Randomized controlled trial subject characteristics.

<p>Randomized controlled trial subject characteristics.</p

Quality assessment using Cochrane Collaboration Risk of Bias Tool.

<p>Green (+) indicates low risk of bias; Red (-) indicates high risk of bias; and Yellow (?) indicates unclear risk of bias.</p

Study selection flow diagram.

<p>PRISMA flow diagram of search results following study section procedure assessing vitamin D supplementation and circulating inflammatory markers, glucose, and insulin sensitivity markers among randomized controlled trials (RCTs) of overweight and/or obese adults. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154215#pone.0154215.ref030" target="_blank">30</a>].</p

Mean differences for inflammatory biomarkers and glucose measures from randomized controlled trials.

<p>Mean differences for inflammatory biomarkers and glucose measures from randomized controlled trials.</p

Randomized controlled trial subject characteristics baseline measures.

<p>Randomized controlled trial subject characteristics baseline measures.</p

Randomized controlled trial study characteristics.

<p>Randomized controlled trial study characteristics.</p

Calibration plot demonstrating the relationship between predicted and observed risk among females.

<p>Calibration plot demonstrating the relationship between predicted and observed risk among females.</p

Descriptive characteristics of the cohort at baseline.

<p>Descriptive characteristics of the cohort at baseline.</p

Development and validation of a population based risk algorithm for obesity: The Obesity Population Risk Tool (OPoRT)

<div><p>Background</p><p>Given the dramatic rise in the prevalence of obesity, greater focus on prevention is necessary. We sought to develop and validate a population risk tool for obesity to inform prevention efforts.</p><p>Methods</p><p>We developed the Obesity Population Risk Tool (OPoRT) using the longitudinal National Population Health Survey and sex-specific Generalized Estimating Equations to predict the 10-year risk of obesity among adults 18 and older. The model was validated using a bootstrap approach accounting for the survey design. Model performance was measured by the Brier statistic, discrimination was measured by the C-statistic, and calibration was assessed using the Hosmer-Lemeshow Goodness of Fit Chi Square (HL χ²).</p><p>Results</p><p>Predictive factors included baseline body mass index, age, time and their interactions, smoking status, living arrangements, education, alcohol consumption, physical activity, and ethnicity. OPoRT showed good performance for males and females (Brier 0.118 and 0.095, respectively), excellent discrimination (C statistic ≥ 0.89) and achieved calibration (HL χ² <20).</p><p>Conclusion</p><p>OPoRT is a valid and reliable algorithm that can be applied to routinely collected survey data to estimate the risk of obesity and identify groups at increased risk of obesity. These results can guide prevention efforts aimed at reducing the population burden of obesity.</p></div

Obesity Population Risk Tool (OPoRT) function for males and females.

<p>Obesity Population Risk Tool (OPoRT) function for males and females.</p