The aim of the present work was to study the association between different anthropometric parameters and insulin resistance (IR) in Spanish schoolchildren without a previous diagnosis. A total of 443 Spanish schoolchildren (9-11 years of age) were studied in this cross-sectional study. The anthropometric measurements collected were weight, height, body circumferences and skinfolds. Body mass index (BMI), waist/hip ratio, percentage body fat and fat-free body mass were determined. Overnight-fasted blood lipids, insulin and glucose levels were analysed, and estimation of IR, taking into account the homeostasis model assessment (HOMA), was calculated. The children with IR had higher serum triglycerides and insulin levels, were heavier and taller, and had a higher BMI, a larger waist circumference, a larger hip circumference, a larger waist/hip ratio and thicker bicipital and tricipital skinfolds than those who did not have IR. Age, sex, BMI and waist circumference explained 32.0% of the variance in the HOMA values; only sex, triglycerides and waist circumference independently influenced this variable. A 1-cm increase in waist circumference was associated with approximately a 3.8% increase in the mean HOMA value. The children with a waist circumference of over the p90 for their age and sex were at greater risk of showing IR as measured by the HOMA: odds ratio = 6.94 (2.01-23.91; P < 0.001). In conclusion, according to these results, waist circumference is the best anthropometric parameter associated with IR in children, and those with a waist circumference of over the p90 for their age and sex would appear to be at particular risk.

A bioactive compound is a product that provides health benefits when consumed, that may include anti-inflammatory, antioxidant activity, ROS and RNS scavenging, specific cytotoxicity, among others. These properties may aid in the prevention and/or mitigation of some chronic diseases 4. Curcumin, known as the Indian gold , has been used to alleviate arrhythmia, and as an aid in the treatment of various illnesses including Alzheimer s, asthma, liver disease, and also as antioxidant, anti-inflammatory, cardioprotective, anticarcinogenic and chemopreventive agent 5 7. Curcumin has been reported to interfere in several mechanisms that control inflammatory responses, among which are: modulation of arachidonic acid metabolism 8 , inhibition of cyclooxygenase 2 COX-2 and lipoxygenase LOX , two enzymes involved in inflammation. COX-2 induces cyto-Abstract: Curcumin is a bioactive compound with proven antioxidant and anti-inflammatory activities, but has low water solubility and dermal absorption. The inflammatory process is considered as the biological response to damage induced by various stimuli. If this process fails to self-regulate, it becomes a potential risk of cancer. The objective of this work was to evaluate the anti-inflammatory activity of curcumin administered to mice with induced atrial edema using two topical vehicles: organogels and O/W-type nanogels at pH 7, Organogels and O/W-type nanogels at pH 7 were prepared, characterized and the antiinflammatory activity was assessed. A histopathological analysis of mouse ears was performed and two gel formulations were selected. Thermograms of organogels indicated that increasing the gelling agent improved the stability of the system. Deformation sweeps confirmed a viscoelastic behavior characteristic of gels in both systems. During the anti-inflammatory activity evaluations, the nanogels demonstrated greater activity (61.8 %) than organogels; Diclofenac ® (2-(2,6-dichloranilino) phenylacetic acid), used as a control medication achieved the highest inhibition (85.4%); however, the drug produced the death of 2 (40%) of the study subjects caused by secondary adverse events. Histopathological analysis confirmed the data.

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This study aimed to determine the body composition profile of candidates applying for a Physical Education and Sports major. 327 young adults (F: 87, M: 240) participated in this cross-sectional study. Nutritional status and body composition analysis were performed, and the profiles were generated using an unsupervised machine learning algorithm. Body mass index (BMI), percentage of fat mass (%FM), percentage of muscle mass (%MM), metabolic age (MA), basal metabolic rate (BMR), and visceral fat level (VFL) were used as input variables. BMI values were normal-weight although VFL was significantly higher in men (<0.001; η2 = 0.104). MA was positively correlated with BMR (0.81 [0.77, 0.85]; p < 0.01), BMI (0.87 [0.84, 0.90]; p < 0.01), and VFL (0.77 [0.72, 0.81]; p < 0.01). The hierarchical clustering analysis revealed two significantly different age-independent profiles: Cluster 1 (n = 265), applicants of both sexes that were shorter, lighter, with lower adiposity and higher lean mass; and, Cluster 2 (n = 62), a group of overweight male applicants with higher VFL, taller, with lower %MM and estimated energy expended at rest. We identified two profiles that might help universities, counselors and teachers/lecturers to identify applicants in which is necessary to increase physical activity levels and improve dietary habits.