Metabolic heterogeneity of obesity: role of adipose tissue

Accurate neuroimaging biomarkers to predict body mass index in adolescents: a longitudinal study

Obesity is often associated with cardiovascular complications. Adolescent obesity is a risk factor for cardiovascular disease in adulthood; thus, intensive management is warranted in adolescence. The brain state contributes to the development of obesity in addition to metabolic conditions, and hence neuroimaging is an important tool for accurately assessing an individual's risk of developing obesity. Here, we aimed to predict body mass index (BMI) progression in adolescents with neuroimaging features using machine learning approaches. From an open database, we adopted 76 resting-state functional magnetic resonance imaging (rs-fMRI) datasets from adolescents with longitudinal BMI scores. Functional connectivity analyses were performed on cortical surfaces and subcortical volumes. We identified baseline functional connectivity features in the prefrontal-, posterior cingulate-, sensorimotor-, and inferior parietal-cortices as significant determinants of BMI changes. A BMI prediction model based on the identified fMRI biomarkers exhibited a high accuracy (intra-class correlation = 0.98) in predicting BMI at the second visit (1~2 years later). The identified brain regions were significantly correlated with the eating disorder-, anxiety-, and depression-related scores. Based on these results, we concluded that these functional connectivity features in brain regions related to eating disorders and emotional processing could be important neuroimaging biomarkers for predicting BMI progression.

Clinical study on electroacupuncture for obese patients with binge eating disorder: A retrospective study

Binge eating disorder (BED) is a common dietary disorder among obese people. Obesity and eating disorders are related to mental health and physical health. At present, there is no definite and effective method for treatment in clinic. The curative effect of electroacupuncture on obesity is definite. Although there is no conclusive evidence to support its long-term benefits, electroacupuncture has been increasingly used in clinic. This retrospective study determined the prognosis and outcome of electro-acupuncture on obese patients with BED.One hundred forty-three patients with BED and obesity were found from 658 people who participated in the scientific experiment of obesity treatment in Nanjing Hospital of Traditional Chinese Medicine and Nanjing Brain Hospital from March 2015 to June 2018, and 84 patients (aged 18-40 years old) with valid data and uninterrupted treatment were found to be eligible for this retrospective study. According to the intervention methods, the patients were divided into electro-acupuncture combined with cognitive group (n = 32), cognitive therapy group (n = 28), and control group (n = 24). In this study, the 5th edition of Diagnosis and Statistics Manual of Mental Diseases, fasting blood glucose, fasting insulin, total cholesterol (TC), triglyceride, high-density lipoprotein, low-density lipoprotein, body fat rate, muscle mass, visceral index grade, nutrient intake (energy, protein, fat, carbohydrate), body weight, and weight changes before and after treatment were observed.Compared with the cognitive therapy group, negative emotion score, TC, triglyceride, high-density lipoprotein, waist circumference, BW, BMI, body fat percentage of the electroacupuncture combined with cognitive group were lower, while positive emotional scores were higher, and there were significant differences in negative emotional scores, TC, waist circumference and BMI (P < .05). The dietary energy and three major nutrients in the electroacupuncture combined with cognitive group were lower than those in the cognitive group and the blank group (P < .05).The current results suggest that electroacupuncture combined with cognitive therapy is more effective than cognitive therapy alone in treating obese patients with BED. Future prospective studies are necessary to further study the mechanism of electroacupuncture on the obese with BED.

Multivariate association between brain function and eating disorders using sparse canonical correlation analysis

Eating disorder is highly associated with obesity and it is related to brain dysfunction as well. Still, the functional substrates of the brain associated with behavioral traits of eating disorder are underexplored. Existing neuroimaging studies have explored the association between eating disorder and brain function without using all the information provided by the eating disorder related questionnaire but by adopting summary factors. Here, we aimed to investigate the multivariate association between brain function and eating disorder at fine-grained question-level information. Our study is a retrospective secondary analysis that re-analyzed resting-state functional magnetic resonance imaging of 284 participants from the enhanced Nathan Kline Institute-Rockland Sample database. Leveraging sparse canonical correlation analysis, we associated the functional connectivity of all brain regions and all questions in the eating disorder questionnaires. We found that executive- and inhibitory control-related frontoparietal networks showed positive associations with questions of restraint eating, while brain regions involved in the reward system showed negative associations. Notably, inhibitory control-related brain regions showed a positive association with the degree of obesity. Findings were well replicated in the independent validation dataset (n = 34). The results of this study might contribute to a better understanding of brain function with respect to eating disorder.

G Protein-Coupled Receptor Kinase 2 (GRK2) as a Potential Therapeutic Target in Cardiovascular and Metabolic Diseases

G protein-coupled receptor kinase 2 (GRK2) is a central signaling node involved in the modulation of many G protein-coupled receptors (GPCRs) and also displaying regulatory functions in other cell signaling routes. GRK2 levels and activity have been reported to be enhanced in patients or in preclinical models of several relevant pathological situations, such as heart failure, cardiac hypertrophy, hypertension, obesity and insulin resistance conditions, or non-alcoholic fatty liver disease (NAFLD), and to contribute to disease progression by a variety of mechanisms related to its multifunctional roles. Therefore, targeting GRK2 by different strategies emerges as a potentially relevant approach to treat cardiovascular disease, obesity, type 2 diabetes, or NAFLD, pathological conditions which are frequently interconnected and present as co-morbidities.

Structural and Functional Brain Connectivity Changes Between People With Abdominal and Non-abdominal Obesity and Their Association With Behaviors of Eating Disorders

Abdominal obesity is important for understanding obesity, which is a worldwide medical problem. We explored structural and functional brain differences in people with abdominal and non-abdominal obesity by using multimodal neuroimaging and up-to-date analysis methods. A total of 274 overweight people, whose body mass index exceeded 25, were enrolled in this study. Participants were divided into abdominal and non-abdominal obesity groups using a waist–hip ratio threshold of 0.9 for males and 0.85 for females. Structural and functional brain differences were assessed with diffusion tensor imaging and resting-state functional magnetic resonance imaging. Centrality measures were computed from structural fiber tractography, and static and dynamic functional connectivity matrices. Significant inter-group differences in structural and functional connectivity were found using degree centrality (DC) values. The associations between the DC values of the identified regions/networks and behaviors of eating disorder scores were explored. The highest association was achieved by combining DC values of the cerebral peduncle, anterior corona radiata, posterior corona radiata (from structural connectivity), frontoparietal network (from static connectivity), and executive control network (from dynamic connectivity) compared to the use of structural or functional connectivity only. Our results demonstrated the effectiveness of multimodal imaging data and found brain regions or networks that may be responsible for behaviors of eating disorders in people with abdominal obesity.

Informatics and machine learning to define the phenotype

INTRODUCTION

For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered: In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed.Approaches for phenotype refinement that can aid in more accurate characterization of traits are also discussed. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR)-derived data. Expert commentary: The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.

Lipid Oxidation Assessed by Indirect Calorimetry Predicts Metabolic Syndrome and Type 2 Diabetes

Purpose: Diabetes has been linked to an impaired ability to oxidize fatty acids. Fat oxidation can be assessed clinically by a respiratory quotient measurement during fasting. We hypothesized that a respiratory quotient might predict metabolic syndrome and type 2 diabetes onset. Methods: In this longitudinal study we used an existing database of 233 individuals who had complete nutritional and biochemical data at baseline and after 12-month follow-up. All participants underwent an indirect calorimetry to measure the respiratory quotient. We excluded participants with diabetes, metabolic syndrome, chronic diseases, and those who had changed food habits in the previous 3 months. Only 88 subjects met the inclusion criteria. Results: Two individuals developed type 2 diabetes and 10 metabolic syndrome after 1 year. Participants in the high respiratory quotient group (>0.91) had a higher incidence of metabolic syndrome/diabetes than those in the low quotient group (25 vs. 8% p = 0.04). In this group, mean basal respiratory quotient was 0.97 ± 0.04. In the high respiratory quotient group, Kaplan-Meier curves showed a greater probability of having metabolic syndrome/diabetes than those in the low respiratoryquotient group (log Rank χ²-test = 8.44; p = 0.004). A multivariable Cox proportional hazards model demonstrated that energy expenditure and weight increase did not predict metabolic syndrome/diabetes [HR (95% CI) = 1 (0.996-1.005), p = 0.86 and 3.9 (0.407-38.061), p = 0.23, respectively). Conclusions: A greater probability of metabolic syndrome/diabetes was found in individuals with a basal respiratory quotient of >0.91 than in those with a respiratoryquotient of ≤ 0.91 after 1 year. In the short-term anthropometric measurements and their variation overtime were not correlated with metabolic syndrome/diabetes.

Adiposity-Dependent Regulatory Effects on Multi-tissue Transcriptomes

Obesity is a global epidemic that is causally associated with a range of diseases, including type 2 diabetes and cardiovascular disease, at the population-level. However, there is marked heterogeneity in obesity-related outcomes among individuals. This might reflect genotype-dependent responses to adiposity. Given that adiposity, measured by BMI, is associated with widespread changes in gene expression and regulatory variants mediate the majority of known complex trait loci, we sought to identify gene-by-BMI (G × BMI) interactions on the regulation of gene expression in a multi-tissue RNA-sequencing (RNA-seq) dataset from the TwinsUK cohort (n = 856). At a false discovery rate of 5%, we identified 16 cis G × BMI interactions (top cis interaction: CHURC1, rs7143432, p = 2.0 × 10(-12)) and one variant regulating 53 genes in trans (top trans interaction: ZNF423, rs3851570, p = 8.2 × 10(-13)), all in adipose tissue. The interactions were adipose-specific and enriched for variants overlapping adipocyte enhancers, and regulated genes were enriched for metabolic and inflammatory processes. We replicated a subset of the interactions in an independent adipose RNA-seq dataset (deCODE genetics, n = 754). We also confirmed the interactions with an alternate measure of obesity, dual-energy X-ray absorptiometry (DXA)-derived visceral-fat-volume measurements, in a subset of TwinsUK individuals (n = 682). The identified G × BMI regulatory effects demonstrate the dynamic nature of gene regulation and reveal a functional mechanism underlying the heterogeneous response to obesity. Additionally, we have provided a web browser allowing interactive exploration of the dataset, including of association between expression, BMI, and G × BMI regulatory effects in four tissues.

Molecular physiopathology of obesity-related diseases: multi-organ integration by GRK2

Obesity is a worldwide problem that has reached epidemic proportions both in developed and developing countries. The excessive accumulation of fat poses a risk to health since it favours the development of metabolic alterations including insulin resistance and tissue inflammation, which further contribute to the progress of the complex pathological scenario observed in the obese. In this review we put together the different outcomes of fat accumulation and insulin resistance in the main insulin-responsive tissues, and discuss the role of some of the key molecular routes that control disease progression both in an organ-specific and also in a more systemic manner. In particular, we focus on the importance of studying the integrated regulation of different organs and pathways that contribute to the global pathophysiology of this condition with a specific emphasis on the role of emerging key molecular nodes such as the G protein-coupled receptor kinase 2 (GRK2) signalling hub.