{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T12:16:33Z","timestamp":1791807393486,"version":"4.3.4"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T00:00:00Z","timestamp":1738540800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell Dev. Biol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Measure and analyze retinal vascular parameters in individuals with varying glucose metabolism, explore preclinical retinal microstructure changes related to diabetic retinopathy (DR), and assess glucose metabolism\u2019s impact on retinal structure.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The study employed a cross-sectional design encompassing a 4-year period from 2020 to 2024. Fundus photographs from 320 individuals (2020\u20132024) were categorized into non-diabetes, pre-diabetes, type 2 diabetes mellitus (T2DM) without DR, and T2DM with mild-to-moderate non-proliferative DR (NPDR) groups. An artificial intelligence (AI)-based automatic measurement system was used to quantify retinal blood vessels in the fundus color photographic images, enabling inter-group parameter comparison and analysis of significant differences.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Between January 2020 and June 2024, fundus color photographs were collected from 320 individuals and categorized into four groups: non-diabetes (n = 54), pre-diabetes (n = 71), T2DM without overt DR (n = 144), and T2DM with mild-to-moderate NPDR (n = 51). In pairwise comparisons among individuals with pre-diabetes, T2DM without DR, and T2DM with mild-to-moderate NPDR. Fasting blood glucose (FBG), glycated hemoglobin (HbA1c), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were significantly different (\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.05). Within the T2DM population, FBG, HbA1c, age, SBP, and DBP were significant predictors for mild-to-moderate NPDR (\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.05). Average venous branching number (branch_avg_v) was significantly different among pre-diabetes, T2DM without DR, and T2DM with mild-to-moderate NPDR groups. In patients with T2DM with mild-to-moderate NPDR, Average length of arteries (length_avg_a) and average length of veins (length_avg_v) increased, whereas branch_avg_v, average venous branching angle (angle_avg_v), average venous branching asymmetry (asymmetry_avg_v),overall length density (vessel_length_density), and vessel area density (vessel_density) decreased significantly (\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.05). Logistic regression analysis identified length_avg_a, branch_avg_v, angle_avg_v, asymmetry_avg_v, vessel_length_density, and vessel_density as independent predictors of mild-to-moderate NPDR in patients with T2DM. Receiver Operating Characteristic (ROC) curve analysis demonstrated that length_avg_a, length_avg_v, branch_avg_v, angle_avg_v, asymmetry_avg_v, vessel_length_density, and vessel_density had diagnostic value for mild-to-moderate NPDR (\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In individuals diagnosed with T2DM, specific retinal vascular parameters, such as branch_avg_v and vessel_density, demonstrate a significant correlation with mild-to-moderate NPDR. These parameters hold promise as preclinical biomarkers for detecting vascular abnormalities associated with DR.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcell.2025.1550176","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T01:35:43Z","timestamp":1738546543000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Comparative analysis of retinal vascular structural parameters in populations with different glucose metabolism status based on color fundus photography and artificial intelligence"],"prefix":"10.3389","volume":"13","author":[{"given":"Naimei","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhentao","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinrong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinya","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihua","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,2,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1097\/IAE.0000000000002373","article-title":"Quantitative optical coherence tomography angiography features for objective classification and staging of diabetic retinopathy","volume":"40","author":"Alam","year":"2020","journal-title":"Retina"},{"key":"B2","doi-asserted-by":"publisher","first-page":"S185","DOI":"10.2337\/dc22-S012","article-title":"12. 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