Body weight, insulin resistance, and serum adipokine levels 2 years after 2 types of bariatric surgery.

OBJECTIVE: Bariatric surgery reverses obesity-related comorbidities, including type 2 diabetes mellitus. Several studies have already described differences in anthropometrics and body composition in patients undergoing Roux-en-Y gastric bypass compared with laparoscopic adjustable gastric banding, but the role of adipokines in the outcomes after the different types of surgery is not known. Differences in weight loss and reversal of insulin resistance exist between the 2 groups and correlate with changes in adipokines. METHODS: Fifteen severely obese women (mean body mass index [BMI]: 46.7 kg/m(2)) underwent 2 types of laparoscopic weight loss surgery (Roux-en-Y gastric bypass=10, adjustable gastric banding=5). Weight, waist and hip circumference, body composition, plasma metabolic markers, and lipids were measured at set intervals during a 24-month period after surgery. RESULTS: At 24 months, patients who underwent Roux-en-Y were overweight (BMI 29.7 kg/m(2)), whereas patients who underwent gastric banding remained obese (BMI 36.3 kg/m(2)). Patients who underwent Roux-en-Y lost significantly more fat mass than patients who underwent gastric banding (mean difference 16.8 kg, P<.05). Likewise, leptin levels were lower in the patients who underwent Roux-en-Y (P=.003), and levels correlated with weight loss, loss of fat mass, insulin levels, and Homeostasis Model of Assessment 2. Adiponectin correlated with insulin levels and Homeostasis Model of Assessment 2 (r=-0.653, P=.04 and r=-0.674, P=.032, respectively) in the patients who underwent Roux-en-Y at 24 months. CONCLUSION: After 2 years, weight loss and normalization of metabolic parameters were less pronounced in patients who underwent gastric banding compared with patients who underwent Roux-en-Y gastric bypass. Our findings require confirmation in a prospective randomized trial.

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.