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This systematic review examines the role of molecular epidemiology in identifying risk factors for chronic diseases, focusing on the integration of genomic, metabolomic, and environmental data. The review analyzes literature from 2015-2025 to understand how molecular epidemiology bridges traditional epidemiology and molecular biology. It highlights the use of multiomic profiling and machine learning to improve risk prediction and classify molecular disease subtypes, ultimately contributing to precision medicine.
Molecular epidemiology, through multiomic profiling and machine learning, enhances risk prediction accuracy and enables the classification of molecular disease subtypes in chronic diseases.
ABSTRACT This study aims to provide a critical review of the role of molecular epidemiology in identifying chronic disease risk factors, emphasizing how molecular integration enhances the precision and validity of epidemiological analyses. Employing a qualitative descriptive approach through a systematic literature review, this research collected and analyzed data from peer-reviewed scientific articles, books, and official reports published between 2015 and 2025. Data were examined through document analysis, thematic coding, and inductive synthesis, allowing the identification of core themes and conceptual relationships within the existing body of knowledge. The findings reveal that molecular epidemiology bridges the gap between traditional population-based approaches and molecular biology by integrating genomic, metabolomic, and environmental data to uncover the biological mechanisms underlying chronic diseases such as cancer, diabetes, and cardiovascular disorders. Furthermore, multiomic profiling and machine learning models have improved risk prediction accuracy, clarified gene–environment interactions, and enabled the classification of molecular disease subtypes. However, challenges remain in biomarker validation, data standardization, and clinical translation. This study concludes that molecular epidemiology contributes significantly to the advancement of precision medicine by promoting more personalized prevention and intervention strategies. Its theoretical and practical implications extend to public health, data science, and biomedical research, underscoring the need for interdisciplinary collaboration and equitable access to molecular technologies in global health contexts. Keywords: molecular epidemiology, chronic diseases, gene–environment interaction, multiomics, precision medicine.
