360-Degree Perspectives on Obesity

Copper biomarkers and their relationship with parameters of insulin resistance in obese women

Some studies have demonstrated the involvement of high concentrations of copper in the manifestation of insulin resistance in individuals with obesity. The objective of this study was to evaluate the copper nutritional status and its relationship with parameters of glycemic control in women with obesity. An observational case-control clinical study involving 203 women aged between 20 and 50 years, divided into two groups: obese (n = 84) and eutrophic (n = 119). Body weight, height and waist, hip and neck circumferences, dietary copper intake, copper biomarkers, determine ceruloplasmin activity and glycemic control parameters were measured. It was observed that women with obesity had higher copper concentrations in plasma and lower concentrations in erythrocytes when compared to the control group. Analysis of glycemic control parameters revealed a statistically significant difference in fasting blood glucose (p < 0.05) between groups. The study identified a significant positive correlation between plasma copper and fasting insulin values and the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) index (p < 0.05). The results of this study demonstrate that obese women have high copper concentrations in plasma and lower concentrations in erythrocytes. Furthermore, the significant positive correlation between plasma copper and fasting insulin and HOMA-IR index suggests the influence of this mineral on glycemic control parameters in obese women.

Effect of long-term exercise on circulating ghrelin in overweight and obese individuals: a systematic review and meta-analysis

Objective

Ghrelin, also known as the "hunger hormone," is a pivotal hormone in controlling appetite, and it is the only known gastrointestinal hormone that promotes food intake, contributing to the regulation of energy balance and body weight. However, studies on the long-term effects of exercise on ghrelin levels in obese populations have shown conflicting results. This study aims to summarize RCT experiments exploring changes in ghrelin levels following long-term exercise in obese or overweight individuals through meta-analysis.

Methods

This study employed meta-analytical techniques, searching databases such as PubMed, Web of Science, and EMBASE, to gather research on exercise and ghrelin. The quality of the studies was assessed according to the Cochrane Handbook standards, and data analysis for ghrelin, BMI, and weight was performed using RevMan 5.4 and Stata 16.0 software. A total of 13 interventions involving 944 participants were included to systematically investigate the regulatory effects of exercise on ghrelin levels in obese and overweight individuals. Meta-analytical results were calculated using standardized mean differences (SMDs).

Results

Exercise interventions significantly increased ghrelin levels (SMD =1.16, 95% CI = 0.52 to 1.80, p < 0.0001), with high inter-study heterogeneity (I 2 = 90%). Subgroup analysis suggested that RT and AE + RT were more effective than AE. For BMI, exercise led to a significant reduction (SMD = -0.43, 95% CI = -0.69 to -0.16, p = 0.002), with low heterogeneity (I 2 = 21%). Similarly, exercise significantly reduced weight (SMD = -0.54, 95% CI = -0.98 to -0.11, p = 0.01), though with high heterogeneity (I 2 = 75%). These results suggest exercise effectively improves ghrelin levels, BMI, and weight.

Conclusion

Prolonged exercise interventions demonstrated a statistically significant effect on ghrelin levels. This indicates that exercise interventions can elevate ghrelin levels, which may be associated with reductions in BMI and weight.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, CRD42024588259.

Molecular Pathways Linking High-Fat Diet and PM2.5 Exposure to Metabolically Abnormal Obesity: A Systematic Review and Meta-Analysis

Obesity, influenced by environmental pollutants, can lead to complex metabolic disruptions. This systematic review and meta-analysis examined the molecular mechanisms underlying metabolically abnormal obesity caused by exposure to a high-fat diet (HFD) and fine particulate matter (PM2.5). Following the PRISMA guidelines, articles from 2019 to 2024 were gathered from Scopus, Web of Science, and PubMed, and a random-effects meta-analysis was performed, along with subgroup analyses and pathway enrichment analyses. This study was registered in the Open Science Framework. Thirty-three articles, mainly case-control studies and murine models, were reviewed, and they revealed that combined exposure to HFD and PM2.5 resulted in the greatest weight gain (82.835 g, p = 0.048), alongside increases in high-density lipoproteins, insulin, and the superoxide dismutase. HFD enriched pathways linked to adipocytokine signaling in brown adipose tissue, while PM2.5 impacted genes associated with fat formation. Both exposures downregulated protein metabolism pathways in white adipose tissue and activated stress-response pathways in cardiac tissue. Peroxisome proliferator-activated receptor and AMP-activated protein kinase signaling pathways in the liver were enriched, influencing non-alcoholic fatty liver disease. These findings highlight that combined exposure to HFD and PM2.5 amplifies body weight gain, oxidative stress, and metabolic dysfunction, suggesting a synergistic interaction with significant implications for metabolic health.

Epigenetic regulation is involved in reversal of obesity

Epigenetic processes play a crucial role in mediating the impact of environmental energetic challenges, from overconsumption to starvation. Over-nutrition of energy-dense foods and sedentary lifestyles contribute to the development of obesity, characterized by excessive fat storage and impaired metabolic signaling, stemming from disrupted brain signaling. Conversely, dieting and physical activity facilitate body weight rebalancing and trigger adaptive neural responses. These adaptations involve the upregulation of neurogenesis, synaptic plasticity and optimized brain function and energy homeostasis, balanced hormone signaling, normal metabolism, and reduced inflammation. The transition of the brain from a maladaptive to an adaptive state is partially guided by epigenetic mechanisms. While epigenetic mechanisms underlying obesity-related brain changes have been described, their role in mediating the reversal of maladaptation/obesity through lifestyle interventions remains less explored. This review focuses on elucidating epigenetic mechanisms involved in hypothalamic adaptations induced by lifestyle interventions. Given that lifestyle interventions are widely prescribed and accessible approaches for weight loss and maintenance, it is our challenge to uncover epigenetic mechanisms moderating these hypothalamic-functional beneficial changes.

The Therapeutic Effectiveness of Laparoscopic Sleeve Gastrectomy Among Individuals with Low BMI Obesity (30-35 Kg/m2) and the Relationship of BMI to Weight Loss

Purpose

Investigating the therapeutic efficacy of Laparoscopic Sleeve Gastrectomy (LSG) in low BMI (30-35 kg/m2) patients with obesity, and exploring the correlation between patients' preoperative BMI and postoperative weight loss.

Methods

Comparing the weight loss, remission of comorbidities, occurrence of complications, and quality of life among the different BMI patients who underwent LSG. Analyzing the relationship between BMI and percentage of excess weight loss (%EWL) by using Spearman correlation analysis and linear regression analysis.

Results

The %EWL at 12 months after the surgical procedure was (104.26±16.41)%, (90.36±9.98)%, and (78.30±14.64)% for patients with Class I, II, and III obesity, respectively, P<0.05. Spearman correlation coefficients between %EWL and BMI at 1, 3, 6, and 12 months after surgery were R=-0.334 (P<0.001), R=-0.389 (P<0.001), and R=-0.442 (P<0.001), R=-0.641 (P<0.001), respectively. The remission of hypertension, diabetes and dyslipidaemia did not differ significantly between groups (P>0.05).

Conclusion

Individuals with obesity for varying BMI can experience favorable outcomes following LSG surgery. It is advisable to consider LSG treatment for patients with Class I obesity.

The 26RFa (QRFP)/GPR103 Neuropeptidergic System: A Key Regulator of Energy and Glucose Metabolism

BACKGROUND

Obesity and type 2 diabetes are strongly associated pathologies, currently considered as a worldwide epidemic problem. Understanding the mechanisms that drive the development of these diseases would enable to develop new therapeutic strategies for their prevention and treatment. Particularly, the role of the brain in energy and glucose homeostasis has been studied for 2 decades. In specific, the hypothalamus contains well-identified neural networks that regulate appetite and potentially also glucose homeostasis. A new concept has thus emerged, suggesting that obesity and diabetes could be due to a dysfunction of the same, still poorly understood, neural networks.

SUMMARY

The neuropeptide 26RFa (also termed QRFP) belongs to the family of RFamide regulatory peptides and has been identified as the endogenous ligand of the human G protein-coupled receptor GPR103 (QRFPR). The primary structure of 26RFa is strongly conserved during vertebrate evolution, suggesting its crucial roles in the control of vital functions. Indeed, the 26RFa/GPR103 peptidergic system is reported to be involved in the control of various neuroendocrine functions, notably the control of energy metabolism in which it plays an important role, both centrally and peripherally, since 26RFa regulates feeding behavior, thermogenesis and lipogenesis. Moreover, 26RFa is reported to control glucose homeostasis both peripherally, where it acts as an incretin, and centrally, where the 26RFa/GPR103 system relays insulin signaling in the brain to control glucose metabolism.

KEY MESSAGES

This review gives a comprehensive overview of the role of the 26RFa/GPR103 system as a key player in the control of energy and glucose metabolism. In a pathophysiological context, this neuropeptidergic system represents a prime therapeutic target whose mechanisms are highly relevant to decipher.

Identification of additional body weight QTLs in the Berlin Fat Mouse BFMI861 lines using time series data

The Berlin Fat Mouse Inbred line (BFMI) is a model for obesity and metabolic syndrome. The sublines BFMI861-S1 and BFMI861-S2 differ in weight despite high genetic similarity and a shared obesity-related locus. This study focused on identifying additional body weight quantitative trait loci (QTLs) by analyzing weekly weight measurements in a male population of the advanced intercross line BFMI861-S1 x BFMI861-S2. QTL analysis, utilizing 200 selectively genotyped mice (GigaMUGA) and 197 males genotyped for top SNPs, revealed a genome-wide significant QTL on Chr 15 (68.46 to 81.40 Mb) for body weight between weeks 9 to 20. Notably, this QTL disappeared (weeks 21 to 23) and reappeared (weeks 24 and 25) coinciding with a diet change. Additionally, a significant body weight QTL on Chr 16 (3.89 to 22.79 Mb) was identified from weeks 6 to 25. Candidate genes, including Gpt, Cbx6, Apol6, Apol8, Sun2 (Chr 15) and Trap1, Rrn3, Mapk1 (Chr 16), were prioritized. This study unveiled two additional body weight QTLs, one of which is novel and responsive to diet changes. These findings illuminate genomic regions influencing weight in BFMI and emphasize the utility of time series data in uncovering novel genetic factors.

Multicomponent (bio)markers for obesity risk prediction: a scoping review protocol

INTRODUCTION

Despite international efforts, the number of individuals struggling with obesity is still increasing. An important aspect of obesity prevention relates to identifying individuals at risk at early stage, allowing for timely risk stratification and initiation of countermeasures. However, obesity is complex and multifactorial by nature, and one isolated (bio)marker is unlikely to enable an optimal risk stratification and prognosis for the individual; rather, a combined set is required. Such a multicomponent interpretation would integrate biomarkers from various domains, such as classical markers (eg, anthropometrics, blood lipids), multiomics (eg, genetics, proteomics, metabolomics), lifestyle and behavioural attributes (eg, diet, physical activity, sleep patterns), psychological traits (mental health status such as depression) and additional host factors (eg, gut microbiota diversity), also by means of advanced interpretation tools such as machine learning. In this paper, we will present a protocol that will be employed for a scoping review that attempts to summarise and map the state-of-the-art in the area of multicomponent (bio)markers related to obesity, focusing on the usability and effectiveness of such biomarkers.

METHODS AND ANALYSIS

PubMed, Scopus, CINAHL and Embase databases will be searched using predefined key terms to identify peer-reviewed articles published in English until January 2024. Once downloaded into EndNote for deduplication, CADIMA will be employed to review and select abstracts and full-text articles in a two-step procedure, by two independent reviewers. Data extraction will then be carried out by several independent reviewers. Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews and Peer Review of Electronic Search Strategies guidelines will be followed. Combinations employing at least two biomarkers from different domains will be mapped and discussed.

ETHICS AND DISSEMINATION

Ethical approval is not required; data will rely on published articles. Findings will be published open access in an international peer-reviewed journal. This review will allow guiding future directions for research and public health strategies on obesity prevention, paving the way towards multicomponent interventions.