Higher Imported Food Patterns Are Associated with Obesity and Severe Obesity in Tuvalu: A Latent Class Analysis

López-Gil, José Francisco; Wu, Stephanie M; Lee, Tai-Lin (Irene); Shih, Chih-Wei; Tausi, Selotia; Sosene, Vine; Maani, Pauke P; Tupulaga, Malo; Hsu, Yu-Tien; Chang, Chia-Rui; Shiau, Shi-Chian; Lo, Yuan-Hung; Wei, Chih-Fu; Lin, Po-Jen; Hershey, Maria Soledad

doi:10.1016/j.cdnut.2024.102080

Cited by 1 publication

(1 citation statement)

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“…Furthermore, our preprocessing method opens the door to more comprehensive analyses of dietary patterns and their health implications. By efficiently handling diverse and complex dietary data [ 36 , 40 , 41 ], we can explore deeper relationships between food intake and health outcomes across different populations. This capability allows for a broader understanding of how specific dietary components contribute to or mitigate the risk of health conditions such as obesity and dyslipidemia, providing a solid foundation for developing more effective public health guidelines and nutritional therapies.…”

Section: Discussionmentioning

confidence: 99%

An Explainable Prediction for Dietary-Related Diseases via Language Models

Choi,

Kim,

Kim

2024

Nutrients

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Our study harnesses the power of natural language processing (NLP) to explore the relationship between dietary patterns and metabolic health outcomes among Korean adults using data from the Seventh Korea National Health and Nutrition Examination Survey (KNHANES VII). Using Latent Dirichlet Allocation (LDA) analysis, we identified three distinct dietary patterns: “Traditional and Staple”, “Communal and Festive”, and “Westernized and Convenience-Oriented”. These patterns reflect the diversity of dietary preferences in Korea and reveal the cultural and social dimensions influencing eating habits and their potential implications for public health, particularly concerning obesity and metabolic disorders. Integrating NLP-based indices, including sentiment scores and the identified dietary patterns, into our predictive models significantly enhanced the accuracy of obesity and dyslipidemia predictions. This improvement was consistent across various machine learning techniques—XGBoost, LightGBM, and CatBoost—demonstrating the efficacy of NLP methodologies in refining disease prediction models. Our findings underscore the critical role of dietary patterns as indicators of metabolic diseases. The successful application of NLP techniques offers a novel approach to public health and nutritional epidemiology, providing a deeper understanding of the diet–disease nexus. This study contributes to the evolving field of personalized nutrition and emphasizes the potential of leveraging advanced computational tools to inform targeted nutritional interventions and public health strategies aimed at mitigating the prevalence of metabolic disorders in the Korean population.

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Section: Discussionmentioning

confidence: 99%