Neuroimaging predictors of bariatric surgical outcomes

项目摘要/摘要 减肥手术是病态肥胖患者的一个重要治疗选择，这些患者无法通过减肥 节食和锻炼。尽管采取了干预措施，20%-50%的患者要么未能达到目标量的体重减轻，要么 恢复最初失去的体重。预测体重减轻程度的尝试并不多见。 成功和没有一个具有长期(&gt；2年)的可靠性。此外，目前还严重缺乏对 预测术后1年或2年后的体重下降，以及除体重下降外的其他结果，包括 肥胖人群中常见的合并症。我们在45名患者中的试验数据表明，个人 功能磁共振成像(FMRI)测量的手术前神经活动的差异可靠地解释了S 33%的 术后1年内体重减轻的差异，以及多方面结果测量的50%以上，FAR 表现好于许多其他指标。这些大脑激活预报器所涉及的区域与 一个既强调消费冲动(“现在”的神经回路)又强调欲望调节的理论模型 和自我控制(一种“后来”的回路)。我们的中心假设是这些神经中的个体差异 这些途径对维持减肥和实现其他关键健康结果的能力产生了强大的影响。 该项目寻求在更长的时间范围内复制和完善这一模型，并评估其预测效用 对于与体重相关的关键健康结果。 我们建议复制从我们的fMRI飞行员数据中获得的预测体重下降的模型，然后再 探索其对卡路里摄入量、活动水平、肝脏脂肪、血红蛋白A1c、血脂、 血压和空腹血糖在N=150的新的独立队列中相继同意， 研究1-3年的外科袖状胃切除术(SG)患者。我们将跟踪试点队列长达7年 而新的队列持续3年或更长时间，以确定在AIM 1中复制的预测因子是否保持其长期 预测能力，特别是在补充了非脑成像变量并使用更大的 纵向数据集。我们将使用成像和非成像数据来开发多变量统计模型 将能量平衡、功能磁共振成像和化验值与目标1中描述的变量结合起来，以帮助 手术后预后的独立预测因子与后果。以帮助分离扫描到扫描的可变性 根据手术后真实的与轨迹相关的大脑变化，我们将招募N=20名肥胖受试者，他们不会 接受减肥手术，并与我们的上述SG受试者单独匹配。最后，就以下方面而言 翻译潜力，我们将评估几个相关的，非fMRI认知测试，探索“现在与。 后来，我们的磁共振成像范例的功能域可能有可能作为临床上的替代测试 在术前患者评估中帮助预测SG成功结局的可能性的实践。 这些研究在理解神经模式在体重中的作用方面都具有重要意义 在管理和帮助患者实现针对特定人群的减肥健康成果方面。

PROJECT SUMMARY/ABSTRACT Bariatric surgery is an important treatment option for morbidly obese patients who fail to lose weight through diet and exercise. Despite intervention, 20-50% of patients either fail to lose targeted amounts of weight or regain weight that was lost initially. Attempts at predicting the degree of weight loss have had only modest success and none have long term (>2 year) reliability. Moreover, there is a serious absence of research to predict weight loss beyond the 1st or 2nd year post-surgery and for outcomes other than weight loss including comorbidities common in the bariatric population. Our pilot data in 45 patients suggest that individual differences on pre-surgical neural activity measured with functional MRI (fMRI) reliably explains s 33% of the variance in weight loss up to 1 year post surgery, and over 50% of a multifaceted outcome measure, far outperforming many other indicators. These brain activation predictors implicate regions that closely conform to a theoretical model emphasizing both consummatory urges (a “Now” neural circuit) vs. regulation of craving and self-control (a “Later” circuit). Our central hypothesis is that individual differences in these neural pathways exert a powerful effect on the ability to sustain weight loss and achieve other key health outcomes. This project seeks to replicate and refine this model over a longer timeframe and to assess its predictive utility for key weight-related health outcomes. We propose to replicate the model derived from our fMRI pilot data predicting weight loss and secondarily to explore its predictive utility for changes in calorie intake, activity levels, liver fat, hemoglobin A1c, plasma lipids, blood pressure, and fasting glucose in a new, independent cohort of N=150 successively consenting, pre- surgical sleeve gastrectomy (SG) patients in study years 1-3. We will follow the pilot cohort for up to 7 years and the new cohort for 3 or more years to determine if predictors replicated in Aim 1 retain their long-term predictive power, particularly when supplemented with non-brain imaging variables and using a larger longitudinal dataset. We will use imaging and non-imaging data to develop multivariate statistical models incorporating energy balance, fMRI, and laboratory values with the variables described in Aim 1 to help to separate predictors vs. consequences of post-surgical outcomes. To help separate scan-to-scan variability from true post-surgical, trajectory-related brain changes, we will enroll N=20 obese subjects who will not undergo bariatric surgery, and are individually matched with our above SG subjects. Finally, in terms of translational potential, we will evaluate whether several related, non-fMRI cognitive tests that probe "Now vs. Later" functional domains to our MRI paradigms might have the potential to act as surrogate tests in clinical practice that help predict the likelihood of successful SG outcome during pre-surgical patient assessment. These studies are highly significant both in terms of understanding the role of neural patterns in weight regulation and in helping patients achieve bariatric population-specific health outcomes.