Estimating metabolite networks subject to dietary preferences and lifestyle

• Published in: Metabolomics Vol. 21; no. 5; p. 105
• Main Authors: Bartzis, Georgios, Peeters, Carel F. W., Uh, Hae-Won, Houwing-Duistermaat, Jeanine J., van Eeuwijk, Fred A.
• Format: Journal Article
• Language: English
• Published: New York Springer US 11.08.2025; Springer Nature B.V
• Subjects: Adult; Age; Biochemistry; Biomedical and Life Sciences; Biomedicine; Cell Biology; Developmental Biology; Diet; Discriminant analysis; Epidemiology; Female; Food; Genetic diversity; Genetics; Humans; Life Sciences; Life Style; Lifestyles; Male; Metabolic Networks and Pathways; Metabolic syndrome; Metabolites; Metabolome; Metabolomics; Metabolomics - methods; Middle Aged; Molecular Medicine; Nutrition research; Original; Original Article; Phenotypes; Phenotypic variations; Polymorphism, Single Nucleotide; Questionnaires; Single-nucleotide polymorphism; Metabolites; Network reconstruction; Repeated measures
• ISSN: 1573-3890; 1573-3882; 1573-3890
• DOI: 10.1007/s11306-025-02296-2

Introduction The metabolome is an intermediate between DNA variation and clinical phenotypes. Metabolomics have been widely used in biomedical studies for reflecting physiological changes in response to variation coming from various sources, such as diet, environment, time, and lifestyle. While lifestyle factors contribute a considerable part of the metabolic variation, current human studies lack information estimating lifestyle, mainly because it is not strictly defined. Objective In this work, metabolite concentrations are measured at two time points (2007 and 2014). Additionally, SNP data together with self-reports on dietary behavior. By having measurements over time, as well as all main sources of metabolic variation (diet, genetics), both time-effects and lifestyle-effects can be estimated. Since lifestyle and time effects can be estimated under this setting, we are interested in identifying metabolites sharing similar relationships to diet and lifestyle, using network analysis. Methods The correlation between repeated measurements is modeled using a random intercepts linear mixed model, with dietary preferences, genetics, and time as fixed effects. The random intercepts can be defined as the lifestyle, and represent the part of the metabolic variation which is not due to diet, genetics, and time and is subject-specific. The part of every metabolite relevant to diet and lifestyle instead of the original values is used as input values to network estimation methods. Conclusions This work demonstrates how correcting for several sources of metabolic variation, allows us to look for residual variation and build networks with meaningful metabolite groups sharing similar association to diet and lifestyle.