The ability to ensure continous availability of energy despite highly variable supplies in the environment is a major determinant of the survival of all species. In higher organisms, including mammals, the capacity to efficiently store excess energy as triglycerides in adipocytes, from which stored energy could be rapidly released for use at other sites, was developed. To orchestrate the processes of energy storage and release, highly integrated systems operating on several physiological levels have evolved. The adipocyte is no longer considered a passive bystander, because fat cells actively secrete many members of the cytokine family, such as leptin, tumor necrosis factor-α, and interleukin-6, among other cytokine signals, which influence peripheral fuel storage, mobilization, and combustion, as well as energy homeostasis. The existence of a network of adipose tissue signaling pathways, arranged in a hierarchical fashion, constitutes a metabolic repertoire that enables the organism to adapt to a wide range of different metabolic challenges, such as starvation, stress, infection, and short periods of gross energy excess.

Obesity, a worldwide epidemic, confers increased risk for multiple serious conditions, including type 2 diabetes, cardiovascular diseases, nonalcoholic fatty liver disease and cancer. Adipose tissue is considered one of the largest endocrine organs in the body as well as an active tissue for cellular reactions and metabolic homeostasis rather than an inert tissue for energy storage. The functional pleiotropism of adipose tissue relies on its ability to synthesize and release a large number of hormones, cytokines, extracellular matrix proteins and growth and vasoactive factors, collectively termed adipokines that influence a variety of physiological and pathophysiological processes. In the obese state, excessive visceral fat accumulation causes adipose tissue dysfunctionality that strongly contributes to the onset of obesity‐related comorbidities. The mechanisms underlying adipose tissue dysfunction include adipocyte hypertrophy and hyperplasia, increased inflammation, impaired extracellular matrix remodelling and fibrosis together with an altered secretion of adipokines. This review describes how adipose tissue becomes inflamed in obesity and summarizes key players and molecular mechanisms involved in adipose inflammation.

Obesity is currently the most extended metabolic disturbance worldwide favoring the development of cardiometabolic alterations such as type 2 diabetes, hypertension, and dyslipidemia. Obesity and the metabolic syndrome (MS) are characterized by an increase in circulating leptin concentrations, in parallel to a decrease in blood levels of adiponectin. Consequently, the adiponectin/leptin ratio has been suggested as a maker of adipose tissue dysfunction. This emerging biomarker correlates with insulin resistance better than adiponectin or leptin alone, or even HOMA and is decreased with increasing number of metabolic risk factors having been proposed as a predictive marker for the MS. Moreover, the adiponectin/leptin ratio is negatively correlated with markers of low-grade chronic inflammation. In this sense, an increase in this ratio has been related with reduced atherosclerosis risk as well as with a decreased risk of some types of cancer in epidemiological studies. In this commentary we propose new cutoffs to estimate obesity- and MS-associated cardiometabolic risk according to the adiponectin/leptin ratio and discuss different therapeutic strategies to increase this promising biomarker of metabolic risk.

BACKGROUND: Genomic studies have yielded important insights into the pathogenesis of obesity. Circulating microRNAs (miRNAs) are valuable biomarkers of systemic diseases and potential therapeutic targets. We sought to define the circulating pattern of miRNAs in obesity and examine changes after weight loss. METHODS: We assessed the genomewide circulating miRNA profile cross-sectionally in 32 men and after surgery-induced weight loss in 6 morbidly obese patients. The most relevant miRNAs were cross-sectionally validated in 80 men and longitudinally in 22 patients (after surgery-induced weight loss). We evaluated the effects of diet-induced weight loss in 9 obese patients. Thirty-six circulating miRNAs were associated with anthropometric variables in the initial sample. RESULTS: In the validation study, morbidly obese patients showed a marked increase of miR-140-5p, miR-142-3p (both P < 0.0001), and miR-222 (P = 0.0002) and decreased levels of miR-532–5p, miR-125b, miR-130b, miR-221, miR-15a, miR-423-5p, and miR-520c-3p (P < 0.0001 for all). Interestingly, in silico targets leukemia inhibitory factor receptor (LIFR) and transforming growth factor receptor (TGFR) of miR-140-5p, miR-142-3p, miR-15a, and miR-520c-3p circulated in association with their corresponding miRNAs. Moreover, a discriminant function of 3 miRNAs (miR-15a, miR-520c-3p, and miR-423-5p) was specific for morbid obesity, with an accuracy of 93.5%. Surgery-induced (but not diet-induced) weight loss led to a marked decrease of miR-140-5p, miR-122, miR-193a-5p, and miR-16-1 and upregulation of miR-221 and miR-199a-3p (P < 0.0001 for all). CONCLUSIONS: Circulating miRNAs are deregulated in severe obesity. Weight loss–induced changes in this profile and the study of in silico targets support this observation and suggest a potential mechanistic relevance.

OBJECTIVE To 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 METHODS We 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. RESULTS The 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. CONCLUSIONS CUN-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.