Using artificial intelligence to predict chronic diseases through diet and multi-omics data

introduction
Traditional approaches and limitations
How AI deciphers the link between diet and disease
Application to chronic diseases
Challenges and ethical considerations
conclusion
References
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This article describes how artificial intelligence integrates nutritional data, machine learning, and multi-omics to improve predictions of diet-disease relationships, emphasizing the need for validation, transparency, and clinical surveillance. It focuses on new clinical applications in chronic diseases, as well as methodological limitations in measurement, causality, and ethical implementation.

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introduction

Artificial intelligence (AI) refers to computer systems designed to perform tasks that require human intelligence. Machine learning (ML), on the other hand, is used to learn patterns from data and subsequently improve predictions without direct programming. AI and ML are widely used to analyze large population datasets and identify patterns that can quickly diagnose diseases.

Traditional approaches and limitations

Traditional studies often use self-report tools such as food frequency questionnaires (FFQ) to assess diet. Although these methods can evaluate studies in large populations, they have a number of limitations, such as imprecision and recall bias, which can lead to reporting variability, misinterpret diet-disease associations, and introduce random and systematic measurement errors, which can weaken or distort associations between dietary exposures and disease outcomes.^{2, 3}

Diversity in diets, genes, and lifestyles further complicates scientific research and makes results difficult to generalize to the public. Diet is a highly complex exposure consisting of thousands of foods consumed in different combinations over time, often with nonlinear and interactive effects that are not well captured by traditional regression-based approaches.^{2, 3} Importantly, many traditional epidemiological models assume linearity and independence of exposure, which may oversimplify real-world dietary patterns.³ These challenges highlight the need for advanced computational tools such as AI and ML that can process complex nutritional data and accurately elucidate relationships between diet and disease.^{2, 3}

How AI deciphers the link between diet and disease

ML techniques

AI can decipher complex diet-disease relationships primarily through ML techniques and effectively evaluate high-dimensional nutritional data beyond traditional statistical models. Supervised learning techniques such as random forests, support vector machines, and deep neural networks are widely used to predict disease risk and health outcomes due to food intake when combined with clinical and lifestyle changes. These approaches enable the modeling of nonlinear and nonadditive associations and can incorporate large datasets from electronic health records, wearable devices, and dietary tracking applications.^{2, 3} However, although these models improve predictive performance, they cannot essentially establish a causal relationship between diet and disease without appropriate study design and validation.³ By learning nonlinear associations between nutrients, foods, and physiological responses, these models can accurately calculate postprandial blood glucose levels, cardiometabolic risk markers, and obesity-related outcomes.^{2, 3}

Unsupervised learning techniques such as clustering, principal component analysis, and latent class analysis have also been used to identify underlying dietary patterns without predefined labels. These approaches are particularly useful for studying dietary patterns, where overall dietary patterns (such as Western or Mediterranean-style patterns) may more accurately predict disease risk than analysis of single nutrients. Unsupervised models are particularly valuable in pattern-based nutrition research showing that overall dietary patterns, rather than specific nutrients, can increase disease risk.^{2, 3}

Multi-omics integration

AI combines dietary data with genomics, metabolomics, proteomics, and gut microbiome profiles to better understand how diet drives disease. Multi-omics integration enables the identification of biomarkers such as branched-chain amino acids, lipid species, and microbiota-derived metabolites associated with future risk of type 2 diabetes and cardiovascular disease.⁴ Using ML, AI can analyze complex data to identify specific disease-related biomarkers, such as blood fats and gut metabolites, that can predict the risk of diabetes and heart-related diseases faster than traditional methods. By learning from large-scale multi-omics data, AI can be applied to create personalized nutritional plans for individuals, often within a structured clinical framework rather than as a standalone automated system.^1,4

How AI is transforming personalized nutrition for better health