Graduate Program in Translational Biology, Medicine, and Health, Virginia Tech.
Fralin Biomedical Research Institute at Virginia Tech Carilion, Virginia Tech.
Behav Neurosci. 2021 Jun;135(3):426-442. doi: 10.1037/bne0000385.
Obesity is a worldwide epidemic that is on the rise, with approximately 30% of the world population classified as either overweight or obese. The United States has some of the highest rates of obesity, and in most countries in the world, obesity now poses more of a serious health concern than malnutrition. Obesity is a chronic, relapsing disorder that is both preventable and treatable; however, traditional interventions that target eating less and exercising more have low success rates, especially in the long term. Therefore, identifying the neurobehaviors that predict obesity is important to help identify targets to decrease BMI and improve obesity outcomes. Using the Competing Neurobehavioral Decisions System (CNDS) Theory, we hypothesized that individuals with obesity compared to individuals without obesity would display neurobehaviors marked by a hyperactive impulsive system and a hypoactive executive system. To test this hypothesis, we collected data from a battery of self-reported measures and neurocognitive assessments through Amazon Mechanical Turk from n = 178 obese (BMI ≥ 30) and n = 198 nonobese controls who were weight stable for the past 3 months. We found that compared to the nonobese control group, individuals with obesity showed heightened delay discounting (a marker of CNDS imbalance), impaired motivation, poor self-image, decreased affective state, and impaired executive function. Using a Bayesian network approach, we established a neurobehavioral model that predicts obesity with 64.4% accuracy and indicates an imbalance between impulsive and executive neural systems. Results from our study suggest that interventions targeting neurobehaviors may be integral to help improve obesity outcomes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
肥胖是一种全球性的流行疾病，且呈上升趋势，全球约有 30%的人口超重或肥胖。美国的肥胖率位居前列，在世界上大多数国家，肥胖现在比营养不良更成 为一个严重的健康问题。肥胖是一种可以预防和治疗的慢性、复发性疾病；然而，以少吃多运动为目标的传统干预措施成功率较低，尤其是在长期来看。因此，确定预测肥胖的神经行为对于帮助确定减少 BMI 和改善肥胖结果的目标非常重要。本研究使用竞争神经行为决策系统（CNDS）理论，假设与非肥胖者相比，肥胖者会表现出以过度活跃的冲动系统和不活跃的执行系统为特征的神经行为。为了验证这一假设，我们通过亚马逊 Mechanical Turk 从 n = 178 名肥胖（BMI≥30）和 n = 198 名体重稳定在过去 3 个月内的非肥胖对照组中收集了一系列自我报告的测量和神经认知评估的数据。我们发现，与非肥胖对照组相比，肥胖个体表现出更高的延迟折扣（CNDS 失衡的标志）、动机受损、自我形象差、情绪状态下降和执行功能受损。使用贝叶斯网络方法，我们建立了一个可以预测肥胖的神经行为模型，准确率为 64.4%，表明冲动和执行神经系统之间存在不平衡。我们的研究结果表明，针对神经行为的干预措施可能对于改善肥胖结果至关重要。
