GLP-1 receptor (GLP-1R) is widely located throughout the brain, but the precise molecular mechanisms mediating the actions of GLP-1 and its long-acting analogs on adipose tissue as well as the brain areas responsible for these interactions remain largely unknown. We found that central injection of a clinically used GLP-1R agonist, liraglutide, in mice stimulates brown adipose tissue (BAT) thermogenesis and adipocyte browning independent of nutrient intake. The mechanism controlling these actions is located in the hypothalamic ventromedial nucleus (VMH), and the activation of AMPK in this area is sufficient to blunt both central liraglutide-induced thermogenesis and adipocyte browning. The decreased body weight caused by the central injection of liraglutide in other hypothalamic sites was sufficiently explained by the suppression of food intake. In a longitudinal study involving obese type 2 diabetic patients treated for 1 year with GLP-1R agonists, both exenatide and liraglutide increased energy expenditure. Although the results do not exclude the possibility that extrahypothalamic areas are also modulating the effects of GLP-1R agonists, the data indicate that long-acting GLP-1R agonists influence body weight by regulating either food intake or energy expenditure through various hypothalamic sites and that these mechanisms might be clinically relevant.

Context: Body mass index (BMI) is widely used as a measure of overweight and obesity, but underestimates the prevalence of both conditions, defined as an excess of body fat. Objective: We assessed the degree of misclassification on the diagnosis of obesity using BMI as compared with direct body fat percentage (BF%) determination and compared the cardiovascular and metabolic risk of non-obese and obese BMI-classified subjects with similar BF%. Design: We performed a cross-sectional study. Subjects: A total of 6123 (924 lean, 1637 overweight and 3562 obese classified according to BMI) Caucasian subjects (69% females), aged 18-80 years. Methods: BMI, BF% determined by air displacement plethysmography and well-established blood markers of insulin sensitivity, lipid profile and cardiovascular risk were measured. Results: We found that 29% of subjects classified as lean and 80% of individuals classified as overweight according to BMI had a BF% within the obesity range. Importantly, the levels of cardiometabolic risk factors, such as C-reactive protein, were higher in lean and overweight BMI-classified subjects with BF% within the obesity range (men 4.3 ± 9.2, women 4.9 ± 19.5 mg l À1 ) as well as in obese BMI-classified individuals (men 4.2 ± 5.5, women 5.1 ± 13.2 mg l À1 ) compared with lean volunteers with normal body fat amounts (men 0.9 ± 0.5, women 2.1 ± 2.6 mg l À1 ; Po0.001 for both genders). Conclusion: Given the elevated concentrations of cardiometabolic risk factors reported herein in non-obese individuals according to BMI but obese based on body fat, the inclusion of body composition measurements together with morbidity evaluation in the routine medical practice both for the diagnosis and the decision-making for instauration of the most appropriate treatment of obesity is desirable.

OBJECTIVETo assess the predictive capacity of a recently described equation that we have termed CUN-BAE (Clínica Universidad de Navarra-Body Adiposity Estimator) based on BMI, sex, and age for estimating body fat percentage (BF%) and to study its clinical usefulness.RESEARCH DESIGN AND METHODSWe conducted a comparison study of the developed equation with many other anthropometric indices regarding its correlation with actual BF% in a large cohort of 6,510 white subjects from both sexes (67% female) representing a wide range of ages (18–80 years) and adiposity. Additionally, a validation study in a separate cohort (n = 1,149) and a further analysis of the clinical usefulness of this prediction equation regarding its association with cardiometabolic risk factors (n = 634) was carried out.RESULTSThe mean BF% in the cohort of 6,510 subjects determined by air displacement plethysmography was 39.9 ± 10.1%, and the mean BF% estimated by the CUN-BAE was 39.3 ± 8.9% (SE of the estimate, 4.66%). In this group, BF% calculated with the CUN-BAE showed the highest correlation with actual BF% (r = 0.89, P < 0.000001) compared with other anthropometric measures or BF% estimators. Similar agreement was found in the validation sample. Moreover, BF% estimated by the CUN-BAE exhibits, in general, better correlations with cardiometabolic risk factors than BMI as well as waist circumference in the subset of 634 subjects.CONCLUSIONSCUN-BAE is an easy-to-apply predictive equation that may be used as a first screening tool in clinical practice. Furthermore, our equation may be a good tool for identifying patients at cardiovascular and type 2 diabetes risk.

Obesity is the major risk factor for the development of prediabetes and type 2 diabetes. BMI is widely used as a surrogate measure of obesity, but underestimates the prevalence of obesity, defined as an excess of body fat. We assessed the presence of impaired glucose tolerance or impaired fasting glucose (both considered together as prediabetes) or type 2 diabetes in relation to the criteria used for the diagnosis of obesity using BMI as compared to body fat percentage (BF%). We performed a cross‐sectional study including 4,828 (587 lean, 1,320 overweight, and 2,921 obese classified according to BMI) white subjects (66% females), aged 18–80 years. BMI, BF% determined by air‐displacement plethysmography (ADP) and conventional blood markers of glucose metabolism and lipid profile were measured. We found a higher than expected number of subjects with prediabetes or type 2 diabetes in the obese category according to BF% when the sample was globally analyzed (P < 0.0001) and in the lean BMI‐classified subjects (P < 0.0001), but not in the overweight or obese‐classified individuals. Importantly, BF% was significantly higher in lean (by BMI) women with prediabetes or type 2 diabetes as compared to those with normoglycemia (NG) (35.5 ± 7.0 vs. 30.3 ± 7.7%, P < 0.0001), whereas no differences were observed for BMI. Similarly, increased BF% was found in lean BMI‐classified men with prediabetes or type 2 diabetes (25.2 ± 9.0 vs. 19.9 ± 8.0%, P = 0.008), exhibiting no differences in BMI or waist circumference. In conclusion, assessing BF% may help to diagnose disturbed glucose tolerance beyond information provided by BMI and waist circumference in particular in male subjects with BMI <25 kg/m2 and over the age of 40.

Non-alcoholic fatty liver disease (NAFLD) is a major global health threat due to its growing incidence and prevalence. It is becoming the leading cause of liver disease in addition to its strong association with cardio-metabolic disease. Therefore, its prevention and treatment are of strong public interest. Therapeutic approaches emphasize lifestyle modifications including physical activity and the adoption of healthy eating habits that intend to mainly control body weight and cardio-metabolic risk factors associated with the metabolic syndrome. Lifestyle interventions may be reinforced by pharmacological treatment in advanced stages, though there is still no registered drug for the specific treatment of NAFLD. The purpose of this review is to assess the evidence available regarding the impact of dietary recommendations against NAFLD, highlighting the effect of macronutrient diet composition and dietary patterns in the management of NAFLD.

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1810-1817.

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Type 2 diabetes prevalence is increasing dramatically worldwide. Metabolic surgery is the most effective treatment for selected patients with diabetes and/or obesity. When compared to intensive medical therapy and lifestyle intervention, metabolic surgery has shown superiority in achieving glycemic improvement, reducing number of medications and cardiovascular risk factors, which translates in long-term benefits on cardiovascular morbidity and mortality. The mechanisms underlying diabetes improvement after metabolic surgery have not yet been clearly understood but englobe a complex interaction among improvements in beta cell function and insulin secretion, insulin sensitivity, intestinal gluconeogenesis, changes in glucose utilization, and absorption by the gut and changes in the secretory pattern and morphology of adipose tissue. These are achieved through different mediators which include an enhancement in gut hormones release, especially, glucagon-like peptide 1, changes in bile acids circulation, gut microbiome, and glucose transporters expression. Therefore, this review aims to provide a comprehensive appraisal of what is known so far to better understand the mechanisms through which metabolic surgery improves glycemic control facilitating future research in the field.