μm) | St. Dev. (μm) | Mean (μm) | St. Dev. (μm) | p |
Central | 275 | 20.2 | 276 | 26.1 | 0.800 |
Inner Nasal | 343 | 19.1 | 344 | 21.3 | 0.854 |
Outer Nasal | 310 | 19.0 | 316 | 25.9 | 0.339 |
Inner Inferior | 335 | 19.4 | 340 | 19.8 | 0.227 |
Outer Inferior | 291 | 18.7 | 301 | 19.2 | 0.083 |
Inner Temporal | 326 | 17.8 | 330 | 20.6 | 0.409 |
Outer Temporal | 282 | 17.2 | 288 | 17.9 | 0.212 |
Inner Superior | 340 | 18.9 | 342 | 22.4 | 0.595 |
Outer Superior | 302 | 17.9 | 304 | 22.4 | 0.383 |
Thickness measurements of the domain from the boundary between the RPE and CH to the ISOS junction (
Location zero is the fovea, negative locations indicate degrees temporal to the fovea, and positive locations indicate degrees nasal to the fovea. Orange circles are the control subjects, and blue circles are the diabetic subjects. Control subjects were significantly thicker than the diabetic subjects at 1 deg temporal to the fovea and 3 deg nasal to the fovea. These data do not support the idea that diabetic subjects as a whole develop thicker deep retinal layers prior to clinical disease, for locations in the central retina.
The distribution of power varied with frequency in a different manner for normal subjects compared with diabetic subjects, particularly for the deeper layers (Figs
A) Average power spectrum for diabetics and controls for the domain that ranges from the boundary between the RPE and CH to the ISOS junction. B) Controls had statistically significant more power in the frequency range from 25.5 to 29 microns/cycle compared to diabetics. C) Diabetics had statistically significant more power in the frequency range from 15.5 to 18.2 microns/cycle compared to controls.
A) Individual z-scores for the control subjects computed for the power at each frequency for the domain from the boundary between the RPE and CH to the ISOS junction. B) Individual z-scores for the diabetic subjects in the same domain. C) The averaged z-score for the diabetic subjects at each frequency for the same domain. The diabetics consistently had more power at the higher frequencies than the control subjects.
Spectral power of the diabetics was significantly greater than for the control subjects for the full retinal domain in the frequency range of 21.9 to 24.2 microns/cycle, with significance values ranging from p = 0.0245 at 22.9 microns / cycle to 0.0491 at 22 microns/cycle. Recall that there were no statistically significant differences in thickness for the 15 measured locations (
For the domain between the ISOS junction and the ILM (
The domain between the ISOS junction and the boundary between the NFL and GCL (
The domain contained between the ISOS junction to the boundary between the IPL and INL (
In addition to frequency content, retinal thickness was also measured. In all ETDRS regions for the control subjects, a linear regression revealed that there was a decrease in thickness with increasing age. The association was weak however, with the largest r-squared value being 0.124, for the inner superior region. However, for the diabetic subjects, this was not the case. For the central subfield and the four inner regions, there was an increase in thickness as a function of age, but, like the control subjects, for the four outer regions, there was a decrease in thickness as a function of age. This association was also weak, with the largest r-squared value being 0.0333 (
Diabetics | Controls | |||
---|---|---|---|---|
Region | Linear Regression | R2 | Linear Regression | R2 |
Central | y = 0.217x + 262 | 0.0096 | y = -0.429x + 301 | 0.0247 |
Inner Nasal | y = 0.127x + 335 | 0.0037 | y = -0.791x + 390 | 0.0037 |
Outer Nasal | y = -0.0395x + 312 | 0.0004 | y = -0.761x + 359 | 0.0710 |
Inner Inferior | y = 0.226x + 321 | 0.0112 | y = -0.570x + 373 | 0.0756 |
Outer Inferior | y = -0.0207x + 293 | 0.0001 | y = -0.346x + 321 | 0.0277 |
Inner Temporal | y = 0.357x + 305 | 0.0333 | y = -0.618x + 366 | 0.0822 |
Outer Temporal | y = -0.0854x + 287 | 0.0020 | y = -0.241x + 301 | 0.0148 |
Inner Superior | y = 0.156x + 330 | 0.0057 | y = -0.828x + 390 | 0.124 |
Outer Superior | y = -0.226x + 315 | 0.0122 | y = -0.615x + 340 | 0.0122 |
This study of 33 diabetic subjects without clinical signs of diabetic retinopathy or macular edema who were paired with control subjects analyzed the frequency content of OCT images as well as retinal thicknesses. Unlike previous studies that showed a significant difference in thickness at the foveal center of control eyes compared to diabetic eyes, even when there was no evidence of retinopathy in the diabetics [
Despite the lack of thickness differences between the control and diabetic subjects, the frequency analysis indicates that there are differences between these two groups, particularly for the domain contained between the RPE and CH boundary to the ISOS junction. This could be due to a buildup of lipids and proteins in the outer retina, potentially leading to the formation of hard exudates [
This method for measuring the frequency content of retinal OCT images is objective and requires a trained grader only to ensure that the image segmentation is correct. Our data are consistent with detecting an enhancement of power with the spatial frequencies associated with the high frequencies of small structural changes, i.e. hyper-reflective foci and the precursors to hard exudates. There is also the decrease of power for the diabetic subjects for the lower frequencies in the domain of the deeper layers, consistent with less regularity of layer thicknesses or borders. The findings indicate that there is not a consistent trend for the diabetic subjects without diabetic retinopathy or macular edema to have increased thickness, and that the normal subjects sometimes have the thicker retinas. Thus, techniques with increased axial resolution will not improve the sensitivity of detection of the effects of diabetes on the retina for thickness measures in isolation of other information, since the diabetic retinas are not as a whole thicker and neural retinal thinning can occur early in the disease. Further, it is likely that when only retinal thickness is used to classify patients, then false negatives can occur (
We thank Drs. Jorge Cuadros, Glen Ozawa, and Taras Litvin for the collection of the diabetic data from the Alameda Health clinic for underserved patients. We thank Mr. Matthew Muller and Drs. Shane G. Brahm, Stuart B. Young, and Andréa V. Walker-Adeyemi for the organization of the data of the underserved patients, and Drs. Christopher A. Clark and Victor E. Malinovsky for the grading of the OCT images.
PONE-D-21-09236
Quantifying frequency content in cross-sectional retinal scans of diabetics vs. controls
PLOS ONE
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Reviewer #1: The authors present a novel and potentially important analysis of SD-OCT images in persons with diabetes vs age-matched controls. The study is well-designed and presented. The key point is the difference in spatial frequency content between the two groups in a manner than varied with the spatial frequencies. Several points should be clarified:
1. The patients were not found to have visible microvascular lesions on clinical examination but it would be helpful to know how the exams were performed and whether or not they included fundus photographs.
2. It is not clear if the subjects were recruited only in Bloomington and/or in Alameda
3. Please provide the diabetes duration and hemoglobin A1c values if known for the diabetes subjects.
4. the cause of the differences cannot be determined from this study alone and subclinical vascular leakage is one possibility. Another could be molecular structural alterations from diabetes.
5. The authors modestly do not point out the innovation of the method but I think it is worthwhile if they wish to do so.
Hopefully the authors will perform longitudinal assessment of the subjects.
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RE: PONE-D-21-09236
Quantifying frequency content in cross-sectional retinal scans of diabetics vs. controls
PLOS ONE
Thank you for the careful review of our article about quantifying the differences in frequency content from OCT images gathered from diabetic vs control subjects. We appreciate the amount of time it takes to appropriately review articles, and the suggestions that were made.
1. The patients were not found to have visible microvascular lesions on clinical examination but it would be helpful to know how the exams were performed and whether or not they included fundus photographs.
We have reorganized the wording of the Methods paragraph in response to items 1, 2, and 3:
Subjects for the OCT computations were recruited from the Indiana University School of Optometry clinic. The diabetic subjects were diagnosed as not having diabetic retinopathy or macular edema during a comprehensive ophthalmological exam by a faculty member. The duration of diabetes was self-reported for all but one subject, ranging from 1 – 25 yr, mean = 6.78 + 6.06 yr. The HbA1c was self-reported as < 7 by 18 of 33 subjects, as > 7 by 7 subjects, and unreported by 8 subjects. Thirty-three subjects with diabetes and 33 age- and sex-matched controls were recruited. There were 15 males and 18 females in each group. One of the diabetic subjects had Type 1 diabetes, with the rest having Type 2.
Written informed consent was obtained from all of the subjects, and the experiments conformed to the principles expressed in the Declaration of Helsinki. This research was approved by the Indiana University Institutional Review Board for all subjects. We selected subjects to illustrate the problem with limiting the diagnosis of pathological changes in diabetic patients to only central macular thickness values, shown in Fig 1, from a diabetic retinopathy screening study with consent and study approval also through the University of California Berkeley and Alameda Health for the subjects.
The patients in the OCT computation study were not patients from a diabetic retinopathy screening study, as were the patients in Fig. 1, but rather patients scheduled for their standard dilated fundus exams. Color fundus photographs are not routinely taken.
On page 7, line 159, we added, ”Subjects were imaged within one year of exam using spectral domain optical coherence tomography (SD-OCT).” The effect of the potential development of retinopathy was minimal, since the OCT thickness values did not differ from control values. This is stated on lines 228-233.
2. It is not clear if the subjects were recruited only in Bloomington and/or in Alameda
We clarified that the OCT computation subjects were only from Bloomington by the above rewording in Methods. The Fig 1 subjects were from our screening study in Alameda, included to illustrate how diabetes can lead to not only thickening of the human retina, but also to thinning. The subjects recruited from Bloomington for this study do not exhibit those later stages of DR, as we want to detect earlier stages of the disease. Thus, the Bloomington subjects could not illustrate causes for abnormal central retinal thickness because as seen in Table 1, our diabetic and control subjects have similar retinal thickness based on the ETDRS grids from OCT.
3. Please provide the diabetes duration and hemoglobin A1c values if known for the diabetes subjects.
The duration data and HbA1c range are now provided where available in Methods, Lines 133-136. In our clinic, duration and HbA1c values are self-reported during routine clinical examinations, so they should not be used for scientific results. We are reporting what subjects state during their most recent clinical examination to the date of OCT testing.
4. The cause of the differences cannot be determined from this study alone and subclinical vascular leakage is one possibility. Another could be molecular structural alterations from diabetes.
We agree on this point. We changed the wording and recalled again references 1 (Antonetti) and 6 and 7 for vascular changes:
Lines 363-365 Molecular changes to vascular, neural, or glial tissue [1], including but not limited to vascular remodeling [6, 7], are also potential sources of the frequency content differences. Note that our findings are limited to the resolution of the instrumentation used.
5. The authors modestly do not point out the innovation of the method but I think it is worthwhile if they wish to do so.
Thank you. We appreciate the comment and hope that this and other analyses expected from pathological changes will enter into classification methods, as this methodology can be implemented with basic software updates to analyze these kinds of data.
Hopefully the authors will perform longitudinal assessment of the subjects.
While only some of these subjects have test-retest data, the current grant in Dr. Burns’ lab, EY024315, is a longitudinal study. At each visit, the normal data collection includes OCT data of sufficient density and comparable macular grid size to permit further analysis.
Thanks for your time and consideration,
Dr. Ann E. Elsner and Joel A. Papay
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Quantifying frequency content in cross-sectional retinal scans of diabetics vs. controls
PONE-D-21-09236R1
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PONE-D-21-09236R1
Quantifying frequency content in cross-sectional retinal scans of diabetics vs. controls
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