Obesity represents a major global health challenge with important clinical implications. Despite its recognized importance, the global disease burden attributable to high body mass index (BMI) remains less well understood. We systematically analyzed global deaths and disability-adjusted life years (DALYs) attributable to high BMI using the methodology and analytical approaches of the Global Burden of Disease Study (GBD) 2021. High BMI was defined as a BMI over 25 kg/m² for individuals aged ≥20 years. The Socio-Demographic Index (SDI) was used as a composite measure to assess the level of socio-economic development across different regions. Subgroup analyses considered age, sex, year, geographical location, and SDI. From 1990 to 2021, the global deaths and DALYs attributable to high BMI increased more than 2.5-fold for females and males. However, the age-standardized death rates remained stable for females and increased by 15.0% for males. Similarly, the age-standardized DALY rates increased by 21.7% for females and 31.2% for males. In 2021, the six leading causes of high BMI-attributable DALYs were diabetes mellitus, ischemic heart disease, hypertensive heart disease, chronic kidney disease, low back pain and stroke. From 1990 to 2021, low-middle SDI countries exhibited the highest annual percentage changes in age-standardized DALY rates, whereas high SDI countries showed the lowest. The worldwide health burden attributable to high BMI has grown significantly between 1990 and 2021. The increasing global rates of high BMI and the associated disease burden highlight the urgent need for regular surveillance and monitoring of BMI. 10.13039/501100001809 National Natural Science Foundation of China and National Key R&D Program of China.

Introduction: Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG) are the two most performed techniques in bariatric surgery. The aim of this study is to compare two surgical procedures in terms of weight loss and the development of comorbidities such as type II diabetes mellitus T2D, arterial hypertension, sleep apnea (OSAS), and gastroesophageal reflux disease (GERD). Methods: Data from the German Bariatric Surgery Registry (GBSR) from 2005 to 2021 were used. 1,392 RYGB and 1,132 SG primary surgery patients were included. Minimum age 18 years; five-year follow-up data available. Tests were performed with a 5% significance level. Results: Loss of follow-up 95.41% within five years. Five years after surgery, the RYGB showed significant advantages in terms of excess weight loss (%EWL 64.2% vs. 56.9%) and remission rates of the studied comorbidities: hypertension (54.4% vs. 47.8%), OSAS (64.5% vs. 50.1%), and GERD (86.1% vs. 66.9%). Compared to the pre-test, individuals diagnosed with insulin-dependent T2D showed significant improvements with RYGB over a five-year period (remission rate: 75% vs. 63%). In contrast, non-insulin-dependent T2D showed no significant difference between the two approaches (p = 0.125). Conclusion: Both surgical procedures resulted in significant weight loss and improved comorbidities. However, the improvement in comorbidities was significantly greater in patients who underwent RYGB than in those who underwent SG, suggesting that the RYGB technique is preferable. Nevertheless, RYGB requires a high degree of surgical skill. Therefore, acquiring expertise in the technical facets of the surgery is essential to achieving favorable outcomes.

Predictive modeling for metastasis in oncology has gained significant traction due to its potential to improve prognosis, guide treatment strategies and enhance patient outcomes. Current methods leverage advancements in machine learning, genomics and imaging technologies to predict the likelihood of cancer spread. Techniques such as logistic regression, decision trees, support vector machines and neural networks have been employed to analyze clinical, pathological, and molecular data. Genomic profiling, liquid biopsies, and radiomics are increasingly integrated into these models to identify metastatic patterns and risk factors. Despite these advances, challenges persist, including data heterogeneity, model interpretability, and the need for larger, high-quality datasets for validation. Furthermore, the integration of artificial intelligence with precision medicine offers promising avenues for more personalized metastasis predictions. Future directions focus on enhancing model accuracy through deep learning, improving the interpretability of black-box models, and incorporating multi-omics data to capture the complexity of metastatic mechanisms. With the advent of advanced computational tools and growing datasets, predictive modeling in oncology is poised to revolutionize metastasis management, offering clinicians’ valuable insights for early detection and tailored treatment strategies.