Quantitative Estimation of Sensitivity of Lipolysis to Insulin

肥胖和糖尿病在非洲裔美国人中比白人更常见。由于游离脂肪酸（FFA）参与这些条件的发展，研究胰岛素对FFA和葡萄糖调节的种族差异是必不可少的。目的：本研究的目的是确定葡萄糖和FFA对胰岛素的反应是否存在种族差异。设计：这是一项横断面研究。背景：本研究在一家临床研究中心进行。参会人员：34名绝经前妇女（17名非洲裔美国人，17名白人）年龄匹配36 ± 10岁（平均标准差）和体重指数（30.0 ± 6.7 kg/m2）。干预措施：胰岛素修饰的频繁采样的静脉葡萄糖耐量试验进行了数据分析的葡萄糖和游离脂肪酸的单独的最小模型。主要结果测量：血糖测量包括胰岛素敏感性指数（S（I））和急性胰岛素对葡萄糖的反应（AIRg）。FFA指标为FFA清除率（c（f））。结果：非裔美国人的体重指数相似，但脂肪量高于白人（P < 0.01）。与白人相比，非裔美国人的S（I）较低（3.71 ± 1.55 vs. 5.23 ± 2.74 ± 10（-4）min（-1）/（微单位/毫升）（P = 0.05），AIRg较高（642 379 vs. 263 206 mU/L（-1）min，P < 0.01）。校正脂肪量后，非裔美国人的FFA清除率较高，c（f）（0.13 ± 0.06 vs. 0.08 ± 0.05 min（-1），P < 0.01）。调整AIRg后，c（f）的种族差异不再存在（P = 0.51）。在所有女性中，c（f）与AIRg有显著相关性（r = 0.64，P < 0.01），而与S（I）无显著相关性（r =-0.07，P = 0.71）。当两组被分别研究时，同样的模式仍然存在。结论：非裔美国妇女比白色妇女更胰岛素抵抗，但他们有更大的FFA清除。非洲裔美国妇女的胰岛素浓度明显较高，FFA清除率较高。 我们正在与米勒博士合作开发一种新的数学方法，用于从测量的C肽血浆浓度推断胰岛素分泌率。从C肽测量值推断胰岛素分泌速率（ISR）作为胰腺β细胞功能的定量，在与胰岛素敏感性和胰岛素作用降低相关的疾病中具有临床重要性。从C肽浓度导出的ISR是非参数贝叶斯模型选择的示例，其中建议的ISR时间过程被认为是模型。从离散的可观察数据中推断出不可访问的连续变量的值在生物学和医学中通常是有问题的，因为需要计算高效的方法来解决高维约束的统计推断问题。由后验分布加权的预测可以转换为统计场论中使用的函数积分。 函数积分通常很难计算，特别是对于非分析约束，如估计参数的正性。我们提出了一种易于计算的方法，该方法使用相关似然函数的精确解作为完整模型后验的马尔科夫链蒙特卡罗评估的先验。 我们的方法证明了函数积分贝叶斯模型选择作为这种数据驱动推理的实用方法的可行性，允许数据确定平滑时间尺度和模型空间上先验的宽度。 我们正在与Yanovski实验室（NICHD）合作，将我们的研究扩展到奥利司他干预对儿科患者的影响，以及不同种族群体的比较研究（Sumner，NIDDK）。 我们正在研究β受体阻滞剂对模型参数的影响，检验胰岛素对血浆FFA作用的变化可能与这些药物的疗效相关的BF假设（与马里兰州大学Beitelshees合作）。 此外，我们正在检验BF假设，即TZD在肥胖受试者中的疗效与胰岛素对脂解作用的数学模型中参数的变化相关（与Snitker，马里兰州大学合作）。

Obesity and diabetes are more common in African-Americans than whites. Because free fatty acids (FFA) participate in the development of these conditions, studying race differences in the regulation of FFA and glucose by insulin is essential. Objective: The objective of the study was to determine whether race differences exist in glucose and FFA response to insulin. Design: This was a cross-sectional study. Setting: The study was conducted at a clinical research center. Participants: Thirty-four premenopausal women (17 African-Americans, 17 whites) matched for age 36 10 yr (mean sd) and body mass index (30.0 6.7 kg/m(2)). Interventions: Insulin-modified frequently sampled iv glucose tolerance tests were performed with data analyzed by separate minimal models for glucose and FFA. Main Outcome Measures: Glucose measures were insulin sensitivity index (S(I)) and acute insulin response to glucose (AIRg). FFA measures were FFA clearance rate (c(f)). Results: Body mass index was similar but fat mass was higher in African-Americans than whites (P < 0.01). Compared with whites, African-Americans had lower S(I) (3.71 1.55 vs. 5.23 2.74 10(-4) min(-1)/(microunits per milliliter) (P = 0.05) and higher AIRg (642 379 vs. 263 206 mU/liter(-1) min, P < 0.01). Adjusting for fat mass, African-Americans had higher FFA clearance, c(f) (0.13 0.06 vs. 0.08 0.05 min(-1), P < 0.01). After adjusting for AIRg, the race difference in c(f) was no longer present (P = 0.51). For all women, the relationship between c(f) and AIRg was significant (r = 0.64, P < 0.01), but the relationship between c(f) and S(I) was not (r = -0.07, P = 0.71). The same pattern persisted when the two groups were studied separately. Conclusion: African-American women were more insulin resistant than white women, yet they had greater FFA clearance. Acutely higher insulin concentrations in African-American women accounted for higher FFA clearance. We are collaborating with Dr. Miller in developing a new mathematical method for inferring insulin secretion rates from measured C-peptide plasma concentrations. Inference of the insulin secretion rate (ISR) from C-peptide measurements as a quantification of pancreatic beta-cell function is clinically important in diseases related to reduced insulin sensitivity and insulin action. ISR derived from C-peptide concentration is an example of non-parametric Bayesian model selection where a proposed ISR time course is considered to be a model'. An inferred value of inaccessible continuous variables from discrete observable data is often problematic in biology and medicine, because computationally efficient methods are required to solve high-dimensional constrained statistical inference problems. Predictions weighted by the posterior distribution can be cast as functional integrals as used in statistical field theory. Functional integrals are generally difficult to evaluate, especially for nonanalytic constraints such as positivity of the estimated parameters. We propose a computationally tractable method that uses the exact solution of an associated likelihood function as a prior for a Markov-chain Monte Carlo evaluation of the posterior for the full model. Our method demonstrates the feasibility of functional integral Bayesian model selection as a practical method for such data-driven inference, allowing the data to determine the smoothing time scale and the width of the prior on the space of models. We are collaborating with the Yanovski laboratory (NICHD) in extending our studies to the effects of Orlistat intervention in pediatric patients, and to comparative studies of different ethnic groups (Sumner, NIDDK). We are investigating the effects of beta-blockers on model parameters, testing the bf hypothesis that changes in insulin action on plasma FFA may be correlated with the efficacy of these drugs (collaboration with Beitelshees, University of Maryland). Furthermore, we are testing the bf hypothesis that the efficacy of TZDs in obese subjects is correlated with changes in parameters in the mathematical model of insulin action on lipolysis (collaboration with Snitker, University of Maryland).