ArticleNPJ systems biology and applications2017
Phenotype-driven identification of modules in a hierarchical map of multifluid metabolic correlations.
Article in NPJ systems biology and applications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 35 citations in OpenAlex.
- Procalcitonin metabolomics in the critically ill reveal relationships between inflammation intensity and energy utilization pathways.Scientific reports · 2021Trial
- Multiomic profiling of canine immune and metabolic aging in a cohort of young and senior beagles.Frontiers in immunology · 2026Article
- Fecal Microbiome and Metabolomic Profiles of Mixed-Fed Infants Are More Similar to Formula-Fed than Breastfed Infants.Microorganisms · 2025Article
- AutoFocus: a hierarchical framework to explore multi-omic disease associations spanning multiple scales of biomolecular interaction.Communications biology · 2024Article
- Serum and Urine Metabolites and Kidney Function.Journal of the American Society of Nephrology : JASN · 2024Article
- Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS.Molecular medicine (Cambridge, Mass.) · 2023Article
- Article
- Circulating N-formylmethionine and metabolic shift in critical illness: a multicohort metabolomics study.Critical care (London, England) · 2022Article
- Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS.PLoS pathogens · 2022Article
- Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS.medRxiv : the preprint server for health sciences · 2022Article
- Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS.medRxiv : the preprint server for health sciences · 2022Article
- Broad Metabolome Alterations Associated with the Intake of Oral Contraceptives Are Mediated by Cortisol in Premenopausal Women.Metabolites · 2021Article
- Metabolomic differences between critically Ill women and men.Scientific reports · 2021Article
- Multi-omics integration in biomedical research - A metabolomics-centric review.Analytica chimica acta · 2021Review
- Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference.Metabolites · 2020Article
- Metabolic signature associated with parameters of the complete blood count in apparently healthy individuals.Journal of cellular and molecular medicine · 2019Article
- Human Plasma Metabolomics in Age-Related Macular Degeneration: Meta-Analysis of Two Cohorts.Metabolites · 2019Article
- Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective.Metabolites · 2019Review
- MoDentify: phenotype-driven module identification in metabolomics networks at different resolutions.Bioinformatics (Oxford, England) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of phenotype-driven network modules in complex, multifluid metabolomics data poses a considerable challenge for statistical analysis and result interpretation. This is the case for phenotypes with only few associations ('sparse' effects), but, in particular, for phenotypes with a large number of metabolite associations ('dense' effects). Herein, we postulate that examining the data at different layers of resolution, from metabolites to pathways, will facilitate the interpretation of modules for both the sparse and the dense cases. We propose an approach for the phenotype-driven identification of modules on multifluid networks based on untargeted metabolomics data of plasma, urine, and saliva samples from the German Study of Health in Pomerania (SHIP-TREND) study. We generated a hierarchical, multifluid map of metabolism covering both metabolite and pathway associations using Gaussian graphical models. First, this map facilitates a fundamental understanding of metabolism within and across fluids for our study, and can serve as a valuable and downloadable resource. Second, based on this map, we then present an algorithm to identify regulated modules that associate with factors such as gender and insulin-like growth factor I (IGF-I) as examples of traits with dense and sparse associations, respectively. We found IGF-I to associate at the rather fine-grained metabolite level, while gender shows well-interpretable associations at pathway level. Our results confirm that a holistic and interpretable view of metabolic changes associated with a phenotype can only be obtained if different layers of metabolic resolution from multiple body fluids are considered.
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.