Boston Ray C, Moate Peter J
School of Veterinary Medicine, University of Pennsylvania, New Bolton Center, 382 W. St. Road, Kennett Square, PA 19348, USA.
Am J Physiol Regul Integr Comp Physiol. 2008 Aug;295(2):R395-403. doi: 10.1152/ajpregu.90317.2008. Epub 2008 Jun 18.
The kinetics of nonesterified fatty acid (NEFA) metabolism in humans requires quantification to facilitate understanding of diseases like type 1 and 2 diabetes, metabolic syndrome, and obesity, and the mechanisms underpinning various interventions. Oral glucose tolerance tests (OGTT) and glucose meal tolerance tests (MTT) are potentially useful procedures for enabling quantification of NEFA kinetics because they both cause transitory, but substantial, declines and then rebounds in plasma NEFA concentrations in response to physiologically relevant increases in plasma glucose. The Boston MINIMAL model of NEFA kinetics was developed to analyze data from the intravenous glucose tolerance test (IVGTT), but in this work, we present for the first time its application to modeling NEFA data from both OGTT and MTT studies. This model enables estimation of SFFA (micromol.l(-1).min(-1)) (a parameter describing the maximum rate of lipolysis), and KFFA (%/min) (a parameter related to NEFA oxidation rate). The model could well describe the trajectories of NEFA concentrations following an OGTT (R2 in excess of 0.97) but was not as successful with the MTT (R2>0.65). Model parameters derived from analysis of OGTT and MTT data were well identified with coefficients of variation generally less than 15%. Type 2 diabetes, body mass index, and dietary treatment (high-fat vs. high-glycemic-index diets) were all shown to have significant effects on model parameters. Modeling plasma NEFA concentrations over 24 h has helped to identify and quantify the extent that periprandial NEFA peaks and nocturnal elevation in plasma NEFA can be accounted for by our model.
人体中非酯化脂肪酸(NEFA)代谢动力学需要进行量化,以便更好地理解1型和2型糖尿病、代谢综合征和肥胖症等疾病,以及各种干预措施的潜在机制。口服葡萄糖耐量试验(OGTT)和葡萄糖餐耐量试验(MTT)可能是量化NEFA动力学的有用方法,因为它们都会导致血浆NEFA浓度出现短暂但显著的下降,随后随着血浆葡萄糖的生理性升高而反弹。NEFA动力学的波士顿MINIMAL模型是为分析静脉葡萄糖耐量试验(IVGTT)的数据而开发的,但在本研究中,我们首次展示了该模型在OGTT和MTT研究的NEFA数据建模中的应用。该模型能够估计SFFA(微摩尔·升⁻¹·分钟⁻¹)(一个描述脂肪分解最大速率的参数)和KFFA(%/分钟)(一个与NEFA氧化速率相关的参数)。该模型能够很好地描述OGTT后NEFA浓度的变化轨迹(R²超过0.97),但在MTT中表现不如OGTT(R²>0.65)。通过分析OGTT和MTT数据得出的模型参数能够很好地确定,变异系数通常小于15%。2型糖尿病、体重指数和饮食治疗(高脂饮食与高血糖指数饮食)均对模型参数有显著影响。对24小时内的血浆NEFA浓度进行建模有助于识别和量化我们的模型能够解释的餐后NEFA峰值和夜间血浆NEFA升高的程度。