Clinical Unit in Cambridge, GlaxoSmithKline, Cambridge University Hospital NHS Trust, Cambridge, UK.
Clin Pharmacol Ther. 2012 Jun;91(6):1027-34. doi: 10.1038/clpt.2011.333. Epub 2012 Feb 15.
To develop statistical models for predicting weight loss and regain, we analyzed the phenotypic responses in an outpatient study of 60 obese subjects randomized to one of three 12-week interventions, diet (-600 kcal) alone, diet with exercise, and diet with sibutramine. This was followed by 12 weeks of observation. The best of the "baseline covariates" models was one that incorporated intervention group and baseline homeostasis model assessment-estimated insulin resistance (HOMA(IR)). It predicted week 12 weight change with R(2) of 0.38 and root mean square error (√MSE) of 2.92 kg. An alternative model incorporating baseline fat mass plus change in weight and HOMA(IR) at week 4 improved the prediction (R(2), 0.67, √MSE, 2.19 kg). We could not identify a satisfactory model to predict weight regain. We conclude that prediction of weight loss over 12 weeks is significantly improved when short-term weight change is incorporated into the model. This information could be utilized to forecast the success of a weight-loss program and to motivate and contribute to innovative designing of obesity trials.
为了开发预测体重减轻和反弹的统计模型，我们分析了一项针对 60 名肥胖患者的门诊研究的表型反应，这些患者被随机分为三组 12 周干预措施之一：单独节食（-600 卡路里）、节食加运动和节食加西布曲明。随后进行了 12 周的观察。“基线协变量”模型中最好的一个是包含干预组和基线稳态模型评估估计的胰岛素抵抗（HOMA(IR)）的模型。它预测第 12 周的体重变化，R²为 0.38，均方根误差（√MSE）为 2.92 公斤。另一个包含基线脂肪量和第 4 周体重变化以及 HOMA(IR)的模型改善了预测（R²，0.67，√MSE，2.19 公斤）。我们无法确定一个满意的模型来预测体重反弹。我们的结论是，当将短期体重变化纳入模型时，对 12 周内的体重减轻预测会显著改善。这些信息可用于预测减肥计划的成功，并为肥胖试验的创新设计提供动力和帮助。
