ArticleJournal of proteome research2018
Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data.
Article in Journal of proteome research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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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.
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Who cites it
27 citing papers in PubMed, 57 citations in OpenAlex.
- Upper-gastrointestinal tract metabolite profile regulates glycaemic and satiety responses to meals with contrasting structure: a pilot study.Nature metabolism · 2025Trial
- Alternative antibiotic regimens improve palatability and welfare in mice for gut bacterial depletion.Lab animal · 2026Article
- A gut microbiome-kidney-heart axis predictive of future cardiovascular diseases.Nature communications · 2026Article
- Software Application Profile: TriplotGUI, a molecular epidemiology toolbox for investigating associations between exposures, omics, and outcomes.International journal of epidemiology · 2025Article
- Exploring uric acid as a biomarker in lipedema and lymphedema: A metabolomics study with prospective validation.Obesity medicine · 2025Article
- A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks.NPJ systems biology and applications · 2024Article
- Altered serum metabolome as an indicator of paraneoplasia or concomitant cancer in patients with rheumatic disease.Annals of the rheumatic diseases · 2024Article
- Adjusting for covariates and assessing modeling fitness in machine learning using MUVR2.Bioinformatics advances · 2024Article
- Metabolomic-Based Approaches for Endometrial Cancer Diagnosis and Prognosis: A Review.Cancers · 2023Review
- Pathobionts in the tumour microbiota predict survival following resection for colorectal cancer.Microbiome · 2023Observational
- Demographic, Health and Lifestyle Factors Associated with the Metabolome in Older Women.Metabolites · 2023Article
- Controlling the confounding effect of metabolic gene expression to identify actual metabolite targets in microsatellite instability cancers.Human genomics · 2023Article
- Characterizing the metabolic effects of the selective inhibition of gut microbial β-glucuronidases in mice.Scientific reports · 2022Article
- Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension.Metabolites · 2022Article
- Multi-omic phenotyping reveals host-microbe responses to bariatric surgery, glycaemic control and obesity.Communications medicine · 2022Article
- Article
- Statistical analysis in metabolic phenotyping.Nature protocols · 2021Article
- Metabolomic Profile in Venous Thromboembolism (VTE).Metabolites · 2021Review
- Multiomic features associated with mucosal healing and inflammation in paediatric Crohn's disease.Alimentary pharmacology & therapeutics · 2020Article
- Longitudinal Multi-omics Reveals Subset-Specific Mechanisms Underlying Irritable Bowel Syndrome.Cell · 2020Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
Metabolism is altered by genetics, diet, disease status, environment, and many other factors. Modeling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data, we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-adjusted projections to latent structures (CA-PLS) are exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS, we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques.
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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.