Interrelationships Among Changes in Leptin, Insulin, Cortisol and Growth Hormone and Weight Status in Youth

Objective: While acute alterations in leptin, insulin, cortisol and growth hormone (GH) levels have been reported in children following weight change interventions, little is known about natural hormonal changes as children grow and how these changes are affected b

The Relationship between Changes in Weight Status and Insulin Resistance in Youth

This study examined changes in insulin resistance (IR) in 120 youth over two years. IR was quantified via homeostatic model (HOMA-IR), and weight status changes were quantified via body mass index (BMI). When all participants were considered, the mean HOMA-IR and BMI increased 13.4% and 1.65 units, respectively. Change in BMI z-score and percent change in HOMA-IR were moderately associated (r = 0.39). Follow-up analyses were performed for the following weight groups: NN (normal at baseline and two years later), NO (normal to overweight), ON (overweight to normal), and OO (overweight at both points). The NO group had a greater change in HOMA-IR (+50%) compared to other groups: ON (−8%), NN (+2%), and OO (−0.1%) (P < .05). The association between changes in BMI z-score and HOMA-IR was r = 0.49 when only the NO and ON groups were included. These results reinforce the importance of preventing youth from becoming overweight to control IR

Metabolic characterization of overweight and obese adults

Traditional evaluations of metabolic health may overlook underlying dysfunction in individuals who show no signs of insulin resistance or dyslipidemia. The purpose of this study was to characterize metabolic health in overweight and obese adults using traditional and non-traditional metabolic variables. A secondary purpose was to evaluate differences between overweight/obese and male/female cohorts, respectively

Gut Microbiome and Metabolome Variations in Self-Identified Muscle Builders Who Report Using Protein Supplements

Muscle builders frequently consume protein supplements, but little is known about their effect on the gut microbiota. This study compared the gut microbiome and metabolome of self-identified muscle builders who did or did not report consuming a protein supplement. Twenty-two participants (14 males and 8 females) consumed a protein supplement (PS), and seventeen participants (12 males and 5 females) did not (No PS). Participants provided a fecal sample and completed a 24-h food recall (ASA24). The PS group consumed significantly more protein (118 &plusmn; 12 g No PS vs. 169 &plusmn; 18 g PS, p = 0.02). Fecal metabolome and microbiome were analyzed by using untargeted metabolomics and 16S rRNA gene sequencing, respectively. Metabolomic analysis identified distinct metabolic profiles driven by allantoin (VIP score = 2.85, PS 2.3-fold higher), a catabolic product of uric acid. High-protein diets contain large quantities of purines, which gut microbes degrade to uric acid and then allantoin. The bacteria order Lactobacillales was higher in the PS group (22.6 &plusmn; 49 No PS vs. 136.5 &plusmn; 38.1, PS (p = 0.007)), and this bacteria family facilitates purine absorption and uric acid decomposition. Bacterial genes associated with nucleotide metabolism pathways (p &lt; 0.001) were more highly expressed in the No PS group. Both fecal metagenomic and metabolomic analyses revealed that the PS group&rsquo;s higher protein intake impacted nitrogen metabolism, specifically altering nucleotide degradation

Trait Energy and Fatigue May Be Connected to Gut Bacteria among Young Physically Active Adults: An Exploratory Study

Recent scientific evidence suggests that traits energy and fatigue are two unique unipolar moods with distinct mental and physical components. This exploratory study investigated the correlation between mental energy (ME), mental fatigue (MF), physical energy (PE), physical fatigue (PF), and the gut microbiome. The four moods were assessed by survey, and the gut microbiome and metabolome were determined from 16 S rRNA analysis and untargeted metabolomics analysis, respectively. Twenty subjects who were 31 &plusmn; 5 y, physically active, and not obese (26.4 &plusmn; 4.4 kg/m2) participated. Bacteroidetes (45%), the most prominent phyla, was only negatively correlated with PF. The second most predominant and butyrate-producing phyla, Firmicutes (43%), had members that correlated with each trait. However, the bacteria Anaerostipes was positively correlated with ME (0.048, p = 0.032) and negatively with MF (&minus;0.532, p = 0.016) and PF (&minus;0.448, p = 0.048), respectively. Diet influences the gut microbiota composition, and only one food group, processed meat, was correlated with the four moods&mdash;positively with MF (0.538, p = 0.014) and PF (0.513, p = 0.021) and negatively with ME (&minus;0.790, p &lt; 0.001) and PE (&minus;0.478, p = 0.021). Only the Firmicutes genus Holdemania was correlated with processed meat (r = 0.488, p = 0.029). Distinct metabolic profiles were observed, yet these profiles were not significantly correlated with the traits. Study findings suggest that energy and fatigue are unique traits that could be defined by distinct bacterial communities not driven by diet. Larger studies are needed to confirm these exploratory findings