{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T03:02:24Z","timestamp":1782788544368,"version":"3.54.5"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"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. Med."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>The aim of this study is to develop and validate a novel multivariable prediction model capable of accurately estimating the probability of cataract development, utilizing parameters such as blood biochemical markers and age.<\/jats:p><\/jats:sec><jats:sec><jats:title>Design<\/jats:title><jats:p>This population-based cross-sectional study comprised 9,566 participants drawn from the National Health and Nutrition Examination Survey (NHANES) across the 2005\u20132008 cycles.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Demographic information and laboratory test results from the patients were collected and analyzed using LASSO regression and multivariate logistic regression to accurately capture the influence of biochemical indicators on the outcomes. The SHAP (Shapley Additive Explanations) scale was employed to assess the importance of each clinical feature, excluding age. A multivariate logistic regression model was then developed and visualized as a nomogram. To assess the model\u2019s performance, its discrimination, calibration, and clinical utility were evaluated using receiver operating characteristic (ROC) curves, 10-fold cross-validation, Hosmer-Lemeshow calibration curves, and decision curve analysis (DCA), respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Logistic regression analysis identified age, erythrocyte folate (nmol\/L), blood glucose (mmol\/L), and blood urea nitrogen (mmol\/L) as independent risk factors for cataract, and these variables were incorporated into a multivariate logistic regression-based nomogram for cataract risk prediction. The area under the receiver operating characteristic (ROC) curve (AUC) for cataract risk prediction was 0.917 (95% CI: 0.9067\u20130.9273) in the training cohort, and 0.9148 (95% CI: 0.8979\u20130.9316) in the validation cohort. The Hosmer-Lemeshow calibration curve demonstrated a good fit, indicating strong model calibration. Ten-fold cross-validation confirmed the logistic regression model\u2019s robust predictive performance and stability during internal validation. Decision curve analysis (DCA) demonstrated that the nomogram prediction model provided greater clinical benefit for predicting cataract risk when the patient\u2019s threshold probability ranged from 0.10 to 0.90.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study identified blood urea nitrogen (mmol\/L), serum glucose (mmol\/L), and erythrocyte folate (mmol\/L) as significant risk factors for cataract. A risk prediction model was developed, demonstrating strong predictive accuracy and clinical utility, offering clinicians a reliable tool for early and effective diagnosis. Cataract development may be delayed by reducing levels of blood urea nitrogen, serum glucose, and erythrocyte folate through lifestyle improvements and dietary modifications.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2024.1452756","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T04:39:30Z","timestamp":1729485570000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Development and construction of a cataract risk prediction model based on biochemical indices: the National Health and Nutrition Examination Survey, 2005\u20132008"],"prefix":"10.3389","volume":"11","author":[{"given":"Guoqing","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang-Long","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"e144","DOI":"10.1016\/s2214-109x(20)30489-7","article-title":"Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the right to sight: an analysis for the global burden of disease study","volume":"9","author":"Steinmetz","year":"2021","journal-title":"Lancet Glob Health"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"010804","DOI":"10.7189\/jogh.08.010804","article-title":"The national and subnational prevalence of cataract and cataract blindness in China: a systematic review and meta-analysis","volume":"8","author":"Song","year":"2018","journal-title":"J Glob Health"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"477","DOI":"10.4103\/ijo.IJO_1127_17","article-title":"The prevalence and risk factors for cataract in rural and urban India","volume":"67","author":"Singh","year":"2019","journal-title":"Indian J Ophthalmol"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.2147\/opth.S355191","article-title":"Prevalence and 11-year incidence of cataract and cataract surgery and the effects of socio-demographic and lifestyle factors","volume":"16","author":"Purola","year":"2022","journal-title":"Clin Ophthalmol"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1111\/ceo.13430","article-title":"Longitudinal changes in global cataract surgery rate inequality and associations with socioeconomic indices","volume":"47","author":"Yan","year":"2019","journal-title":"Clin Experiment Ophthalmol"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1016\/s0140-6736(17)30544-5","article-title":"Cataracts","volume":"390","author":"Liu","year":"2017","journal-title":"Lancet"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1038\/s41433-020-0806-3","article-title":"Global and regional prevalence of age-related cataract: a comprehensive systematic review and meta-analysis","volume":"34","author":"Hashemi","year":"2020","journal-title":"Eye (Lond)"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1146\/annurev.nu.10.070190.001313","article-title":"Nutritional factors in cataract","volume":"10","author":"Bunce","year":"1990","journal-title":"Annu Rev Nutr"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1001\/archopht.1995.01100090039020","article-title":"Biochemical factors in the lens opacities. 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