ABSTRACT Objective: Thus, the aim of this study was to compare if higher or smaller fibronectin type 3 domain-containing protein 5 (FNDC5)/irisin levels are associated with inflammatory and metabolic markers, caloric/macronutrient intake, physical fitness and type 2 diabetes mellitus (T2DM) risk in obese middle-aged men, and also to correlate all variables analyzed with FNDC5/irisin. Subjects and methods: On the basis of a cluster study, middle-aged obese men (IMC: 31.01 ± 1.64 kg/m2) were divided into groups of higher and smaller levels of FNDC5/irisin. The levels of leptin, resistin, adiponectin, tumor necrosis factor alpha (TNFα), interleukin 6 and 10 (IL6, IL10), lipopolysaccharide (LPS), glucose, insulin, glycated hemoglobin, insulin resistance and sensibility, lipid profile, risk of T2DM development, body composition, rest energy expenditure, caloric/macronutrient intake and physical fitness were measured. Results: The higher FNDC5/ irisin group presented improved insulin sensibility (homeostasis model assessment - sensibility (HOMA-S) (p = 0.01) and QUICKI index (p < 0.01)), insulin (p = 0.02) and triglyceride levels (p = 0.01), lower insulin resistance (homeostasis model assessment - insulin resistance (HOMA-IR) (p = 0.01), triglycerides/glucose (TYG index) (p = 0.02), neck circumference (p = 0.02), risk of T2DM development (p = 0.02), tendency to decrease serum resistin (p = 0.08) and significant lower LPS levels (p = 0.02). Inverse correlations between FNDC5/irisin and body weight (r −0.46, p = 0.04), neck circumference (r −0.51, p = 0.02), free fat mass (r −0.49, p = 0.02), triglycerides (r −0.43, p = 0.05) and risk of developing T2DM (r −0.61, p = 0.04) were observed. Conclusions: These results suggest that higher FNDC5/irisin levels in obese middle-aged men are related to a better metabolic profile and lower risk of T2DM development and serum LPS, a potential inducer of insulin resistance.

Abstract Aim This review aimed to provide an overview of the publications using metabolomics in research with physical exercises and to demonstrate how researchers have been applying this approach. Methods A systematic search in the databases Web of Science, SCOPUS and PubMed was performed, with the key words: "metabolomics" OR "metabonomics" and with "metabolomics" OR "metabonomics" AND "exercise" in the title or abstract of the articles. The search period was from 2000 to 2016. Forty-four original articles were selected. The studies found were separated into four categories: metabolic responses to physical exercise, supplementation and physical exercise, sports performance, and physical exercise related to diseases. Results It was possible to observe the exponential growth of the use of this approach in Sports and Health Sciences, and the four sub-fields towards which these researches involving exercise are directed, enabling a more comprehensive characterization of different metabolic profiles, as well as their study for identifying new biomarkers related to physical exercise. Conclusions The possibilities of using metabolomics approach are increasing in the fields of Health Sciences, Sports, and Physical Activity. The experimental design of the study is essential to take advantage of this tool and be able to answer questions in the metabolism comprehension.