Obesity is a chronic progressive relapsing disease of particular interest for internal medicine

Female obesity: clinical and psychological assessment toward the best treatment

Obesity is a heterogeneous condition which results from complex interactions among sex/gender, sociocultural, environmental, and biological factors. Obesity is more prevalent in women in most developed countries, and several clinical and psychological obesity complications show sex-specific patterns. Females differ regarding fat distribution, with males tending to store more visceral fat, which is highly correlated to increased cardiovascular risk. Although women are more likely to be diagnosed with obesity and appear more motivated to lose weight, as confirmed by their greater representation in clinical trials, males show better outcomes in terms of body weight and intra-abdominal fat loss and improvements in the metabolic risk profile. However, only a few relatively recent studies have investigated gender differences in obesity, and sex/gender is rarely considered in the assessment and management of the disease. This review summarizes the evidence of gender differences in obesity prevalence, contributing factors, clinical complications, and psychological challenges. In addition, we explored gender differences in response to obesity treatments in the specific context of new anti-obesity drugs.

The relationship between weight-adjusted-waist index and total bone mineral density in adults aged 20-59

Introduction

According to reports, obesity has a significant impact on bone health. And the weight-adjusted-waist index (WWI), superior to BMI and waist circumference (WC), is a new obesity indicator arising in recent years. This research investigated the relationship between WWI and total bone mineral density (BMD) for adults aged 20 to 59.

Methods

Using data from the 2011-2018 NHANES, we looked into the independent link between WWI and total BMD as well as its nonlinearity using weighted multiple linear regression and smooth curve fitting. Two-stage linear regression models were employed to calculate the threshold effects. There were additional subgroup analyses and testing for interactions.

Results

Multiple linear regression studies on a total of 10,372 individuals showed a significant inverse link between WWI and total BMD in adults between 20 and 59 [β = -0.04, 95% CI: (-0.04, -0.03), P<0.0001]. And smoking, race, and chronic kidney disease (CKD) had no significant effect on this negative connection (P for interaction >0.05). In addition, we found a nonlinear relationship between WWI and total BMD in diabetic and CKD patients, for which the saturation point was 11.38 cm/√kg in the CKD patient group and 10.29 cm/√kg in the diabetic patient group.

Conclusion

Our analysis demonstrated a significant inverse association between WWI and total BMD in adults aged 20-59.

The causal association between obesity and gastric cancer and shared molecular signatures: a large-scale Mendelian randomization and multi-omics analysis

Purpose

While observational studies have identified obesity as a potential risk factor for gastric cancer, the causality remains uncertain. This study aimed to evaluate the causal relationship between obesity and gastric cancer and identify the shared molecular signatures linking obesity to gastric cancer.

Methods

A two-sample Mendelian randomization (MR) analysis was conducted using the GWAS data of body fat percentage (exposure, n = 331,117) and gastric cancer (outcome, n = 202,308). Bioinformatics and meta-analysis of multi-omics data were performed to identify key molecules mediating the causality. The meta-analysis of the plasma/serum proteome included 1,662 obese and 3,153 gastric cancer patients. Obesity and gastric cancer-associated genes were identified using seven common gene ontology databases. The transcriptomic data were obtained from TCGA and GEO databases. The Bioinformatic findings were clinically validated in plasma from 220 obese and 400 gastric cancer patients across two hospitals. Finally, structural-based virtual screening (SBVS) was performed to explore the potential FDA-approved drugs targeting the identified mediating molecules.

Results

The MR analysis revealed a significant causal association between obesity and gastric cancer (IVW, OR = 1.37, 95% CI:1.12-1.69, P = 0.0028), without pleiotropy or heterogeneity. Bioinformatic and meta-analysis of multi-omics data revealed shared TNF, PI3K-AKT, and cytokine signaling dysregulation, with significant upregulation of AKT1, IL-6, and TNF. The clinical study confirmed widespread upregulation of systemic inflammatory markers in the plasma of both diseases. SBVS identified six novel potent AKT1 inhibitors, including the dietary supplement adenosine, representing a potentially preventive drug with low toxicity.

Conclusion

Obesity causally increases gastric cancer, likely mediated by persistent AKT1/IL-6/TNF upregulation. As a potential AKT1 inhibitor, adenosine may mitigate the obesity-to-gastric cancer transition. These findings could inform preventive drug development to reduce gastric cancer risk in obesity.

Proteome and genome integration analysis of obesity

ABSTRACT

The prevalence of obesity has increased worldwide in recent decades. Genetic factors are now known to play a substantial role in the predisposition to obesity and may contribute up to 70% of the risk for obesity. Technological advancements during the last decades have allowed the identification of many hundreds of genetic markers associated with obesity. However, the transformation of current genetic variant-obesity associations into biological knowledge has been proven challenging. Genomics and proteomics are complementary fields, as proteomics extends functional analyses. Integrating genomic and proteomic data can help to bridge a gap in knowledge regarding genetic variant-obesity associations and to identify new drug targets for the treatment of obesity. We provide an overview of the published papers on the integrated analysis of proteomic and genomic data in obesity and summarize four mainstream strategies: overlap, colocalization, Mendelian randomization, and proteome-wide association studies. The integrated analyses identified many obesity-associated proteins, such as leptin, follistatin, and adenylate cyclase 3. Despite great progress, integrative studies focusing on obesity are still limited. There is an increased demand for large prospective cohort studies to identify and validate findings, and further apply these findings to the prevention, intervention, and treatment of obesity. In addition, we also discuss several other potential integration methods.