Global metabolomic profiling targeting childhood obesity in the Hispanic population

Background: Metabolomics may unravel important biological pathways involved in the pathophysiology of childhood obesity

A Youth Compendium of Physical Activities: Activity Codes and Metabolic Intensities

A Youth Compendium of Physical Activities (Youth Compendium) was developed to estimate the energy costs of physical activities using data on youth only

Multivariate Path Analysis of Serum 25-Hydroxyvitamin D Concentration, Inflammation, and Risk of Type 2 Diabetes Mellitus

Background and Aims. Despite growing interest in the protective role that vitamin D may have in health outcomes, little research has examined the mechanisms underlying this role. This study aimed to test two hypotheses: (1) serum 25-hydroxyvitamin D [25(OH)D] is inversely associated with type 2 diabetes mellitus (T2DM) and elevated hemoglobin A1c; (2) these associations are mediated by serum C-reactive protein (CRP). Methods. Participants aged 20 and older in 2001–2006 National Health and Nutrition Examination Surveys (n = 8,655) with measures of serum 25(OH)D, CRP, hemoglobin A1c, and other important covariates were included in the present study. Logistic regression and path analysis methods were applied to test the study hypotheses. Results. Decreased serum 25(OH)D concentration was significantly associated with increased odds of T2DM. In males, an estimated 14.9% of the association between 25(OH)D and hemoglobin A1c was mediated by serum CRP. However, this mediation effect was not observed in females. Conclusion. Using a nationally representative sample, the present study extends previous research and provides new evidence that the effect of decreased serum vitamin D concentration on T2DM may proceed through increased systemic inflammation in males. Longitudinal studies and randomized control trials are needed to confirm the present findings

Functional data analysis of sleeping energy expenditure

<div><p>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.</p></div

Smooth curves superimposed over raw energy expenditure (SEE).

<p>Plots showing raw SEE (black lines) along with the smoothed curves (red lines) using B-splines smoothing with K = 40.</p

Summary of functional principal component (FPC) scores.

<p>Summary of functional principal component (FPC) scores.</p

Comparing means of the sleeping energy expenditure (SEE).

<p>The left plot shows the mean SEEs at each time point. The right plot shows the means after adjusting data for weight.</p

The functional principal component (FPC) scores for the individuals.

<p>The FPC scores are indicated by open circle (non-obese) or solid circle (obese). The plot on the left hand side shows the original FPC scores while the plot on the right hand side shows the VARIMAX rotated FPC scores.</p

The functional principal component plots.

<p>The solid line is at the mean function, and the lines above and below are the functional principal component curves added (+) and subtracted (-) from the mean function. The first row shows the obese and the second row shows the non-obese components. The columns show the first and second FPCA components, respectively.</p

Raw sleeping energy expenditure (SEE) of an individual.

<p>A plot showing an example of raw sleeping energy expenditure (SEE) of an individual.</p