Computational approaches to predicting treatment response to obesity using neuroimaging

Body mass, neuro-hormonal stress processing, and disease activity in lean to obese people with multiple sclerosis

Overweight and obesity can worsen disease activity in multiple sclerosis (MS). Although psychobiological stress processing is increasingly recognized as important obesity factor that is tightly connected to proinflammatory metabolic hormones and cytokines, its role for MS obesity remains unexplored. Consequently, we investigated the interplay between body mass index (BMI), neural stress processing (functional connectivity, FC), and immuno-hormonal stress parameters (salivary cortisol and T cell glucocorticoid [GC] sensitivity) in 57 people with MS (six obese, 19 over-, 28 normal-, and four underweight; 37 females, 46.4 ± 10.6 years) using an Arterial-Spin-Labeling MRI task comprising a rest and stress stage, along with quantitative PCR. Our findings revealed significant positive connections between BMI and MS disease activity (i.e., higher BMI was accompanied by higher relapse rate). BMI was positively linked to right supramarginal gyrus and anterior insula FC during rest and negatively to right superior parietal lobule and cerebellum FC during stress. BMI showed associations with GC functioning, with higher BMI associated with lower CD8+ FKBP4 expression and higher CD8+ FKBP5 expression on T cells. Finally, the expression of CD8+ FKBP4 positively correlated with the FC of right supramarginal gyrus and left superior parietal lobule during rest. Overall, our study provides evidence that body mass is tied to neuro-hormonal stress processing in people with MS. The observed pattern of associations between BMI, neural networks, and GC functioning suggests partial overlap between neuro-hormonal and neural-body mass networks. Ultimately, the study underscores the clinical importance of understanding multi-system crosstalk in MS obesity.

A review of the application of deep learning in obesity: From early prediction aid to advanced management assistance

BACKGROUND AND AIMS

Obesity is a chronic disease which can cause severe metabolic disorders. Machine learning (ML) techniques, especially deep learning (DL), have proven to be useful in obesity research. However, there is a dearth of systematic reviews of DL applications in obesity. This article aims to summarize the current trend of DL usage in obesity research.

METHODS

An extensive literature review was carried out across multiple databases, including PubMed, Embase, Web of Science, Scopus, and Medline, to collate relevant studies published from January 2018 to September 2023. The focus was on research detailing the application of DL in the context of obesity. We have distilled critical insights pertaining to the utilized learning models, encompassing aspects of their development, principal results, and foundational methodologies.

RESULTS

Our analysis culminated in the synthesis of new knowledge regarding the application of DL in the context of obesity. Finally, 40 research articles were included. The final collection of these research can be divided into three categories: obesity prediction (n = 16); obesity management (n = 13); and body fat estimation (n = 11).

CONCLUSIONS

This is the first review to examine DL applications in obesity. It reveals DL's superiority in obesity prediction over traditional ML methods, showing promise for multi-omics research. DL also innovates in obesity management through diet, fitness, and environmental analyses. Additionally, DL improves body fat estimation, offering affordable and precise monitoring tools. The study is registered with PROSPERO (ID: CRD42023475159).

Biobehavioral susceptibility for obesity in childhood: Behavioral, genetic and neuroimaging studies of appetite

Modern food environments are conducive to overeating and weight gain, but not everyone develops obesity. One reason for this may be that individuals differ in appetitive characteristics, or traits, that manifest early in life and go on to influence their behavioral susceptibility to gain and maintain excess weight. Classic studies showing that eating behavior in children can be measured by behavioral paradigms such as tests of caloric compensation and eating in the absence of hunger inspired the development and validation of psychometric instruments to assess appetitive characteristics in children and infants. A large body of evidence now suggests that food approach traits increase obesity risk, while food avoidant traits, such as satiety responsiveness, decrease obesity risk. Twin studies and genetic association studies have demonstrated that appetitive characteristics are heritable, consistent with a biological etiology. However, family environment factors are also influential, with mounting evidence suggesting that genetic and environmental risk factors interact and correlate with consequences for child eating behavior and weight. Further, neuroimaging studies are revealing that individual differences in responses to visual food cues, as well as to small tastes and larger amounts of food, across a number of brain regions involved in reward/motivation, cognitive control and other functions, may contribute to individual variation in appetitive behavior. Growing evidence also suggests that variation on psychometric measures of appetite is associated with regional differences in brain structure, and differential patterns of resting state functional connectivity. Large prospective studies beginning in infancy promise to enrich our understanding of neural and other biological underpinnings of appetite and obesity development in early life, and how the interplay between genetic and environmental factors affects appetitive systems. The biobehavioral susceptibility model of obesity development and maintenance outlined in this narrative review has implications for prevention and treatment of obesity in childhood.

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

The present study aims to provide a narrative review of the molecular mechanisms of Western diet-induced obesity and obesity-related carcinogenesis. A literature search of the Cochrane Library, Embase and Pubmed databases, Google Scholar and the grey literature was conducted. Most of the molecular mechanisms that induce obesity are also involved in the twelve Hallmarks of Cancer, with the fundamental process being the consumption of a highly processed, energy-dense diet and the deposition of fat in white adipose tissue and the liver. The generation of crown-like structures, with macrophages surrounding senescent or necrotic adipocytes or hepatocytes, leads to a perpetual state of chronic inflammation, oxidative stress, hyperinsulinaemia, aromatase activity, activation of oncogenic pathways and loss of normal homeostasis. Metabolic reprogramming, epithelial mesenchymal transition, HIF-1α signalling, angiogenesis and loss of normal host immune-surveillance are particularly important. Obesity-associated carcinogenesis is closely related to metabolic syndrome, hypoxia, visceral adipose tissue dysfunction, oestrogen synthesis and detrimental cytokine, adipokine and exosomal miRNA release. This is particularly important in the pathogenesis of oestrogen-sensitive cancers, including breast, endometrial, ovarian and thyroid cancer, but also 'non-hormonal' obesity-associated cancers such as cardio-oesophageal, colorectal, renal, pancreatic, gallbladder and hepatocellular adenocarcinoma. Effective weight loss interventions may improve the future incidence of overall and obesity-associated cancer.

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

The study aimed to perform a systematic review and meta-analysis of the evidence for the prevention of future cancers following bariatric surgery. A systematic literature search of the Cochrane Library, Embase, Scopus, Web of Science and PubMed databases (2007–2023), Google Scholar and grey literature was conducted. A meta-analysis was performed using the inverse variance method and random effects model. Thirty-two studies involving patients with obesity who received bariatric surgery and control patients who were managed with conventional treatment were included. The meta-analysis suggested bariatric surgery was associated with a reduced overall incidence of cancer (RR 0.62, 95% CI 0.46–0.84, p < 0.002), obesity-related cancer (RR 0.59, 95% CI 0.39–0.90, p = 0.01) and cancer-associated mortality (RR 0.51, 95% CI 0.42–0.62, p < 0.00001). In specific cancers, bariatric surgery was associated with reduction in the future incidence of hepatocellular carcinoma (RR 0.35, 95% CI 0.22–0.55, p < 0.00001), colorectal cancer (RR 0.63, CI 0.50–0.81, p = 0.0002), pancreatic cancer (RR 0.52, 95% CI 0.29–0.93, p = 0.03) and gallbladder cancer (RR 0.41, 95% CI 0.18–0.96, p = 0.04), as well as female specific cancers, including breast cancer (RR 0.56, 95% CI 0.44–0.71, p < 0.00001), endometrial cancer (RR 0.38, 95% CI 0.26–0.55, p < 0.00001) and ovarian cancer (RR 0.45, 95% CI 0.31–0.64, p < 0.0001). There was no significant reduction in the incidence of oesophageal, gastric, thyroid, kidney, prostate cancer or multiple myeloma after bariatric surgery as compared to patients with morbid obesity who did not have bariatric surgery. Obesity-associated carcinogenesis is closely related to metabolic syndrome; visceral adipose dysfunction; aromatase activity and detrimental cytokine, adipokine and exosomal miRNA release. Bariatric surgery results in long-term weight loss in morbidly obese patients and improves metabolic syndrome. Bariatric surgery may decrease future overall cancer incidence and mortality, including the incidence of seven obesity-related cancers.