ArticleMetabolomics : Official journal of the Metabolomic Society2017
Multilevel pharmacokinetics-driven modeling of metabolomics data.
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 5 papers.
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Who cites it
5 citing papers in PubMed.
- Evaluation of Lung Cancer Patient Response to First-Line Chemotherapy by Integration of Tumor Core Biopsy Metabolomics with Multiscale Modeling.Annals of biomedical engineering · 2023Article
- Serum metabolites as early detection markers of non-muscle invasive bladder cancer in Chinese patients.Frontiers in oncology · 2023Article
- Modeling of Tumor Growth with Input from Patient-Specific Metabolomic Data.Annals of biomedical engineering · 2022Article
- Article
- Multi-level and hybrid modelling approaches for systems biology.Computational and structural biotechnology journal · 2017Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMultilevel modeling is a quantitative statistical method to investigate variability and relationships between variables of interest, taking into account population structure and dependencies. It can be used for prediction, data reduction and causal inference from experiments and observational studies allowing for more efficient elucidation of knowledge.
objectivesIn this study we introduced the concept of multilevel pharmacokinetics (PK)-driven modelling for large-sample, unbalanced and unadjusted metabolomics data comprising nucleoside and creatinine concentration measurements in urine of healthy and cancer patients.
methodsA Bayesian multilevel model was proposed to describe the nucleoside and creatinine concentration ratio considering age, sex and health status as covariates. The predictive performance of the proposed model was summarized via area under the ROC, sensitivity and specificity using external validation.
resultsCancer was associated with an increase in methylthioadenosine/creatinine excretion rate by a factor of 1.42 (1.09-2.03) which constituted the highest increase among all nucleosides. Age influenced nucleosides/creatinine excretion rates for all nucleosides in the same direction which was likely caused by a decrease in creatinine clearance with age. There was a small evidence of sex-related differences for methylthioadenosine. The individual
conclusionBayesian multilevel pharmacokinetics-driven modeling in metabolomics may be useful in understanding the data and may constitute a new tool for searching towards potential candidates of disease indicators.
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