{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T22:51:07Z","timestamp":1756075867510,"version":"3.41.2"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"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. Aging Neurosci."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Alzheimer\u2019s disease (AD) is a neurodegenerative disorder characterized by progressive, irreversible brain damage. Current diagnostic procedures for AD are both costly and highly invasive for patients. Age-related cataract (ARC), a common ocular condition in elderly populations, correlates with a 1.43-fold increased risk of developing AD. This study sought to establish a novel model for early detection of mild cognitive impairment (MCI) in patients with ARC.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The study prospectively collected 170 monocular data as training dataset and 65 monocular data from another independent medical center as test dataset. Demographic data and comprehensive ophthalmic examination results were collected. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression analysis were performed using R software for dimensionality reduction and variable selection. A nomogram was constructed, and its discriminative ability was evaluated using receiver operating characteristic (ROC) curve, area under the ROC curve (AUC) with 95% confidence interval (CI), as well as sensitivity and specificity. Internal validation was performed using 1,000-resample bootstrap analysis, while model calibration was assessed through calibration curves and Brier scores. Decision curve analysis (DCA) was performed to evaluate clinical utility. A baseline model incorporating demographic variables was developed for comparison with the nomogram. Additionally, an external dataset from an independent medical center was employed as a test set to further validate the nomogram\u2019s predictive performance. An online calculator was created using the \u201cDynNom\u201d and \u201crsconnect\u201d functions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Through LASSO regression and multivariate logistic regression analyses, six variables were identified and incorporated into the nomogram: age (OR: 1.097; 95%CI: 1.041\u20131.161; <jats:italic>p<\/jats:italic> &amp;lt; 0.001), years of education (OR: 0.333; 95%CI: 0.140\u20130.749; <jats:italic>p<\/jats:italic> = 0.010), diastolic blood pressure (OR: 0.949; 95%CI: 0.907\u20130.990; <jats:italic>p<\/jats:italic> = 0.019), short posterior ciliary artery flow rate (OR: 1.063; 95%CI: 1.008\u20131.132; <jats:italic>p<\/jats:italic> = 0.038), vertical cup-to-disc ratio (OR: 11.927; 95%CI: 1.059\u2013155.308; <jats:italic>p<\/jats:italic> = 0.049), and peripapillary retinal nerve fiber layer thickness (inferior; OR: 0.979; 95%CI: 0.964\u20130.993; <jats:italic>p<\/jats:italic> = 0.005). The nomogram demonstrated strong discriminatory power for the diagnosis of MCI, with the area under the ROC curve reaching 0.791 (95%CI: 0.722\u20130.864) in the training dataset and 0.750 (95%CI: 0.627\u20130.858) in the external dataset. Calibration curve validation showed good agreement between predicted and ideal probabilities (<jats:italic>p<\/jats:italic> &amp;gt; 0.05, Brier score = 0.171). DCA indicated substantial net benefit across most threshold probabilities in both training and test datasets, supporting the nomogram\u2019s clinical utility.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Through systematic analysis of clinical data, this study established and validated a novel online calculator for identifying early cognitive impairment in patients with ARC, using demographic and ocular biomarkers, thereby providing a visual representation of the prediction model.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnagi.2025.1492804","type":"journal-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T05:27:14Z","timestamp":1745299634000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Early identification of mild cognitive impairment: an innovative model using ocular biomarkers"],"prefix":"10.3389","volume":"17","author":[{"given":"Lingjing","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanwei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zi","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaohui","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,4,22]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.jalz.2011.03.008","article-title":"The diagnosis of mild cognitive impairment due to Alzheimer\u2019s disease: Recommendations from the National Institute on Aging-Alzheimer\u2019s Association workgroups on diagnostic guidelines for Alzheimer\u2019s disease.","volume":"7","author":"Albert","year":"2011","journal-title":"Alzheimers Dement."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1016\/j.ijbiomac.2020.06.239","article-title":"Biosensors for detection of Tau protein as an Alzheimer\u2019s disease marker.","volume":"162","author":"Ameri","year":"2020","journal-title":"Int. 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