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Updated: May 26, 2026

The MPLEx Protocol for Multi-omic Analyses of Soil Samples
Published on: May 30, 2018
Molecular basis of precision nutrition: Food components, microbiome-derived metabolites, and multi-omics modeling
Souvia Rahimah1, Trina Ekawati Tallei2,3, Maghfirah Savitri4
1Department of Food Industrial Technology, Faculty of Agroindustrial Technology, Universitas Padjadjaran, Jatinangor, West Java, Indonesia.
Abstract:
Metabolic disorders, including obesity, type 2 diabetes, metabolic syndrome, and fatty liver disease, reflect multifactorial interactions among diet, host genetics, the environment, and the gut microbiome. However, conventional population-level dietary guidance often fails to capture the marked interindividual variability in metabolic responses to identical foods and nutrients. Precision nutrition has therefore emerged as an integrative paradigm that combines genomics, epigenomics regulation, microRNA-mediated control, and microbiome profiling to refine dietary recommendations, with a growing emphasis on targeted functional food-based strategies for metabolic health. This narrative review synthesizes the mechanistic foundations of precision nutrition, emphasizing how functional foods and their bioactive constituents engage nutrient-sensing and regulatory pathways that connect dietary exposures to gene regulation and downstream metabolic phenotypes. We summarize evidence for diet-gene interactions at key metabolic loci, epigenetic programming shaped by early-life nutrition, and diet-responsive microRNAs as candidate biomarkers of nutritional response. We further examine microbiome-derived signaling, specifically short-chain fatty acids and bile-acid metabolism within the gut-liver axis, as a major route by which functional dietary components can influence host pathways and condition metabolic outcomes. We highlight insights from large cohorts and controlled metabolic profiling studies and discuss enabling methodological advances, including multi-omics integration, causal inference, and machine learning models for response prediction. Key limitations remain, notably incomplete reproducibility, heterogeneity in exposure and outcome measurements, data governance challenges, and barriers to clinical implementation. In summary, precision nutrition provides a biologically grounded framework for personalized functional-food and dietary strategies, but robust validation and responsible translation are required before routine adoption.
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