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IntroductionObesity is a complex disease that predisposes individuals to cardiometabolic alterations. It leads to adipose tissue (AT) dysfunction, which triggers insulin resistance (IR). This suggests that people with obesity develop local IR first and systemic IR later. AT secretes extracellular vesicles, which may be physiopathologically associated with the development of IR. Our aim was to evaluate the effect of a high-fat diet on different parameters of adiposity in a rat model of early-stage obesity and to determine if these parameters are associated with markers of systemic IR. In addition, we sought to explore the relationship between fasting blood measures of IR (Triglycerides/High Density Lipoprotein-cholesterol [TAG/HDL-c] and Triglycerides-Glucose Index [TyG Index]) with the size of adipocyte-derived extracellular vesicles (adEV).MethodsWe used a model of diet-induced obesity for ten weeks in Wistar rats exposed to a high-fat diet. Final weight gain was analyzed by Dual X-ray absorptiometry. Visceral obesity was measured as epididymal AT weight. IR was evaluated with fasting TyG Index & TAG/HDL-c, and adEV were isolated from mature adipocytes on ceiling culture.ResultsIn the high-fat diet group, glucose and triglyceride blood concentrations were higher in comparison to the control group (Log2FC, 0.5 and 1.5 times higher, respectively). The values for TyG Index and adEV size were different between the control animals and the high-fat diet group. Multiple linear regression analyses showed that adEV size can be significantly associated with the TyG Index value, when controlling for epididymal AT weight.ConclusionOur results show that lipid and glucose metabolism, as well as the size and zeta potential of adEV are already altered in early-stage obesity and that adEV size can be significantly associated with liver and systemic IR, estimated by TyG Index.

Abstract Background Gestational diabetes mellitus (GDM) represents the main metabolic alteration during pregnancy. The available methods for diagnosing GDM identify women when the disease is established, and pancreatic beta-cell insufficiency has occurred.The present study aimed to generate an early prediction model (under 18 weeks of gestation) to identify those women who will later be diagnosed with GDM. Methods A cohort of 75 pregnant women was followed during gestation, of which 62 underwent normal term pregnancy and 13 were diagnosed with GDM. Targeted metabolomics was used to select serum biomarkers with predictive power to identify women who will later be diagnosed with GDM. Results Candidate metabolites were selected to generate an early identification model employing a criterion used when performing Random Forest decision tree analysis. A model composed of two short-chain acylcarnitines was generated: isovalerylcarnitine (C5) and tiglylcarnitine (C5:1). An analysis by ROC curves was performed to determine the classification performance of the acylcarnitines identified in the study, obtaining an area under the curve (AUC) of 0.934 (0.873–0.995, 95% CI). The model correctly classified all cases with GDM, while it misclassified ten controls as in the GDM group. An analysis was also carried out to establish the concentrations of the acylcarnitines for the identification of the GDM group, obtaining concentrations of C5 in a range of 0.015–0.25 μmol/L and of C5:1 with a range of 0.015–0.19 μmol/L. Conclusion Early pregnancy maternal metabolites can be used to screen and identify pregnant women who will later develop GDM. Regardless of their gestational body mass index, lipid metabolism is impaired even in the early stages of pregnancy in women who develop GDM.

Extracellular vesicles (EVs) are nanoparticles secreted by cells under physiological and pathological conditions, such as metabolic diseases. In this context, EVs are considered potential key mediators in the physiopathology of obesity. It has been reported that EVs derived from adipose tissue (ADEVs) contribute to the development of a local inflammatory response that leads to adipose tissue dysfunction. In addition, it has been proposed that EVs are associated with the onset and progression of several obesity-related metabolic diseases such as insulin resistance. In particular, characterizing the molecular fingerprint of obesity-related ADEVs can provide a bigger picture that better reflects metabolic adaptation though PI3K/Akt/mTOR. Hence, in this review we describe the possible crosstalk communication of ADEVs with metabolically active organs and the intracellular response in the insulin signaling pathway.

Introduction: Studies of obesity require the estimation of fat mass (FM) and fat-free mass (FFM); therefore it is important to validate methods that evaluate these measurements. Objective: We sought to compare two different bioelectrical impedance analysis systems (BIAs) to estimate FM and FFM using dual-energy X-ray absorptiometry (DXA) as reference. Methods: We used a cross-sectional design. We evaluated FM and FFM using DXA and two types of BIA equipment: a foot-foot system (FFS) and a hand-footsystem (HFS). We conducted paired analysis (paired t-test). We used Bland-Altman plots to assess the relationships between FM and FFMI, limits of agreement were constructed (CL). Results: A total of 175 female students (22.9 ± 2.2 years-old) participated in the study. The paired analysis showed significant differences between the mean value of body fat percentage (BF%) estimated by BIA equipment compared to DXA (FFS = 28.7%, HFS= 34.4% and DXA= 35.3%). The mean difference between the HFS and DXA of BF% was -0.96, ((CL -5.29, 7.20). For the FFS, the mean difference was -6.69, (CL -0.29, -13.09). The paired analysis revealed significant differences between the estimates of FFMI by BIA compared to DXA (FFS =16.29, HFS =14.95, DXA =14.18). The mean difference between HFS and DXA was 0.78, and (CL -2.27, 0.72) whereas the FFS mean difference was -2.11 (CL -3.73 -0.49). Conclusion: A different magnitude of bias was observed between the BIA equipment arrays. The HFS appears to be more reliable than the FFS used, particularly in obtaining FFMI in young women.

Background: Sarcopenia is a geriatric syndrome, which affects the functional status and mobility of individuals. Objective: To identify the prevalence of sarcopenia and undernutrition, and to assess the association between sarcopenia and mobility, and sarcopenia and basic activities of daily living (ADL) in a group of elderly women. Subjects and methods: A cross-sectional study was performed in patients attending a geriatric service at a government hospital in Mexico City. Sarcopenia was identified applying Baumgartner's equation by obtaining skeletal muscle mass index (SMI) and undernutrition was diagnosed using mininutritional assessment instrument (MNA). The SENECA questionnaire and Katz index were used to evaluate mobility and ADL, respectively. Statistical analysis: lineal regression models were constructed fitting SMI as a dependent variable and SENECA, and ADL scores as independent variables, adjusted for age. Results: 90 women with a mean age of 78.2 (± 6.8) were studied. Undernutrition was identified in 15.5% of the patients. The prevalence of sarcopenia was 41.1%. Patients with sarcopenia presented a high prevalence of undernutrition (p < 0.001). Sarcopenia was associated with difficulty in climbing stairs (OR = 2.63, p = 0.03), adjusted for age. The mobility score was associated with sarcopenia, in the group without sarcopenia the mean score was 16.3, and with sarcopenia it was 18.3, (p = 0.04). Regarding ADL, 64.9% of the patients with intermediate independence and dependence had sarcopenia, while 35.1% with total independence presented sarcopenia (p < 0.001). Conclusions: The elder women with sarcopenia had a higher prevalence of undernutrition. Sarcopenia was associated with difficulties in mobility, particularly difficulties in climbing stairs.