DIET AND INSULIN SENSITIVITY: STATISTICAL METHODS

描述：胰岛素敏感性决定因素的评估是糖尿病和心血管疾病风险研究的基本目标。胰岛素敏感性（SI）测量的侵入性和间接性之间的权衡是营养流行病学和临床试验的挑战。虽然葡萄糖钳夹技术是高度精确的，但它的侵入性太强，而且价格昂贵，不能用于大型研究。口服葡萄糖耐量试验（OGTT）更实用，但需要复杂的建模与许多假设，以产生常用的胰岛素敏感性的估计。已经发表了基于房室模型和相关微分方程的各种OGTT SI算法，但有迹象表明，广泛引用的方法不能有效地用于正在进行的或初期代谢疾病的背景下。NHLBI OMNI-Carb研究是一项对照喂养研究，在巴尔的摩和波士顿对163名超重或肥胖的糖尿病或CVD风险升高的参与者进行了研究。所有参与者在交叉析因设计中接受随机序列的四个五周饮食，高或低血糖指数和高或低碳水化合物定义了主要实验因素;在基线和每个饮食期结束时进行两小时，七个样本的OGTT方案。获得了135名参与者所有饮食的完整OGTT系列。基于口头最小模型的估计的应用（Della Man等人AJP 2004）表明，在估计依赖于对任意选择的起始值敏感的解过程的收敛的意义上，SI的估计在参与者的非平凡部分上在数值上是不稳定的;对于序列的非平凡部分，也无法实现生物学上合理的解的收敛。拟议的项目将使用新的程序来评估最小模型的关键参数的“实际”不可识别性，并将应用新开发的贝叶斯推理技术，以适当地量化SI推理中的不确定性。我们还将创建和说明采用多变量时间序列分析中新开发的概念的分析方法，以更有力地区分与暴露或健康状况相关的OGTT系列特征。这项工作将有助于充分收获现有的和新的OGTT数据库，并将通过提供透明的计算解决方案和更灵活和更容易获得的分析框架，促进越来越多的研究结果的协调，这些研究结果使用OGTT来解决肥胖，糖尿病和心血管疾病流行病的后果。

DESCRIPTION: Assessment of determinants of insulin sensitivity is a basic objective of research into diabetes and cardiovascular disease risk. Trade-offs between invasiveness and indirectness of insulin sensitivity (SI) measurement are a challenge for nutritional epidemiology and clinical trials. While the glucose clamp is highly accurate, it is too invasive and expensive t use in large studies. The oral glucose tolerance test (OGTT) is much more practical but requires complex modeling with numerous assumptions to yield commonly used estimates of insulin sensitivity. Various OGTT SI algorithms based on compartmental models and associated differential equations have been published, but there are indications that widely cited methods cannot be used effectively in the context of ongoing or incipient metabolic disease. The NHLBI OMNI-Carb study was a controlled feeding study of 163 overweight or obese participants at elevated risk for diabetes or CVD in Baltimore and Boston. All participants received a random sequence of four five-week diets in a crossover factorial design with high or low glycemic index and high or low carbohydrate defining the main experimental factors; a two-hour, seven sample OGTT protocol was performed at baseline and at the end of each diet period. Full OGTT series on all diets were obtained for 135 participants. Application of the oral minimal model based estimation (Della Man et al AJP 2004) indicated that estimation of SI was numerically unstable on a nontrivial fraction of participants in the sense that estimates were dependent on convergence of solution processes that were sensitive to arbitrarily chosen starting values; convergence to biologically plausible solutions was also not achievable for a nontrivial fraction of series. The proposed project will use new procedures for assessing "practical" nonidentifiability of key parameters of the minimal model, and will apply newly developed Bayesian inference techniques to properly quantify uncertainty in inference on SI. We will also create and illustrate analytical methods that employ newly developed concepts in multivariate time series analysis to more powerfully discriminate features of OGTT series that correlate with exposures or health conditions. This work will help fully harvest existing and new OGTT databases and will facilitate harmonization of the growing body of research results that use OGTT to address the consequences of obesity, diabetes and cardiovascular disease epidemics by providing transparent computational solutions and more flexible and accessible analytic frameworks.