Early identification of mild cognitive impairment: an innovative model using ocular biomarkers
BackgroundAlzheimer’s 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...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1492804/full |
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| author | Lingjing Zhang Lingjing Zhang Yanwei Wang Yuming Liu Zi Ye Zhaohui Li |
| author_facet | Lingjing Zhang Lingjing Zhang Yanwei Wang Yuming Liu Zi Ye Zhaohui Li |
| author_sort | Lingjing Zhang |
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| description | BackgroundAlzheimer’s 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.MethodsThe 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’s predictive performance. An online calculator was created using the “DynNom” and “rsconnect” functions.ResultsThrough LASSO regression and multivariate logistic regression analyses, six variables were identified and incorporated into the nomogram: age (OR: 1.097; 95%CI: 1.041–1.161; p < 0.001), years of education (OR: 0.333; 95%CI: 0.140–0.749; p = 0.010), diastolic blood pressure (OR: 0.949; 95%CI: 0.907–0.990; p = 0.019), short posterior ciliary artery flow rate (OR: 1.063; 95%CI: 1.008–1.132; p = 0.038), vertical cup-to-disc ratio (OR: 11.927; 95%CI: 1.059–155.308; p = 0.049), and peripapillary retinal nerve fiber layer thickness (inferior; OR: 0.979; 95%CI: 0.964–0.993; p = 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: 0.722–0.864) in the training dataset and 0.750 (95%CI: 0.627–0.858) in the external dataset. Calibration curve validation showed good agreement between predicted and ideal probabilities (p > 0.05, Brier score = 0.171). DCA indicated substantial net benefit across most threshold probabilities in both training and test datasets, supporting the nomogram’s clinical utility.ConclusionThrough 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. |
| format | Article |
| id | doaj-art-d556bb7d407f48d29a27ca86e461120e |
| institution | DOAJ |
| issn | 1663-4365 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj-art-d556bb7d407f48d29a27ca86e461120e2025-08-20T03:14:59ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652025-04-011710.3389/fnagi.2025.14928041492804Early identification of mild cognitive impairment: an innovative model using ocular biomarkersLingjing Zhang0Lingjing Zhang1Yanwei Wang2Yuming Liu3Zi Ye4Zhaohui Li5School of Medicine, Nankai University, Tianjin, ChinaDepartment of Ophthalmology, The General Hospital of the People’s Liberation Army, Beijing, ChinaDepartment of General Surgery, No. 926 Hospital, Joint Logistics Support Force of PLA, Kaiyuan, ChinaMedical School of Chinese PLA, Beijing, ChinaDepartment of Ophthalmology, The General Hospital of the People’s Liberation Army, Beijing, ChinaDepartment of Ophthalmology, The General Hospital of the People’s Liberation Army, Beijing, ChinaBackgroundAlzheimer’s 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.MethodsThe 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’s predictive performance. An online calculator was created using the “DynNom” and “rsconnect” functions.ResultsThrough LASSO regression and multivariate logistic regression analyses, six variables were identified and incorporated into the nomogram: age (OR: 1.097; 95%CI: 1.041–1.161; p < 0.001), years of education (OR: 0.333; 95%CI: 0.140–0.749; p = 0.010), diastolic blood pressure (OR: 0.949; 95%CI: 0.907–0.990; p = 0.019), short posterior ciliary artery flow rate (OR: 1.063; 95%CI: 1.008–1.132; p = 0.038), vertical cup-to-disc ratio (OR: 11.927; 95%CI: 1.059–155.308; p = 0.049), and peripapillary retinal nerve fiber layer thickness (inferior; OR: 0.979; 95%CI: 0.964–0.993; p = 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: 0.722–0.864) in the training dataset and 0.750 (95%CI: 0.627–0.858) in the external dataset. Calibration curve validation showed good agreement between predicted and ideal probabilities (p > 0.05, Brier score = 0.171). DCA indicated substantial net benefit across most threshold probabilities in both training and test datasets, supporting the nomogram’s clinical utility.ConclusionThrough 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.https://www.frontiersin.org/articles/10.3389/fnagi.2025.1492804/fullmild cognitive impairmentocular biomarkersAlzheimer’s diseaseprediction modelweb-based calculator |
| spellingShingle | Lingjing Zhang Lingjing Zhang Yanwei Wang Yuming Liu Zi Ye Zhaohui Li Early identification of mild cognitive impairment: an innovative model using ocular biomarkers Frontiers in Aging Neuroscience mild cognitive impairment ocular biomarkers Alzheimer’s disease prediction model web-based calculator |
| title | Early identification of mild cognitive impairment: an innovative model using ocular biomarkers |
| title_full | Early identification of mild cognitive impairment: an innovative model using ocular biomarkers |
| title_fullStr | Early identification of mild cognitive impairment: an innovative model using ocular biomarkers |
| title_full_unstemmed | Early identification of mild cognitive impairment: an innovative model using ocular biomarkers |
| title_short | Early identification of mild cognitive impairment: an innovative model using ocular biomarkers |
| title_sort | early identification of mild cognitive impairment an innovative model using ocular biomarkers |
| topic | mild cognitive impairment ocular biomarkers Alzheimer’s disease prediction model web-based calculator |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1492804/full |
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