Dataset Information

Physical Activity and Pediatric Obesity: A Quantile Regression Analysis.

ABSTRACT: PURPOSE:We aimed to determine whether moderate to vigorous physical activity (MVPA) and sedentary behavior (SB) were independently associated with body mass index (BMI) and waist circumference (WC) in children and adolescents. METHODS:Data from the International Children's Accelerometry Database were used to address our objectives (N = 11,115; 6-18 yr; 51% female). We calculated age- and gender-specific BMI and WC z-scores and used accelerometry to estimate MVPA and total SB. Self-reported television viewing was used as a measure of leisure time SB. Quantile regression was used to analyze the data. RESULTS:MVPA and total SB were associated with lower and higher BMI and WC z-scores, respectively. These associations were strongest at the higher percentiles of the z-score distributions. After including MVPA and total SB in the same model, the MVPA associations remained, but the SB associations were no longer present. For example, each additional hour per day of MVPA was not associated with BMI z-score at the 10th percentile (b = -0.02, P = 0.170) but was associated with lower BMI z-score at the 50th (b = -0.19, P < 0.001) and 90th percentiles (b = -0.41, P < 0.001). More television viewing was associated with higher BMI and WC, and the associations were strongest at the higher percentiles of the z-score distributions, with adjustment for MVPA and total SB. CONCLUSIONS:Our observation of stronger associations at the higher percentiles indicates that increasing MVPA and decreasing television viewing at the population-level could shift the upper tails of the BMI and WC frequency distributions to lower values, thereby lowering the number of children and adolescents classified as obese.

SUBMITTER: Mitchell JA

PROVIDER: S-EPMC5321492 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Physical Activity and Pediatric Obesity: A Quantile Regression Analysis.

Mitchell Jonathan A JA Dowda Marsha M Pate Russell R RR Kordas Katarzyna K Froberg Karsten K Sardinha Luís B LB Kolle Elin E Page Angela A

Medicine and science in sports and exercise 20170301 3

<h4>Purpose</h4>We aimed to determine whether moderate to vigorous physical activity (MVPA) and sedentary behavior (SB) were independently associated with body mass index (BMI) and waist circumference (WC) in children and adolescents.<h4>Methods</h4>Data from the International Children's Accelerometry Database were used to address our objectives (N = 11,115; 6-18 yr; 51% female). We calculated age- and gender-specific BMI and WC z-scores and used accelerometry to estimate MVPA and total SB. Self ...[more]

PMID: 27755284

Similar Datasets

Project description:Radiomics involves the study of tumor images to identify quantitative markers explaining cancer heterogeneity. The predominant approach is to extract hundreds to thousands of image features, including histogram features comprised of summaries of the marginal distribution of pixel intensities, which leads to multiple testing problems and can miss out on insights not contained in the selected features. In this paper, we present methods to model the entire marginal distribution of pixel intensities via the quantile function as functional data, regressed on a set of demographic, clinical, and genetic predictors to investigate their effects of imaging-based cancer heterogeneity. We call this approach quantile functional regression, regressing subject-specific marginal distributions across repeated measurements on a set of covariates, allowing us to assess which covariates are associated with the distribution in a global sense, as well as to identify distributional features characterizing these differences, including mean, variance, skewness, heavy-tailedness, and various upper and lower quantiles. To account for smoothness in the quantile functions, account for intrafunctional correlation, and gain statistical power, we introduce custom basis functions we call quantlets that are sparse, regularized, near-lossless, and empirically defined, adapting to the features of a given data set and containing a Gaussian subspace so non-Gaussianness can be assessed. We fit this model using a Bayesian framework that uses nonlinear shrinkage of quantlet coefficients to regularize the functional regression coefficients and provides fully Bayesian inference after fitting a Markov chain Monte Carlo. We demonstrate the benefit of the basis space modeling through simulation studies, and apply the method to Magnetic resonance imaging (MRI) based radiomic dataset from Glioblastoma Multiforme to relate imaging-based quantile functions to various demographic, clinical, and genetic predictors, finding specific differences in tumor pixel intensity distribution between males and females and between tumors with and without DDIT3 mutations.

Project description:Studies of the health effects of the microbiome often measure overall associations by using diversity metrics, and individual taxa associations in separate analyses, but do not consider the correlated relationships between taxa in the microbiome. In this study, we applied random subset weighted quantile sum regression with repeated holdouts (WQSRSRH), a mixture method successfully applied to ‘omic data to account for relationships between many predictors, to processed amplicon sequencing data from the Human Microbiome Project. We simulated a binary variable associated with 20 operational taxonomic units (OTUs). WQSRSRH was used to test for the association between the microbiome and the simulated variable, adjusted for sex, and sensitivity and specificity were calculated. The WQSRSRH method was also compared to other standard methods for microbiome analysis. The method was further illustrated using real data from the Growth and Obesity Cohort in Chile to assess the association between the gut microbiome and body mass index. In the analysis with simulated data, WQSRSRH predicted the correct directionality of association between the microbiome and the simulated variable, with an average sensitivity and specificity of 75% and 70%, respectively, in identifying the 20 associated OTUs. WQSRSRH performed better than all other comparison methods. In the illustration analysis of the gut microbiome and obesity, the WQSRSRH analysis identified an inverse association between body mass index and the gut microbe mixture, identifying Bacteroides, Clostridium, Prevotella, and Ruminococcus as important genera in the negative association. The application of WQSRSRH to the microbiome allows for analysis of the mixture effect of all the taxa in the microbiome, while simultaneously identifying the most important to the mixture, and allowing for covariate adjustment. It outperformed other methods when using simulated data, and in analysis with real data found results consistent with other study findings.

Dataset Information

Physical Activity and Pediatric Obesity: A Quantile Regression Analysis.

Publications

Physical Activity and Pediatric Obesity: A Quantile Regression Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets