Digestive diseases, which encompass a broad range of conditions that affect the gastrointestinal tract, are a common and significant source of morbidity and mortality worldwide. However, despite their prevalence, digestive diseases have become a growing global burden. Although numerous studies have tried to identify novel pathogenic factors associated with digestive diseases, including lifestyle factors, environmental, metabolic, genetic, and immune response factors, it remains unclear whether these risk factors, which often coexist within individuals, interact with each other and exhibit synergistic effects. Furthermore, while diet is a recognized determinant of digestive diseases such as gastrointestinal tract and liver health, non-dietary factors such as stress, medication, sleep patterns, and socioeconomic status play less understood roles. Additionally, metabolomics and proteomics offer insights into biochemical processes and pathways affected by these diseases, while genetic risk scores enable the identification of individuals at higher risk. This study will employ a comprehensive analytical approach, including longitudinal analyses, multivariate regression models, and machine learning techniques to analyze complex datasets. By integrating dietary assessments, non-dietary factors, metabolomic data, and genetic risk scores, we will identify exposures of interest and key predictors and create robust models for digestive disease risk assessment. The present study is expected to help identify the effects of risk factors on the incidence and mortality of digestive diseases and will provide a holistic view of disease etiology, considering the multifaceted nature of these influences.
