Objective This study explored underlying metabolism‐related dysfunction by examining metabolomic profiles in adults categorized as lean, as having normal weight obesity (NWO), or as having overweight/obesity. Methods Participants (N = 179) had fasting plasma analyzed by liquid chromatography and high‐resolution mass spectrometry for high‐resolution metabolomics. Body composition was assessed by dual‐energy x‐ray absorptiometry. NWO was defined as BMI < 25 and body fat > 30% for women and > 23% for men. Differentiating metabolomic features were determined by using linear regression models and likelihood ratio tests with false discovery rate correction. Mummichog was used for pathway and network analyses. Results A total of 222 metabolites significantly differed between the groups at a false discovery rate of q = 0.2. Linoleic acid, β‐alanine, histidine, and aspartate/asparagine metabolism pathways were significantly enriched (all P < 0.01) by metabolites that were similarly upregulated in the NWO and overweight/obesity groups compared with the lean group. A module analysis linked branched‐chain amino acids and amino acid metabolites as elevated in the NWO and overweight/obesity groups compared with the lean group (all P < 0.05). Conclusions Metabolomic profiles of individuals with NWO reflected similar metabolic disruption as those of individuals with overweight/obesity. High‐resolution metabolomics may help identify people at risk for developing obesity‐related disease, despite normal BMI.

Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this work, we propose a novel prior model for Bayesian network marker selection in the generalized linear model (GLM) framework: the Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph Laplacian matrix to characterize the conditional dependence between neighboring markers accounting for the global network structure. Under mild conditions, we show the proposed model enjoys the posterior consistency with a diverging number of edges and nodes in the network. We also develop a Metropolisadjusted Langevin algorithm (MALA) for efficient posterior computation, which is scalable to large-scale networks. We illustrate the superiorities of the proposed method compared with existing alternatives via extensive simulation studies and an analysis of the breast cancer gene expression dataset in the Cancer Genome Atlas (TCGA).

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Untargeted metabolomics using high-resolution liquid chromatography–mass spectrometry (LC–MS) is becoming one of the major areas of high-throughput biology. Functional analysis, that is, analyzing the data based on metabolic pathways or the genome-scale metabolic network, is critical in feature selection and interpretation of metabolomics data. One of the main challenges in the functional analyses is the lack of the feature identity in the LC–MS data itself. By matching mass-to-charge ratio (m/z) values of the features to theoretical values derived from known metabolites, some features can be matched to one or more known metabolites. When multiple matchings occur, in most cases only one of the matchings can be true. At the same time, some known metabolites are missing in the measurements. Current network/pathway analysis methods ignore the uncertainty in metabolite identification and the missing observations, which could lead to errors in the selection of significant subnetworks/pathways. In this paper, we propose a flexible network feature selection framework that combines metabolomics data with the genome-scale metabolic network. The method adopts a sequential feature screening procedure and machine learning-based criteria to select important subnetworks and identify the optimal feature matching simultaneously. Simulation studies show that the proposed method has a much higher sensitivity than the commonly used maximal matching approach. For demonstration, we apply the method on a cohort of healthy subjects to detect subnetworks associated with the body mass index (BMI). The method identifies several subnetworks that are supported by the current literature, as well as detects some subnetworks with plausible new functional implications. The R code is available at http://web1.sph.emory.edu/users/tyu8/MSS.

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