Quantitative Estimation of Sensitivity of Lipolysis to Insulin

这项工作是与NIDDK临床内分泌科的AE Sumner博士、南加州大学医学院的RN Bergman博士和NIDDK LBM的CC Chow博士合作完成的。 本研究的目的是定量评估FFA对胰岛素的反应。我们推测，将血浆胰岛素测量的时程转换为FFA测量的时程的数学模型将由参数描述，这些参数将作为胰岛素作用调节脂解作用的定量度量。我们考虑了几个不同复杂性和作用机制的不同数学模型。我们根据模型比较的标准原则来区分这些模型：(1)拟合度，(2)描述模型所需的参数空间，(3)参数的可识别性，(4)生物相关性。 与数据和模型复杂性相匹配的最佳执行模型具有通过与葡萄糖最小模型的葡萄糖远程隔室不同的远程脂肪隔室起作用的胰岛素。从生理上讲，胰岛素影响FFA动力学的远端脂肪隔室与胰岛素调节血糖水平的远端葡萄糖隔室不同，这可能并不令人惊讶。胰岛素调节葡萄糖的途径涉及细胞表面的胰岛素受体，最终涉及葡萄糖转运体。相反，胰岛素通过启动一系列事件来调节脂肪分解，从而抑制脂肪分解，促进激素敏感脂肪酶和蛋白质Perilipin的去磷酸化。 对于模型II-2，存在线性清除率(仅依赖于FFA浓度)和远程作用胰岛素对FFA出现的非线性抑制。我们发现，一阶Hill函数足以表示相关的路径。这可能表明在这些通路中存在单一的一阶过程，即速率限制。Hill函数可以与胰岛素钳夹实验中发现的FFA Ra对胰岛素的依赖进行比较，在该实验中发现数据可以符合幂定律。Hill函数可以是适合此数据的另一种参数形式，因此与夹具结果一致。 游离脂肪酸浓度时间进程的一个方面是，最终水平几乎总是高于初始水平。该模型能够匹配数据的这一方面，尽管它没有插入特定的生物物理机制来解释这种影响。取而代之的是，该模型假定FFA的初始值是自由参数。在360分钟时，胰岛素浓度已恢复到基础水平，而FFA达到的水平可能高于初始值。360分钟时FFA水平的升高可能有几个原因，包括最初注射葡萄糖后的反弹，低血糖的影响，反向调节激素，或昼夜变化。 先前的研究表明，可能存在最大限度地抑制FFA血浆出现的水平。这将在3型模型中表现为胰岛素非依赖性脂解率。然而，我们的模型评估表明，添加此参数并不足以改善对数据的拟合度，从而保证将其包括在内。原因可能是测量的基准FFA水平有很大的变异性。可能需要额外的数据来解决胰岛素非依赖率问题。 我们只关注胰岛素对FFA水平的作用，我们发现我们的模型与数据完全吻合，可以产生一个敏感性指数SFI。因此，我们没有探讨葡萄糖对FFA水平的直接作用的可能性。葡萄糖的作用是通过它对胰岛素的作用来间接解释的。有实验证据支持我们的决定。我们采用最小模型的理念也意味着我们没有考虑儿茶酚胺、皮质类固醇和胰高血糖素的直接影响。据推测，这些荷尔蒙导致了模型中没有考虑到的变异性。由于FFA促进胰岛素抵抗调节血糖的能力，这个模型可能会让我们更深入地理解为什么一些肥胖者对胰岛素对血糖的影响具有抵抗力，而另一些人则是敏感的。SFI还可能指导选择最有可能对提高脂肪组织中胰岛素敏感性的药物(如噻唑烷二酮)有反应的患者。SFI与脂肪质量之间的显著负相关是SFI最终将在指导胰岛素增敏剂治疗方面有价值的推定证据。 我们认识到，我们开发的模型必须在其他人群中进行测试，进行重复性测试，并经过严格的验证。提供验证的一种方法是对同一受试者进行IM-FSIGT和胰岛素钳试验，并将我们的模型参数与从钳中得出的参数进行比较。钳制实验可以给出对FFA对胰岛素的出现率和FFA清除率的依赖程度的估计，这可以与从我们的模型得出的等效量进行比较。当前模型的价值在于，它是一个FFA最小模型，似乎可以解释IM-FSIGT期间的大多数FFA水平。这些结果现在正在准备出版，以便对模型进行更广泛的研究和审问。

This work was performed in collaboration with Dr. AE Sumner of the Clinical Endocrinology Branch, NIDDK, Dr. RN Bergman of the USC Medical School and Dr. CC Chow of LBM, NIDDK. The purpose of this investigation was to quantitatively assess FFA response to insulin. We postulated that a mathematical model transforming a time-course of plasma insulin measurements into a time-course of FFA measurements would be described by parameters that would serve as quantitative measures of the regulation of lipolysis by insulin action. We considered several different mathematical models of varying complexity and mechanisms of action. We discriminated between these models on the basis of standard principles of model comparison: (1) Goodness of fit, (2) Parameter space required to describe the model, (3) Identifiability of parameters, and (4) Biological relevance. The best performing model balancing fit to data and model complexity has insulin action through a remote adipose compartment that is different from the glucose remote compartment of the glucose minimal model. Physiologically, it may not be surprising that the remote adipose compartment from which insulin affects FFA dynamics is different from the remote glucose compartment from which insulin modulates glucose levels. The pathways through which insulin regulates glucose involves the insulin receptor on the cell surface and ultimately glucose transporters. In contrast insulin regulates lipolysis by initiating a chain of events that leads to inhibition of lipolysis, by promoting the dephosphorylation of both hormone sensitive lipase and the protein perilipin. For the kinetics of model II-2, there is a linear clearance rate (only dependent on FFA concentration) and a nonlinear suppression of FFA appearance by remote acting insulin. We found that a first order Hill function was sufficient to represent the relevant pathways. This may suggest that there is a single first order process within these pathways that is rate limiting. The Hill function could be compared to the FFA Ra dependence on insulin as found in insulin clamp experiments, where it was found that the data could be fit to a power law. A Hill function could be an alternative parametric form for a fit to this data and is thus consistent with the clamp results. One aspect of the time course of FFA concentration is that the final level is almost always higher than the initial level. The model is able to match this aspect of the data although it does not insert a specific biophysical mechanism to account for this effect. Instead, the model assumes that the initial value of FFA is a free parameter. At 360 minutes, insulin concentration has returned to basal levels and FFA attains a level that can be higher than the initial value. The elevation in FFA levels at 360 minutes may occur for several reasons, including the rebound after exposure to the initial bolus of glucose, the effect of hypoglycemia, counter regulatory hormones, or diurnal variation. Previous studies indicate that there may be a maximally suppressible level of FFA plasma appearance. This would be manifested as the insulin independent lipolysis rate in the Type 3 models. However, our model evaluation showed that adding this parameter did not improve the fit to the data enough to warrant its inclusion. The reason may be the wide variability in the measured baseline FFA levels. Additional data may be required to resolve the insulin independent rate. We focused exclusively on the action of insulin on FFA levels and we found that our models adequately fit to the data to produce a sensitivity index SFI. As a result we did not explore the possibility of direct action of glucose on FFA levels. The action of glucose is indirectly accounted for through its effect on insulin. There is experimental evidence supporting our decision. Our philosophy of employing a minimal model also meant that we did not incorporate the direct effects of catecholamines, corticosteroids and glucagon. Presumably, these hormones contribute to the variability not accounted for by the model. As FFA promote resistance to insulins ability to regulate glucose, this model could potentially lead to a greater insight into our understanding for why some individuals with obesity are resistant to insulins effect on glucose and others are sensitive. The SFI could also potentially guide the selection of patients that are most likely to respond to agents which increase insulin sensitivity in adipose tissues such as thiazolidinediones. The significant negative correlation between SFI and fat mass is presumptive evidence that SFI will ultimately be of value in guiding therapy with insulin sensitizers. We recognize that the model we developed must be tested in other populations, be tested for reproducibility and undergo rigorous validation. One means of providing validation is to perform an IM-FSIGT and an insulin clamp experiment with a tracer such as labeled palmitate on the same subject and compare our model parameters to those derived from the clamp. The clamp experiments can give an estimate of the dependence on FFA rate of appearance on insulin and the rate of FFA clearance, which can be compared to the equivalent quantities derived from our model. The value of the current model is that it is a FFA minimal model that appears to explain most of the FFA levels during the IM-FSIGT. These results are being prepared for publication now to allow the models to be subject to wider study and interrogation.