ReviewNature reviews. Endocrinology2026
Optimizing metabolic health through deep-phenotyping-based personalized nutrition.
Review in Nature reviews. Endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With rising rates of chronic diseases, obesity and metabolic disorders, there is an urgent need for more effective lifestyle interventions. An increasing recognition of how an individual's diet affects their disease susceptibility has highlighted the limitations of traditional one-size-fits-all dietary guidelines, which often fail to account for the considerable metabolic heterogeneity among individuals. Precision nutrition tailors dietary advice and nutritional prescriptions by considering a broad range of factors, including genetics, age, sex, phenotype, lifestyle, preferences, clinical history and other personal characteristics. These data are used to stratify people into subgroups based on biomarkers of metabolic variation, enabling improved estimation of dietary requirements and more effective dietary advice and interventions. This Review explores the potential of utilizing deep human phenotype data from large cohort studies and advances in omics technologies and wearables to provide personalized nutrition guidance and improve the prevention and management of metabolic disorders. It also discusses how these approaches might be integrated into broader clinical practice and identifies the challenges that must be overcome before precision nutrition can be widely adopted in clinical and public health settings.
Identifiers
42844422What OpenQuestion 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 OpenQuestion graph.