ReviewJournal of translational medicine2026
Multiomics: the intersection of personalized nutrition in cardiometabolic diseases.
Review in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Molecular Mechanisms Associated with Metabolic Dysfunction: Contributions of Nutritional Genomics.Metabolites · 2026Review
- Gut Microbiome-Hormone Interactions and Precision Fermentation in the Prevention of Early Cardiovascular Risk in Adolescents.International journal of molecular sciences · 2026Review
- Targeting the cardio-neuro axis through nutrition: inflammatory mechanisms linking cardiovascular and neurodegenerative diseases.Frontiers in nutrition · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiometabolic diseases are among the leading causes of increasing morbidity and mortality worldwide. However, current population-based dietary recommendations do not sufficiently account for biological differences between individuals and therefore do not have the same effect on everyone. The multiomic approach, which incorporates genomic, epigenomic, transcriptomic, proteomic, metabolomic, and microbiome data, facilitates more accurate classification of disease risk and selection of appropriate nutritional interventions by mapping food-disease relationships across different biological layers.
methodsThrough a narrative synthesis of the current literature, we focused on evidence from multiomic studies to assess their ability to guide personalized nutrition strategies based on individual genetic, metabolic, and microbiome characteristics in cardiometabolic diseases.
resultsRecent evidence indicates that metabolomic markers have been reported to provide predictive value in addition to classic risk indicators and to increase the predictive power of models when combined with genetic data. Microbiome research shows that glycemic and lipemic responses can be predicted using algorithms based on gut microbiota. Recent clinical studies show that personalized nutrition plans, which evaluate the microbiome and clinical characteristics together, improve continuous glucose monitoring-based glycemic control, glycated hemoglobin levels, and triglycerides more than the classic Mediterranean diet.
conclusionThis review summarizes the current multiomic evidence, discusses the methodological and practical challenges in this field, and highlights future priorities. The integration of digital biomarkers obtained from wearable technologies with multiomic systems and artificial intelligence-supported models, when developed in accordance with ethical and equitable access principles, has the potential to support the transition from the discovery phase to patient-centered clinical applications.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.