Computational approaches to predicting treatment response to obesity using neuroimaging

Abstract

Obesity is a worldwide disease associated with multiple severe adverse consequences and comorbid conditions. While an increased body weight is the defining feature in obesity, etiologies, clinical phenotypes and treatment responses vary between patients. These variations can be observed within individual treatment options which comprise lifestyle interventions, pharmacological treatment, and bariatric surgery. Bariatric surgery can be regarded as the most effective treatment method. However, long-term weight regain is comparably frequent even for this treatment and its application is not without risk. A prognostic tool that would help predict the effectivity of the individual treatment methods in the long term would be essential in a personalized medicine approach. In line with this objective, an increasing number of studies have combined neuroimaging and computational modeling to predict treatment outcome in obesity. In our review, we begin by outlining the central nervous mechanisms measured with neuroimaging in these studies. The mechanisms are primarily related to reward-processing and include “incentive salience” and psychobehavioral control. We then present the diverse neuroimaging methods and computational prediction techniques applied. The studies included in this review provide consistent support for the importance of incentive salience and psychobehavioral control for treatment outcome in obesity. Nevertheless, further studies comprising larger sample sizes and rigorous validation processes are necessary to answer the question of whether or not the approach is sufficiently accurate for clinical real-world application.

Systematic Review and Meta-Analysis of the Impact of Bariatric Surgery on Future Cancer Risk

“…Specific risk factors for postoperative weight regain include time after BMS; low physical activity; impulsive, disinhibited or maladaptive eating behaviour; low protein/highly refined carbohydrate dense meals; poor patient compliance; limbic/hypothalamic μ-opiate receptor (MOR) phenotype and food reward; larger gastric volume following sleeve gastrectomy (252.7 mL vs. 148.5 mL) and the gastro-jejunal stomal diameter after RYGB. Predictors of sustained weight loss after BMS include postoperative HDL levels, fruit and zinc consumption, postprandial GLP-1 levels, patient self-esteem, social support and willingness to engage in physical activity [ 103 , 104 ].…”

Molecular Mechanisms of Western Diet-Induced Obesity and Obesity-Related Carcinogenesis—A Narrative Review

“…Exercise may prevent some of the adverse effects of a high-fat Western diet, including fat deposition, glucose intolerance and leptin release. Maintaining a lean WAT phenotype stimulates adiponectin release, which activates AMPK and blocks ROS-mediated pathways, including oxidized LDL, lectin-type oxLDL receptor (LOX-1)/NF-κB activation and the formation of foamy macrophages [ 178 , 179 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 203 ]. This prevents atherosclerosis, cellular senescence and the development of cancers driven by hypersecretion of inflammatory adipokines, including leptin.…”

Foreword to the special issue on the neuroscience of obesity and related disorders

“…Emphasis is given to how ultra-high field 7-Tesla (7T) fMRI may be leveraged to shed light on the functional alterations in these subcortical systems and suggestions on how to investigate interactions of these systems with endocannabinoids and neuropeptides are offered. The of neuroimaging is taken a step further in Kozarzewski's work advocating for the combined use of neuroimaging data with computational approaches to predict treatment response in obesity [8]. The authors argue that such an approach could deliver a prognostic tool to help predict the effectiveness of individual treatment methods so as to improve personalized medicine for patients seeking treatment.…”