ReviewiMetaOmics2026
Progress of metabolomics-centric multi-omics research in medicine.
Review in iMetaOmics, 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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolomics reveals molecular changes at the metabolic level during biological processes. Recent advancements, particularly through integration with other omics and artificial intelligence (AI), are advancing metabolic data analysis and multi-omics interpretation. This review introduces a "metabolomics-centric" paradigm as a conceptual framework for multi-omics integration, positioning metabolic readouts as the functional anchor for interpreting genotype-phenotype relationships. It also chronicles the evolution and medical applications of integration technologies. Our discussion first traces the field's evolution from bulk to single-cell and spatial methodologies, next surveys integration strategies to uncover mechanistic insights, and finally examines how AI-driven multimodal data integration and AI-optimized metabolomics workflows tackle persistent biological and computational challenges. Looking forward, this review identifies the AI virtual cell as a pivotal future direction for metabolomics-based multi-omics integration, calling for increased attention to its development to simulate dynamic molecular phenotypic networks within a multi-omics context.
Indexed as
Identifiers
What 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.