Serum Metabolites Responding in a Dose-Dependent Manner to the Intake of a High-Fat Meal in Normal Weight Healthy Men Are Associated with Obesity

Different Diet Energy Levels Alter Body Condition, Glucolipid Metabolism, Fecal Microbiota and Metabolites in Adult Beagle Dogs

Diet energy is a key component of pet food, but it is usually ignored during pet food development and pet owners also have limited knowledge of its importance. This study aimed to explore the effect of diet energy on the body condition, glucolipid metabolism, fecal microbiota and metabolites of adult beagles and analyze the relation between diet and host and gut microbiota. Eighteen healthy adult neutered male beagles were selected and randomly divided into three groups. Diets were formulated with three metabolizable energy (ME) levels: the low-energy (Le) group consumed a diet of 13.88 MJ/kg ME; the medium-energy (Me) group consumed a diet of 15.04 MJ/kg ME; and the high-energy (He) group consumed a diet of 17.05 MJ/kg ME. Moreover, the protein content of all these three diets was 29%. The experiment lasted 10 weeks, with a two-week acclimation period and an eight-week test phase. Body weight, body condition score (BCS), muscle condition score (MCS) and body fat index (BFI) decreased in the Le group, and the changes in these factors in the Le group were significantly higher than in the other groups (p < 0.05). The serum glucose and lipid levels of the Le and He groups changed over time (p < 0.05), but those of the Me group were stable (p > 0.05). The fecal pH of the Le and He groups decreased at the end of the trial (p < 0.05) and we found that the profiles of short-chain fatty acids (SCFAs) and bile acids (BAs) changed greatly, especially secondary BAs (p < 0.05). As SCFAs and secondary BAs are metabolites of the gut microbiota, the fecal microbiota was also measured. Fecal 16S rRNA gene sequencing found that the Me group had higher α-diversity indices (p < 0.05). The Me group had notably higher levels of gut probiotics, such as Faecalibacterium prausnitzii, Bacteroides plebeius and Blautia producta (p < 0.05). The diet-host-fecal microbiota interactions were determined by network analysis, and fecal metabolites may help to determine the best physical condition of dogs, assisting pet food development. Overall, feeding dogs low- or high-energy diets was harmful for glucostasis and promoted the relative abundance of pathogenic bacteria in the gut, while a medium-energy diet maintained an ideal body condition. We concluded that dogs that are fed a low-energy diet for an extended period may become lean and lose muscle mass, but diets with low energy levels and 29% protein may not supply enough protein for dogs losing weight.

Investigating the Postprandial Metabolome after Challenge Tests to Assess Metabolic Flexibility and Dysregulations Associated with Cardiometabolic Diseases

This review focuses on the added value provided by a research strategy applying metabolomics analyses to assess phenotypic flexibility in response to different nutritional challenge tests in the framework of metabolic clinical studies. We discuss findings related to the Oral Glucose Tolerance Test (OGTT) and to mixed meals with varying fat contents and food matrix complexities. Overall, the use of challenge tests combined with metabolomics revealed subtle metabolic dysregulations exacerbated during the postprandial period when comparing healthy and at cardiometabolic risk subjects. In healthy subjects, consistent postprandial metabolic shifts driven by insulin action were reported (e.g., a switch from lipid to glucose oxidation for energy fueling) with similarities between OGTT and mixed meals, especially during the first hours following meal ingestion while differences appeared in a wider timeframe. In populations with expected reduced phenotypic flexibility, often associated with increased cardiometabolic risk, a blunted response on most key postprandial pathways was reported. We also discuss the most suitable statistical tools to analyze the dynamic alterations of the postprandial metabolome while accounting for complexity in study designs and data structure. Overall, the in-depth characterization of the postprandial metabolism and associated phenotypic flexibility appears highly promising for a better understanding of the onset of cardiometabolic diseases.