Cataracts are a major cause of visual impairment worldwide, particularly among older adults, with an increasing prevalence due to population aging. Surgery is the primary treatment; however, preventive strategies are crucial for reducing the disease burden. This study aimed to investigate dietary and health-related factors associated with cataract occurrence and develop a predictive model using machine learning. Data were derived from the Korea National Health and Nutrition Examination Survey 2015–2017. The study included 190 women aged 60–79 years: 124 with cataracts and 66 controls. Analyzed variables included sociodemographic, behavioral, chronic disease, and dietary intake factors. After data preprocessing, 4 machine learning algorithms: support vector machine (SVM), random forest (RF), eXtreme gradient boosting, and multilayer perceptron were used. Model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUROC) and precision-recall curves. Among the tested models, the SVM achieved the best performance under stratified 10-fold cross-validation, with 71% accuracy, 86% precision, 73% recall, 79% F1-score, 65% AUROC, and 81% AUPRC. According to our findings, the odds of having cataracts can be effectively predicted using dietary and health data without relying on specialized ophthalmic equipment. The proposed model demonstrates the potential of machine learning-based tools for early identification and prevention of cataracts. Future studies with larger and more diverse samples, as well as integrating additional data sources such as genomics and lifestyle factors, are warranted to refine predictive accuracy and enhance personalized nutrition-based interventions.
Cataracts are a major cause of vision impairment in older adults and pose a growing concern in aging societies. This study examined the association between dietary macronutrient intake and the odds of having cataracts among 1,619 Korean adults aged ≥ 60 years using data from the 2015–2017 Korea National Health and Nutrition Examination Survey. Cataracts were present in 51.8% of participants. Dietary intake was assessed via 24-hour recall and macronutrient intake was categorized by quartiles and energy ratios. After adjusting for covariates, individuals in the highest quartile of carbohydrate-to-energy intake (> 80%) had 41% higher odds of having cataracts (odds ratio [OR], 1.41; 95% confidence interval [CI], 0.99–2.01), with a significant trend (p for trend = 0.022). In contrast, the highest quartile of protein intake (Q3: 12%–15% energy) was associated with significantly lower odds of having cataracts in women (OR, 0.59; 95% CI, 0.40–0.88). Likewise, fat intake exceeding 18% of total energy was associated with reduced odds of having cataracts (OR, 0.69; 95% CI, 0.49–0.97). Saturated and monounsaturated fat intake also showed inverse associations with the odds of having cataracts. These results suggest that excessive carbohydrate intake, particularly when replacing fats and proteins, may increase the odds of having cataracts in older adults, especially among women. Dietary adjustments aimed at reducing the proportion of carbohydrates and increasing high-quality protein and fat intake may help prevent cataracts in aging populations. Further longitudinal studies are needed to clarify causal relationships and to inform nutritional guideline development.
Citations
First
Prev
