New biomarkers may detect early eye changes that can lead to diabetes-
related blindness

Date:
August 13, 2021
Source:
Indiana University
Summary:
Researchers have identified new biomarkers that may advance the
early detection of diabetic retinopathy, the most common diabetic
eye disease and a leading cause of blindness in U.S. adults.



FULL STORY ==========================================================================
New biomarkers found in the eyes could unlock the key to helping manage diabetic retinopathy, and perhaps even diabetes, according to new research conducted at the Indiana University School of Optometry.


========================================================================== During its early stages, diabetes can affect the eyes before the changes
are detectable with a regular clinical examination. However, new retinal research has found that these changes can be measured earlier than
previously thought with specialized optical techniques and computer
analysis.

The ability to detect biomarkers for this sight-threatening condition
may lead to the early identification of people at risk for diabetes
or visual impairment, as well as improve physicians' ability to manage
these patients.

The study appears in the journal PLOS One.

"Early detection of retinal damage from diabetes is possible to obtain
with painless methods and might help identify undiagnosed patients
early enough to diminish the consequences of uncontrolled diabetes,"
said study co-author Ann E. Elsner, a Distinguished Professor at the IU
School of Optometry.

Diabetic retinopathy, which is caused by changes in the blood vessels in
the retina, is the most common diabetic eye disease and a leading cause
of blindness in U.S. adults. From 2010 to 2050, the number of Americans
with diabetic retinopathy is expected to nearly double, from 7.7 million
to 14.6 million.

The new study is part of the current widespread emphasis on detection
of diabetic retinopathy through artificial intelligence applied to
retinal images.

However, some of these algorithms provide detection based on features
that occur much later than the changes found in this study.

The IU-led method advances earlier detection because of the retinal
image processing algorithms described in the study.

"Many algorithms use any image information that differs between diabetic patients and controls, which can identify which individuals might have diabetes, but these can be nonspecific," Elsner said. "Our method can be combined with the other AI methods to provide early information localized
to specific retinal layers or types of tissues, which allows inclusion
of information not analyzed in the other algorithms." Elsner conducted
the retinal image analysis in her lab at the IU School of Optometry's
Borish Center for Ophthalmic Research, along with her co-author, Joel
A. Papay, a Ph.D. student in the Vision Science Program at the school.

They used data collected from volunteers with diabetes, along with
healthy control subjects. Additional data were also collected from a
diabetic retinopathy screening of members of the underserved community
at the University of California, Berkeley, and Alameda Health.

The computer analysis was performed on retinal image data commonly
collected in well-equipped clinics, but much of the information used in
this study is often ignored for diagnosis or management of patients.

========================================================================== Story Source: Materials provided by Indiana_University. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Joel A. Papay, Ann E. Elsner. Quantifying frequency content
in cross-
sectional retinal scans of diabetics vs. controls. PLOS ONE, 2021;
16 (6): e0253091 DOI: 10.1371/journal.pone.0253091 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/08/210813105533.htm

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