Abstract

Although the composition of the human blood metabolome is influenced both by the health status of the organism and its dietary behavior, the interaction between these two factors has been poorly characterized. This study makes use of a previously published randomized controlled crossover acute intervention to investigate whether the blood metabolome of 15 healthy normal weight (NW) and 17 obese (OB) men having ingested three doses (500, 1000, 1500 kcal) of a high-fat (HF) meal can be used to identify metabolites differentiating these two groups. Among the 1024 features showing a postprandial response, measured between 0 h and 6 h, in the NW group, 135 were dose-dependent. Among these 135 features, 52 had fasting values that were significantly different between NW and OB men, and, strikingly, they were all significantly higher in OB men. A subset of the 52 features was identified as amino acids (e.g., branched-chain amino acids) and amino acid derivatives. As the fasting concentration of most of these metabolites has already been associated with metabolic dysfunction, we propose that challenging normal weight healthy subjects with increasing caloric doses of test meals might allow for the identification of new fasting markers associated with obesity.

Highlights

  • Evaluating the postprandial response to food or meal intake is a key strategy to identify and characterize intake [1,2,3] and effect [4,5,6] as well as susceptibility biomarkers [7,8,9]in nutritional studies [10]

  • As metabolic dysfunction is often mechanistically linked to nutrition, the coupling of metabolomics analyses to challenge tests based on the ingestion of nutrients, such as glucose (OGTT) and lipids (OLTT), foods, or meals, provides an additional sensitive way to evaluate the metabolic status of the human organism [25,26] as well as its response to dietary treatment [27], including weight loss programs [28]

  • Non-linearity in the dose response has already been demonstrated for individual dietary markers [28,31,32,33,34,35], the panel of dose-dependent features responding to the HF meal in the normal weight (NW) group showed a remarkable global saturation of the postprandial metabolome between 1000 and 1500 kcal

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Summary

Introduction

Evaluating the postprandial response to food or meal intake is a key strategy to identify and characterize intake [1,2,3] and effect [4,5,6] as well as susceptibility biomarkers [7,8,9]. The response of clinical chemistry parameters in serum [13]tools andto more holistically investigate the impact of nutrition on the human organism and genes in blood cells [20] in NW and OB men to increasing caloric doses of an HF meal has its metabolic dysfunctions, such as type 2 diabetes [16], metabolic syndrome [17], and been previously reported. Lastand aimOBismen, motivated the hypothesis the postprandial serum metabolome can be used to identify markers that discriminate NW that metabolites increasing their iAUC in response to increasing caloric doses of the HF and OB men under fasting conditions This last aim is motivated by the hypothesis that meal challenge could potentially accumulate over time, in particular in subjects with metmetabolites increasing their iAUC in response to increasing caloric doses of the HF meal abolic disorders, such as potentially obesity, which are characterized a loss in flexibility challenge could accumulate over time, in by particular in metabolic subjects with metabolic [21,22,23]. This last aim is motivated by the hypothesis that meal challenge could potentially accumulate over time, in particular in subjects with metmetabolites increasing their iAUC in response to increasing caloric doses of the HF meal abolic disorders, such as potentially obesity, which are characterized a loss in flexibility challenge could accumulate over time, in by particular in metabolic subjects with metabolic [21,22,23]. disorders, such as obesity, which are characterized by a loss in metabolic flexibility [21,22,23]

Results
17 PEER RE
Discussion
Subjects
Study Design
Design of the crossover
Meal Composition
Untargeted Metabolomics with LC-MS
Data Processing and Statistical Analysis of Untargeted LC-MS Data
Measures of Amino Acids by GC-MS
Clinical Chemistry
Full Text
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