title: Multilevel pharmacokinetics-driven modeling of metabolomics data.
abstract: BACKGROUND
  Multilevel 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.
  
  
  OBJECTIVE
  In 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.
  
  
  METHODS
  A 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.
  
  
  RESULTS
  Cancer 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 individuala posterioriprediction of patient classification as area under the ROC with 5th and 95th percentile was 0.57(0.5-0.67) with sensitivity and specificity of 0.59(0.42-0.76) and 0.57(0.45-0.7), respectively suggesting limited usefulness of 13 nucleosides/creatinine urine concentration measurements in predicting disease in this population.
  
  
  CONCLUSIONS
  Bayesian 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.
authors.affiliation: Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland.
authors.forename: Emilia
authors.initials: E
authors.lastname: Daghir-Wojtkowiak
authors.affiliation: Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland.
authors.forename: Paweł
authors.identifier: 0000-0002-2878-3161
authors.initials: P
authors.lastname: Wiczling
authors.affiliation: Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland.
authors.forename: Małgorzata
authors.initials: M
authors.lastname: Waszczuk-Jankowska
authors.affiliation: Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland.
authors.forename: Roman
authors.initials: R
authors.lastname: Kaliszan
authors.affiliation: Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland.
authors.forename: Michał Jan
authors.initials: MJ
authors.lastname: Markuszewski
keywords: Bayesian analysis; Cancer; Metabolomics; Methylthioadenosine; Multi-level modeling; Nucleosides; Pharmacokinetics