1 Department of Medicine, University of California San Diego , La Jolla, California.
Diabetes Technol Ther. 2013 Oct;15(10):870-80. doi: 10.1089/dia.2013.0084. Epub 2013 Aug 6.
Insulin resistance (IR) and hyperinsulinemia as well as obesity play a key role in the metabolic syndrome (MetS), type 2 diabetes (T2D), and associated cardiovascular disease. Unfortunately, IR and hyperinsulinemia are often diagnosed late (i.e., when the MetS is already clinically evident). An earlier diagnosis of IR would be desirable to reduce its clinical consequences, in particular in view of the increasing prevalence of obesity and diabetes conditions. For this purpose, we developed a mathematical model capable of detecting early onset of IR through small variations of insulin sensitivity, glucose effectiveness, and first- or second-phase responses.
Murine models provide controlled conditions to study various stages of IR. Various degrees of hypercholesterolemia, obesity, IR, and atherosclerosis were induced in low-density lipoprotein receptor-deficient mice by feeding them cholesterol- or sucrose-rich diets. IR was assessed by oral glucose tolerance tests. Controls included animals fed exclusively, or switched back to, regular chow. A nonlinear mathematical model of the order of 5 was developed by refining Bergman's "Minimal Model" and then applied to experimental data.
Different metabolic constellations consistently corresponded to specific and close-meshed changes in model parameters. Reduced second-phase glucose sensitivity characterized an early impaired glucose tolerance. Later stages showed an increased first-phase glucose sensitivity compensating for decreased insulin sensitivity. Finally, T2D was associated with both first- and second-phase sensitivities close to zero.
The new mathematical model detected various insulin-sensitive or -resistant metabolic stages of IR. It can therefore be implemented for quantitative metabolic risk assessment and may be of therapeutic value by anticipating the start of therapeutic interventions.
胰岛素抵抗（IR）和高胰岛素血症以及肥胖在代谢综合征（MetS）、2 型糖尿病（T2D）和相关心血管疾病中起着关键作用。不幸的是，IR 和高胰岛素血症通常诊断较晚（即，当 MetS 已经在临床上明显时）。如果能够更早地诊断出 IR，将有助于减少其临床后果，特别是考虑到肥胖和糖尿病发病率的不断增加。为此，我们开发了一种数学模型，能够通过检测胰岛素敏感性、葡萄糖效应、第一或第二相反应的微小变化来早期发现 IR。
小鼠模型为研究 IR 的各个阶段提供了受控条件。通过给予富含胆固醇或蔗糖的饮食，使低密度脂蛋白受体缺陷小鼠产生不同程度的高胆固醇血症、肥胖、IR 和动脉粥样硬化。通过口服葡萄糖耐量试验评估 IR。对照组包括只喂食或重新喂食普通食物的动物。通过精炼 Bergman 的“最小模型”，开发了一个大约 5 阶的非线性数学模型，然后将其应用于实验数据。
不同的代谢组合始终对应于模型参数的特定和紧密匹配的变化。第二相葡萄糖敏感性降低表明早期糖耐量受损。后期阶段表现出第一相葡萄糖敏感性增加，以补偿胰岛素敏感性降低。最后，T2D 与第一和第二相敏感性接近零有关。
新的数学模型检测到 IR 的各种胰岛素敏感或抵抗代谢阶段。因此，它可以用于定量代谢风险评估，并通过预测治疗干预的开始具有治疗价值。
