@inproceedings{01H25F0NXC74HTYJFAYVEY3GWQ,
  abstract     = {{The rising prevalence and treatment resistance of obesity urges further exploration of underlying metabolic processes. This is especially important at a young age, when metabolic and psychological development, are ongoing. The gastrointestinal metabolome contains a multitude of endogenous and environmental potentially disease-causative agents. The origins of specific compounds are mostly known, i.e., the gastrointestinal microbiome, food and drug intake, lifestyle, etc. The key drivers of most metabolites in relation to the pathophysiology of overweight and obesity remain however poorly understood. 
Using stool and saliva collected from the deeply characterized OPERA cohort (n=112, age 8-18, 54% boys, 30% overweight), UHPLC-HRMS-based metabolomics and lipidomics and 16s rDNA sequencing data were obtained. In parallel, food and drug intake, anthropometrics, lifestyle, psychological and clinical markers were gathered. Individual fecal metabolite and lipid levels were quantitively predicted based on microbial OTUs, using supervised training (random forest regressor). The top 50 prediction coefficients (R2) on the set of 21,435 features ranged from 0.26-0.48 and thus in the same range as Bar et al. (2020, Nature) for the serum metabolome. The salivary metabolome (9,044 features) was also significantly (FDR q<0.05) correlated with its corresponding microbial salivary OTUs with Spearman ρ up to |0.48|, in line with Asnicar et al. (2021, Nature Medicine). Key gastrointestinal metabolites were retained for univariate statistical analysis. As such it was demonstrated that Eikenella spp. correlated with isoleucyl-methionine (ρ=0.39) and substantially decreased in children with obesity as compared to healthy children. Similarly, Streptococcus spp. increased in psychologically stressed individuals, offering novel insights into a species linked with depressive symptoms. Other promising metabolite predictions and correlations based on dietary and drug intake, clinical, anthropometric, and psychological data will be presented.
Using the unique insights of the gastrointestinal metabolome as a foundation, we aim to develop personalized treatments using significantly predicted metabolites as efficacy markers.}},
  author       = {{De Graeve, Marilyn and Wijnant, Kathleen and Michels, Nathalie and De Henauw, Stefaan and Vanhaecke, Lynn}},
  booktitle    = {{Metabolomics Society, 19th Annual conference, Abstracts}},
  keywords     = {{metabolomics,saliva,stool,microbiome,obesity,machine learning}},
  language     = {{eng}},
  location     = {{Niagara Falls Canada}},
  pages        = {{1}},
  title        = {{Determining the main drivers of childhood overweight using the gastrointestinal metabolome of the deeply phenotyped OPERA cohort}},
  year         = {{2023}},
}