Adipose insulin resistance in obese adolescents across the spectrum of glucose tolerance

CONTEXT: Adipocytes represent an important insulin-responsive tissue taking an active part in glucose metabolism. OBJECTIVE: This study sought to assess adipose tissue insulin resistance (IR) across the spectrum of glucose tolerance and to test its relation with free fatty acid (FFA) suppression during an oral glucose tolerance test (OGTT). DESIGN AND SETTING: A cross-sectional analysis of a pediatric clinic–derived cohort of obese adolescents. PATIENTS OR OTHER PARTICIPANTS: Participants age 7–20 y with a body mass index that exceeded the 95th percentile for their age and sex. INTERVENTION(S): A standard oral glucose tolerance test. MAIN OUTCOME MEASURES: The adipose tissue insulin resistance index (calculated as the product of fasting insulin and FFA concentrations) (Adipose IR) and the area under curve of FFAs during the OGTT were compared between glucose tolerance categories. RESULTS: A total of 962 obese children and adolescents participated in this study. Adipose IR significantly increased across glucose tolerance categories (P for trend < .001). Within the normal glucose tolerance participants, an increase in adipose IR was observed related to an increase in 2-hr glucose levels. In a subsample of participants who underwent abdominal imaging for determination of lipid partitioning (n = 115), a tight relation of visceral fat (r = 0.34; P < .001) and the visceral/sc fat ratio (r = 0.55; P < .001) with the Adipose IR index was evident. Greater area under the curve FFAs (lower FFA suppression) during the OGTT was evident with worsening glucose tolerance (P for trend < .001). Glucose tolerance category, degree of obesity (body mass index–z score), IL-6, and low adiponectin emerged as significant predictors of the Adipose IR. CONCLUSIONS: Adipose IR is associated with reduced suppression of FFAs during the OGTT and with an altered adipocytokine profile. The negative relation with insulin secretion deserves further longitudinal investigation in the context of deteriorating glucose tolerance

Glucose effectiveness in obese children: relation to degree of obesity and dysglycemia.

OBJECTIVE: Impaired glucose effectiveness (GE) plays a role in the deterioration of glucose metabolism. Our aim was to validate a surrogate of GE derived from an oral glucose tolerance test (OGTT) and to assess the impact of degrees of obesity and of glucose tolerance on it. RESEARCH DESIGN AND METHODS: The OGTT-derived surrogate of GE (oGE) was validated in obese adolescents who underwent an OGTT and an intravenous glucose tolerance test (IVGTT). We then evaluated anthropometric determinants of the oGE and its impact on the dynamics of glucose tolerance in a cohort of children with varying degrees of obesity. RESULTS: The correlation of oGE and IVGTT-derived GE in 98 obese adolescents was r = 0.35 (P < 0.001) as a whole and r = 0.51 (P < 0.001) in subjects with normal glucose tolerance. In a cohort of 1,418 children, the adjusted GE was associated with increasing obesity (P < 0.001 for each category of obesity). Quartiles of oGE and the oral disposition index were associated with 2-h glucose levels (P < 0.001 for both). Among 421 nondiabetic obese subjects (276 subjects with normal glucose tolerance/145 subjects with impaired glucose tolerance who repeated their OGTT after a mean time of 28 ± 16 months), oGE changes were tightly associated with weight (r = 0.83, P < 0.001) and waist circumference changes (r = 0.67, P < 0.001). Baseline oGE and changes in oGE over time emerged as significant predictors of the change in 2-h glucose levels (standardized B = -0.76 and B = -0.98 respectively, P < 0.001 for both). CONCLUSIONS: The oGE is associated with the degree of and changes in weight and waist circumference and is an independent predictor of glucose tolerance dynamics

Multiple classifier systems under attack

In adversarial classification tasks like spam filtering, intrusion detection in computer networks and biometric authentication, a pattern recognition system must not only be accurate, but also robust to manipulations of input samples made by an adversary to mislead the system itself. It has been recently argued that the robustness of a classifier could be improved by avoiding to overemphasize or underemphasize input features on the basis of training data, since at operation phase the feature importance may change due to modifications introduced by the adversary. In this paper we empirically investigate whether the well known bagging and random subspace methods allow to improve the robustness of linear base classifiers by producing more uniform weight values. To this aim we use a method for performance evaluation of a classifier under attack that we are currently developing, and carry out experiments on a spam filtering task with several linear base classifiers