Pilot study demonstrates non-invasive assessment of conjunctival microvascular changes in type 1 diabetes, highlighting potential diagnostic value.
Background/Introduction Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease often complicated by microvascular changes. While retinal microangiopathy is well-documented, changes in the microcirculation of the bulbar conjunctiva are understudied. Given the conjunctiva’s accessibility, it may offer a valuable, non-invasive site for evaluating microvascular dysfunction in diabetes. Purpose This pilot study aimed to develop and test a model for quantifying conjunctival microcirculation architecture parameters in individuals with T1DM using digital imaging and an original software analysis. Methods Adult patients with T1DM were enrolled. Each participant underwent a detailed ophthalmic assessment, including slit-lamp examination and conjunctival biomicroscopy. High-resolution video sequences of the bulbar conjunctiva were recorded using a camera attached to the slit-lamp. These short recordings were analyzed through a semi-automated workflow, which included manual selection of adequate clips and the use of custom-developed software to extract quantitative microcirculation parameters. Clinical data such as glycated hemoglobin (HbA1c), disease duration, and the presence of diabetic microvascular complications were also collected. Results The developed algorithm provided key microvascular metrics from the conjunctival recordings, which included 161 microvessels: mean vessel diameter was 13.8 ± 6.1 μm, blood velocity averaged 0.44 ± 0.07 mm/s, and blood flow ranged from 4 pL/s to 92.2 pL/s, depending greatly on vessel size. Conclusion(s) This study presents a semi-automated, non-invasive approach for assessing conjunctival microcirculation in patients with T1DM. Preliminary data suggest this technique is efficient in data acquisition, and the results can be compared to similar works from this field. Further validation in larger cohorts is necessary to determine its role in clinical practice and its potential as a diagnosis or monitoring tool in diabetes care.
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Șeremet et al. (2025) studied this question.
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