ArticleMetabolomics : Official journal of the Metabolomic Society2017
Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis.
Article in Metabolomics : Official journal of the Metabolomic Society, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Article
- Diagnostic utility of clinicodemographic, biochemical and metabolite variables to identify viable pregnancies in a symptomatic cohort during early gestation.Scientific reports · 2024Article
- Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Demographic, Health and Lifestyle Factors Associated with the Metabolome in Older Women.Metabolites · 2023Article
- Exploration of Blood Metabolite Signatures of Colorectal Cancer and Polyposis through Integrated Statistical and Network Analysis.Metabolites · 2023Article
- NMR Metabolomics Methods for Investigating Disease.Analytical chemistry · 2023Review
- Towards equations for estimating glomerular filtration rate without demographic characteristics.Clinical and translational medicine · 2022Article
- Exopolysaccharide Produced byFoods (Basel, Switzerland) · 2021Article
- Gender differences in the longitudinal association between husbands' and wives' depressive symptoms among Korean older adults: the moderating effects of the spousal relationship.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2021Article
- Symptomatology and Serum Nuclear Magnetic Resonance Metabolomics; Do They Predict Endometriosis in Fertile Women Undergoing Laparoscopic Sterilisation? A Prospective Cross-sectional Study.Reproductive sciences (Thousand Oaks, Calif.) · 2021Article
- Integrative analysis of time course metabolic data and biomarker discovery.BMC bioinformatics · 2020Article
- Changes in lipids composition and metabolism in colorectal cancer: a review.Lipids in health and disease · 2019Review
- Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture.G3 (Bethesda, Md.) · 2018Article
- NMR-based metabolomics: Where are we now and where are we going?Progress in nuclear magnetic resonance spectroscopyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Introduction: Metabolomics technologies enable the identification of putative biomarkers for numerous diseases; however, the influence of confounding factors on metabolite levels poses a major challenge in moving forward with such metabolites for pre-clinical or clinical applications. Objectives: To address this challenge, we analyzed metabolomics data from a colorectal cancer (CRC) study, and used seemingly unrelated regression (SUR) to account for the effects of confounding factors including gender, BMI, age, alcohol use, and smoking. Methods: A SUR model based on 113 serum metabolites quantified using targeted mass spectrometry, identified 20 metabolites that differentiated CRC patients (n = 36), patients with polyp (n = 39), and healthy subjects (n = 83). Models built using different groups of biologically related metabolites achieved improved differentiation and were significant for 26 out of 29 groups. Furthermore, the networks of correlated metabolites constructed for all groups of metabolites using the ParCorA algorithm, before or after application of the SUR model, showed significant alterations for CRC and polyp patients relative to healthy controls. Results: The results showed that demographic covariates, such as gender, BMI, BMI Conclusion: These results not only provide new insights into addressing the major issue of confounding effects in metabolomics analysis, but also shed light on issues related to establishing reliable biomarkers and the biological connections between them in a complex disease.
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.