Department of Human Genetics, Leiden University Medical Center Leiden, Netherlands.
Front Physiol. 2013 May 1;4:94. doi: 10.3389/fphys.2013.00094. eCollection 2013.
Recently there has been an increasing interest in exploiting computational and statistical techniques for the purpose of component analysis of indirect calorimetry data. Using these methods it becomes possible to dissect daily energy expenditure into its components and to assess the dynamic response of the resting metabolic rate (RMR) to nutritional and pharmacological manipulations. To perform robust component analysis, however, is not straightforward and typically requires the tuning of parameters and the preprocessing of data. Moreover the degree of accuracy that can be attained by these methods depends on the configuration of the system, which must be properly taken into account when setting up experimental studies. Here, we review the methods of Kalman filtering, linear, and penalized spline regression, and minimal energy expenditure estimation in the context of component analysis and discuss their results on high resolution datasets from mice and rats. In addition, we investigate the effect of the sample time, the accuracy of the activity sensor, and the washout time of the chamber on the estimation accuracy. We found that on the high resolution data there was a strong correlation between the results of Kalman filtering and penalized spline (P-spline) regression, except for the activity respiratory quotient (RQ). For low resolution data the basal metabolic rate (BMR) and resting RQ could still be estimated accurately with P-spline regression, having a strong correlation with the high resolution estimate (R (2) > 0.997; sample time of 9 min). In contrast, the thermic effect of food (TEF) and activity related energy expenditure (AEE) were more sensitive to a reduction in the sample rate (R (2) > 0.97). In conclusion, for component analysis on data generated by single channel systems with continuous data acquisition both Kalman filtering and P-spline regression can be used, while for low resolution data from multichannel systems P-spline regression gives more robust results.
最近，人们越来越感兴趣的是利用计算和统计技术来分析间接测热数据的成分。使用这些方法，可以将日常能量消耗分解为其组成部分，并评估静息代谢率（RMR）对营养和药物干预的动态反应。然而，要进行稳健的成分分析并不简单，通常需要调整参数和预处理数据。此外，这些方法所能达到的精度程度取决于系统的配置，在进行实验研究时必须适当考虑这一点。在这里，我们回顾了卡尔曼滤波、线性和惩罚样条回归以及最小能量消耗估计的方法，在成分分析的背景下讨论了它们在小鼠和大鼠高分辨率数据集上的结果。此外，我们还研究了采样时间、活动传感器的精度以及腔的冲洗时间对估计精度的影响。我们发现，在高分辨率数据上，卡尔曼滤波和惩罚样条（P-spline）回归的结果之间存在很强的相关性，除了活动呼吸商（RQ）之外。对于低分辨率数据，P-spline 回归仍然可以准确估计基础代谢率（BMR）和静息 RQ，与高分辨率估计值具有很强的相关性（R (2)>0.997；采样时间为 9 分钟）。相比之下，食物的热效应（TEF）和与活动相关的能量消耗（AEE）对采样率的降低更为敏感（R (2)>0.97）。总之，对于具有连续数据采集的单通道系统生成的数据进行成分分析，可以同时使用卡尔曼滤波和 P-spline 回归，而对于多通道系统的低分辨率数据，P-spline 回归给出了更稳健的结果。
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