Lee Jong Soo, Zakeri Issa F, Butte Nancy F
Department of Mathematical Sciences, University of Massachusetts Lowell, Massachusetts, United States of America.
Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, Pennsylvania, United States of America.
PLoS One. 2017 May 10;12(5):e0177286. doi: 10.1371/journal.pone.0177286. eCollection 2017.
Adequate sleep is crucial during childhood for metabolic health, and physical and cognitive development. Inadequate sleep can disrupt metabolic homeostasis and alter sleeping energy expenditure (SEE). Functional data analysis methods were applied to SEE data to elucidate the population structure of SEE and to discriminate SEE between obese and non-obese children. Minute-by-minute SEE in 109 children, ages 5-18, was measured in room respiration calorimeters. A smoothing spline method was applied to the calorimetric data to extract the true smoothing function for each subject. Functional principal component analysis was used to capture the important modes of variation of the functional data and to identify differences in SEE patterns. Combinations of functional principal component analysis and classifier algorithm were used to classify SEE. Smoothing effectively removed instrumentation noise inherent in the room calorimeter data, providing more accurate data for analysis of the dynamics of SEE. SEE exhibited declining but subtly undulating patterns throughout the night. Mean SEE was markedly higher in obese than non-obese children, as expected due to their greater body mass. SEE was higher among the obese than non-obese children (p<0.01); however, the weight-adjusted mean SEE was not statistically different (p>0.1, after post hoc testing). Functional principal component scores for the first two components explained 77.8% of the variance in SEE and also differed between groups (p = 0.037). Logistic regression, support vector machine or random forest classification methods were able to distinguish weight-adjusted SEE between obese and non-obese participants with good classification rates (62-64%). Our results implicate other factors, yet to be uncovered, that affect the weight-adjusted SEE of obese and non-obese children. Functional data analysis revealed differences in the structure of SEE between obese and non-obese children that may contribute to disruption of metabolic homeostasis.
充足的睡眠对儿童的代谢健康、身体和认知发展至关重要。睡眠不足会扰乱代谢稳态并改变睡眠能量消耗（SEE）。应用功能数据分析方法对SEE数据进行分析，以阐明SEE的总体结构，并区分肥胖儿童和非肥胖儿童的SEE。在房间呼吸热量计中测量了109名5至18岁儿童的逐分钟SEE。应用平滑样条法对热量计数据进行处理，以提取每个受试者的真实平滑函数。使用功能主成分分析来捕捉功能数据的重要变化模式，并识别SEE模式的差异。将功能主成分分析与分类算法相结合用于对SEE进行分类。平滑有效地去除了房间热量计数据中固有的仪器噪声，为分析SEE的动态变化提供了更准确的数据。SEE在整个夜间呈现下降但有细微波动的模式。正如预期的那样，由于肥胖儿童体重更大，其平均SEE明显高于非肥胖儿童。肥胖儿童的SEE高于非肥胖儿童（p<0.01）；然而，经事后检验后，体重调整后的平均SEE没有统计学差异（p>0.1）。前两个成分的功能主成分得分解释了SEE中77.8%的方差，并且两组之间也存在差异（p = 0.037）。逻辑回归、支持向量机或随机森林分类方法能够以良好的分类率（62 - 64%）区分肥胖和非肥胖参与者的体重调整后的SEE。我们的结果表明，还有其他尚未发现的因素影响肥胖和非肥胖儿童的体重调整后的SEE。功能数据分析揭示了肥胖和非肥胖儿童之间SEE结构的差异，这可能导致代谢稳态的破坏。
