搜索结果：Obesity surgery

Overweight and obesity represent a rapidly growing threat to the health of populations in an increasing number of countries. Indeed they are now so common that they are replacing more traditional problems such as undernutrition and infectious diseases as the most significant causes of ill-health. Obesity comorbidities include coronary heart disease, hypertension and stroke, certain types of cancer, non-insulin-dependent diabetes mellitus, gallbladder disease, dyslipidaemia, osteoarthritis and gout, and pulmonary diseases, including sleep apnoea. In addition, the obese suffer from social bias, prejudice and discrimination, on the part not only of the general public but also of health professionals, and this may make them reluctant to seek medical assistance. WHO therefore convened a Consultation on obesity to review current epidemiological information, contributing factors and associated consequences, and this report presents its conclusions and recommendations. In particular, the Consultation considered the system for classifying overweight and obesity based on the body mass index, and concluded that a coherent system is now available and should be adopted internationally. The Consultation also concluded that the fundamental causes of the obesity epidemic are sedentary lifestyles and high-fat energy-dense diets, both resulting from the profound changes taking place in society and the behavioural patterns of communities as a consequence of increased urbanization and industrialization and the disappearance of traditional lifestyles. A reduction in fat intake to around 20-25% of energy is necessary to minimize energy imbalance and weight gain in sedentary individuals. While there is strong evidence that certain genes have an influence on body mass and body fat, most do not qualify as necessary genes, i.e. genes that cause obesity whenever two copies of the defective allele are present; it is likely to be many years before the results of genetic research can be applied to the problem. Methods for the treatment of obesity are described, including dietary management, physical activity and exercise, and antiobesity drugs, with gastrointestinal surgery being reserved for extreme cases.

Purpose: We developed an artificial neural network (ANN) combining radiomics with clinical and dosimetric features to predict the extent of body mass index (BMI) increase after surgery and proton therapy, with advantage of improved accuracy and integrated key feature selection. Methods and Materials: Uniform treatment protocol composing of limited surgery and proton radiotherapy was given to 84 pediatric craniopharyngioma patients (aged 1-20 years). Post-treatment obesity was classified into 3 groups (<10%, 10-20%, and >20%) based on the normalized BMI increase during a 5-year follow-up. We developed a densely connected 4-layer ANN with radiomics calculated from pre-surgery MRI (T1w, T2w, and FLAIR), combining clinical and dosimetric features as input. Accuracy, area under operative curve (AUC), and confusion matrices were compared with random forest (RF) models in a 5-fold cross-validation. The Group lasso regularization optimized a sparse connection to input neurons to identify key features from high-dimensional input. Results: Classification accuracy of the ANN reached above 0.9 for T1w, T2w, and FLAIR MRI. Confusion matrices showed high true positive rates of above 0.9 wh

OBJECTIVES: To assess the clinical effectiveness and cost-effectiveness of bariatric surgery for obesity. DATA SOURCES: Seventeen electronic databases were searched [MEDLINE; EMBASE; PreMedline In-Process & Other Non-Indexed Citations; The Cochrane Library including the Cochrane Systematic Reviews Database, Cochrane Controlled Trials Register, DARE, NHS EED and HTA databases; Web of Knowledge Science Citation Index (SCI); Web of Knowledge ISI Proceedings; PsycInfo; CRD databases; BIOSIS; and databases listing ongoing clinical trials] from inception to August 2008. Bibliographies of related papers were assessed and experts were contacted to identify additional published and unpublished references. REVIEW METHODS: Two reviewers independently screened titles and abstracts for eligibility. Inclusion criteria were applied to the full text using a standard form. Interventions investigated were open and laparoscopic bariatric surgical procedures in widespread current use compared with one another and with non-surgical interventions. Population comprised adult patients with body mass index (BMI) > or = 30 and young obese people. Main outcomes were at least one of the following after at least 12 months follow-up: measures of weight change; quality of life (QoL); perioperative and postoperative mortality and morbidity; change in obesity-related comorbidities; cost-effectiveness. Studies eligible for inclusion in the systematic review for comparisons of Surgery versus Surgery were RCTs. For comparisons of Surgery versus Non-surgical procedures eligible studies were RCTs, controlled clinical trials and prospective cohort studies (with a control cohort). Studies eligible for inclusion in the systematic review of cost-effectiveness were full cost-effectiveness analyses, cost-utility analyses, cost-benefit analyses and cost-consequence analyses. One reviewer performed data extraction, which was checked by two reviewers independently. Two reviewers independently applied quality assessment criteria and differences in opinion were resolved at each stage. Studies were synthesised through a narrative review with full tabulation of the results of all included studies. In the economic model the analysis was developed for three patient populations, those with BMI > or = 40; BMI > or = 30 and < 40 with Type 2 diabetes at baseline; and BMI > or = 30 and < 35. Models were applied with assumptions on costs and comorbidity. RESULTS: A total of 5386 references were identified of which 26 were included in the clinical effectiveness review: three randomised controlled trials (RCTs) and three cohort studies compared surgery with non-surgical interventions and 20 RCTs compared different surgical procedures. Bariatric surgery was a more effective intervention for weight loss than non-surgical options. In one large cohort study weight loss was still apparent 10 years after surgery, whereas patients receiving conventional treatment had gained weight. Some measures of QoL improved after surgery, but not others. After surgery statistically fewer people had metabolic syndrome and there was higher remission of Type 2 diabetes than in non-surgical groups. In one large cohort study the incidence of three out of six comorbidities assessed 10 years after surgery was significantly reduced compared with conventional therapy. Gastric bypass (GBP) was more effective for weight loss than vertical banded gastroplasty (VBG) and adjustable gastric banding (AGB). Laparoscopic isolated sleeve gastrectomy (LISG) was more effective than AGB in one study. GBP and banded GBP led to similar weight loss and results for GBP versus LISG and VBG versus AGB were equivocal. All comparisons of open versus laparoscopic surgeries found similar weight losses in each group. Comorbidities after surgery improved in all groups, but with no significant differences between different surgical interventions. Adverse event reporting varied; mortality ranged from none to 10%. Adverse events from conventional therapy included intolerance to medication, acute cholecystitis and gastrointestinal problems. Major adverse events following surgery, some necessitating reoperation, included anastomosis leakage, pneumonia, pulmonary embolism, band slippage and band erosion. Bariatric surgery was cost-effective in comparison to non-surgical treatment in the reviewed published estimates of cost-effectiveness. However, these estimates are likely to be unreliable and not generalisable because of methodological shortcomings and the modelling assumptions made. Therefore a new economic model was developed. Surgical management was more costly than non-surgical management in each of the three patient populations analysed, but gave improved outcomes. For morbid obesity, incremental cost-effectiveness ratios (ICERs) (base case) ranged between 2000 pounds and 4000 pounds per QALY gained. They remained within the range regarded as cost-effective from an NHS decision-making perspective when assumptions for deterministic sensitivity analysis were changed. For BMI > or = 30 and 40, ICERs were 18,930 pounds at two years and 1397 pounds at 20 years, and for BMI > or = 30 and < 35, ICERs were 60,754 pounds at two years and 12,763 pounds at 20 years. Deterministic and probabilistic sensitivity analyses produced ICERs which were generally within the range considered cost-effective, particularly at the long twenty year time horizons, although for the BMI 30-35 group some ICERs were above the acceptable range. CONCLUSIONS: Bariatric surgery appears to be a clinically effective and cost-effective intervention for moderately to severely obese people compared with non-surgical interventions. Uncertainties remain and further research is required to provide detailed data on patient QoL; impact of surgeon experience on outcome; late complications leading to reoperation; duration of comorbidity remission; resource use. Good-quality RCTs will provide evidence on bariatric surgery for young people and for adults with class I or class II obesity. New research must report on the resolution and/or development of comorbidities such as Type 2 diabetes and hypertension so that the potential benefits of early intervention can be assessed.

Obesity is defined as the excessive accumulation or abnormal distribution of body fat. According to data from World Obesity Atlas 2024, the increase in prevalence of obesity has become a major worldwide health problem in adults as well as among children and adolescents. Although an increasing number of drugs have been approved for the treatment of obesity in recent years, many of these drugs have inevitable side effects which have increased the demand for new safe, accessible and effective drugs for obesity and prompt interest in natural products. Berberine (BBR) and its metabolites, known for their multiple pharmacological effects. Recent studies have emphatically highlighted the anti-obesity benefits of BBR and the underlying mechanisms have been gradually elucidated. However, its clinical application is limited by poor oral absorption and low bioavailability. Based on this, this review summarizes current research on the anti-obesity effects of BBR and its metabolites, including advancements in clinical trail results, understanding potential molecular mechanisms and absorption and bioavailability. As a natural compound derived from plants, BBR holds potential as an alternative ap

Despite obesity being widely discussed in the social sciences, the effect of a robot's perceived obesity level on trust is not covered by the field of HRI. While in research regarding humans, Body Mass Index (BMI) is commonly used as an indicator of obesity, this scale is completely irrelevant in the context of robots, so it is challenging to operationalize the perceived obesity level of robots; indeed, while the effect of robot's size (or height) on people's trust in it was addressed in previous HRI papers, the perceived obesity level factor has not been addressed. This work examines to what extent the perceived obesity level of humanoid robots affects people's trust in them. To test this hypothesis, we conducted a within-subjects study where, using an online pre-validated questionnaire, the subjects were asked questions while being presented with two pictures of humanoids, one with a regular obesity level and the other with a high obesity level. The results show that humanoid robots with lower perceived obesity levels are significantly more likely to be trusted.

In this paper we investigate the spatio-temporal dynamics of obesity rates across Italian regions from 2010 to 2022, aiming to identify spatial and temporal trends and assess potential heterogeneities. We implement a Bayesian hierarchical Beta regression model to analyze regional obesity rates, integrating spatial and temporal random effects, alongside gender and various exogenous predictors. The model leverages the Stochastic Search Variable Selection technique to identify significant predictors supported by the data. The analysis reveals both regional heterogeneity and dependence in obesity rates over the study period, emphasizing the importance of considering gender and spatial correlation in explaining its dynamics over time. In fact, the inclusion of structured spatial and temporal random effects captures the complexities of regional variations over time. These random effects, along with gender, emerge as the primary determinants of obesity prevalence across Italian regions, while the role of exogenous covariates is found to be minimal at the regional level. While socioeconomic and lifestyle factors remain fundamental at a micro-level, the findings demonstrate that the integra

Novel brain biomarkers of obesity were sought by studying statistical measurements on fractional anisotropy (FA) images of different white matter (WM) tracts from subjects with specific demographic characteristics. Tract measurements were chosen that showed differences between two groups (normal weigh and overweight/obese) and that were correlated with their BMI. From these measurements, a simple and novel process was applied to select those that would allow the creation of models to quantify and classify the state of obesity of individuals. The biomarkers were created from the tract measurements used in the models. Some positive correlations were found between WM integrity and BMI, mainly in tracts involved in motor functions. From this result, neuroplasticity in motor tracts associated with obesity was hypothesized. Two models were built to quantify and classify obesity status, whose regression coefficients formed the novel proposed obesity-associated brain biomarkers. A process for the selection of tract measurements was proposed, such that models were built to determine the obesity status of subjects individually. From these models, novel brain biomarkers associated with obesit

Obesity prevalence in Indonesian adults increased from 10.5% in 2007 to 23.4% in 2023. Studies showed that genetic predisposition significantly influences obesity susceptibility. To aid this, polygenic risk scores (PRS) help aggregate the effects of numerous genetic variants to assess genetic risk. However, 91% of genome-wide association studies (GWAS) involve European populations, limiting their applicability to Indonesians due to genetic diversity. This study aims to develop and validate an ancestry adjusted PRS for obesity in the Indonesian population using principal component analysis (PCA) method constructed from the 1000 Genomes Project data and our own genomic data from approximately 2,800 Indonesians. We calculate PRS for obesity using all races, then determine the first four principal components using ancestry-informative SNPs and develop a linear regression model to predict PRS based on these principal components. The raw PRS is adjusted by subtracting the predicted score to obtain an ancestry adjusted PRS for the Indonesian population. Our results indicate that the ancestry-adjusted PRS improves obesity risk prediction. Compared to the unadjusted PRS, the adjusted score

BACKGROUND: Bariatric (weight loss) surgery for obesity is considered when other treatments have failed. The effects of the available bariatric procedures compared with medical management and with each other are uncertain. This is an update of a Cochrane review first published in 2003 and previously updated in 2005. OBJECTIVES: To assess the effects of bariatric surgery for obesity. SEARCH STRATEGY: Studies were obtained from computerized searches of multiple electronic bibliographic databases, supplemented with searches of reference lists and consultation with experts in obesity research. SELECTION CRITERIA: Randomised controlled trials (RCTs) comparing different surgical procedures, and RCTs, controlled clinical trials and prospective cohort studies comparing surgery with non-surgical management for obesity. DATA COLLECTION AND ANALYSIS: Data were extracted by one reviewer and checked independently by two reviewers. Two reviewers independently assessed trial quality. MAIN RESULTS: Twenty six studies were included. Three RCTs and three prospective cohort studies compared surgery with non-surgical management, and 20 RCTs compared different bariatric procedures. The risk of bias of many trials was uncertain; just five had adequate allocation concealment. A meta-analysis was not appropriate.Surgery results in greater weight loss than conventional treatment in moderate (body mass index greater than 30) as well as severe obesity. Reductions in comorbidities, such as diabetes and hypertension, also occur. Improvements in health-related quality of life occurred after two years, but effects at ten years are less clear.Surgery is associated with complications, such as pulmonary embolism, and some postoperative deaths occurred.Five different bariatric procedures were assessed, but some comparisons were assessed by just one trial. The limited evidence suggests that weight loss following gastric bypass is greater than vertical banded gastroplasty or adjustable gastric banding, but similar to isolated sleeve gastrectomy and banded gastric bypass. Isolated sleeve gastrectomy appears to result in greater weight loss than adjustable gastric banding. Evidence comparing vertical banded gastroplasty with adjustable gastric banding is inconclusive. Data on the comparative safety of the bariatric procedures was limited.Weight loss and quality of life were similar between open and laparoscopic surgery. Conversion from laparoscopic to open surgery may occur. AUTHORS' CONCLUSIONS: Surgery is more effective than conventional management. Certain procedures produce greater weight loss, but data are limited. The evidence on safety is even less clear. Due to limited evidence and poor quality of the trials, caution is required when interpreting comparative safety and effectiveness.

Childhood and adolescent obesity rates are a global concern because obesity is associated with chronic diseases and long-term health risks. Artificial intelligence technology has emerged as a promising solution to accurately predict obesity rates and provide personalized feedback to adolescents. This study emphasizes the importance of early identification and prevention of obesity-related health issues. Factors such as height, weight, waist circumference, calorie intake, physical activity levels, and other relevant health information need to be considered for developing robust algorithms for obesity rate prediction and delivering personalized feedback. Hence, by collecting health datasets from 321 adolescents, we proposed an adolescent obesity prediction system that provides personalized predictions and assists individuals in making informed health decisions. Our proposed deep learning framework, DeepHealthNet, effectively trains the model using data augmentation techniques, even when daily health data are limited, resulting in improved prediction accuracy (acc: 0.8842). Additionally, the study revealed variations in the prediction of the obesity rate between boys (acc: 0.9320) and

Childhood obesity remains a major public health challenge in the United States, strongly influenced by a combination of individual-level, household-level, and environmental-level risk factors. Traditional epidemiological studies typically analyze these levels independently, limiting insights into how structural environmental conditions interact with individual-level characteristics to influence health outcomes. In this study, we introduce a micro-macro machine learning framework that integrates (1) individual-level anthropometric and socioeconomic data from NHANES and (2) macro-level structural environment features, including food access, air quality, and socioeconomic vulnerability extracted from USDA and EPA datasets. Four machine learning models Logistic Regression, Random Forest, XGBoost, and LightGBM were trained to predict obesity using NHANES microdata. XGBoost achieved the strongest performance. A composite environmental vulnerability index (EnvScore) was constructed using normalized indicators from USDA and EPA at the state level. Multi-level comparison revealed strong geographic similarity between states with high environmental burden and the nationally predicted micro-le

Obesity is widely recognized as a serious and pervasive health concern. We study obesity through body mass index (BMI), which is known to be highly heritable, and identify important genetic risk factors for BMI from hundreds of thousands of single nucleotide polymorphisms (SNPs) in the Framingham Study data. Several challenges arise when using traditional genome-wide association studies (GWAS): (1) They suffer from a low power due to a combination of a limited number of participants and the stringent genome-wide significance threshold; (2) existing prior knowledge from large meta-analyses may provide valuable guidance but is often underutilized; (3) the one-at-a-time univariate marginal regression framework ignores the joint and conditional nature of genetic effects; (4) GWAS focus solely on mean outcomes, whereas obesity inherently concerns abnormally high BMI levels. To address these challenges, we conduct the analysis by proposing and applying a novel Knowledge Integration Quantile Regression (KIQR) approach via simultaneous variable selection and estimation, focusing on the conditional high quantiles of BMI, which are most relevant to obesity risk, while integrating prior infor

Overweight and obese individuals tend to have increased brain age, reflecting poorer brain health likely due to grey and white matter atrophy related to obesity. However, it is unclear if older brain age associated with obesity can be reversed following weight loss and cardiometabolic health improvement. The aim of this study was to assess the impact of weight loss and cardiometabolic improvement following bariatric surgery on brain health, as measured by change in brain age estimated based on voxel-based morphometry (VBM). We used three datasets: 1) CamCAN to train the brain age prediction model, 2) Human Connectome Project to investigate whether individuals with obesity have greater brain age than individuals with normal weight, and 3) pre-surgery, as well as 4, 12, and 24 month post-surgery data from participants (n=87) who underwent a bariatric surgery to investigate whether weight loss and cardiometabolic improvement as a result of bariatric surgery lowers the brain age. Our results from the HCP dataset showed a higher brain age for individuals with obesity compared to individuals with normal weight (p&lt;0.0001). We also found significant improvement in brain health, indicate

Concept erasure in text-to-image diffusion models is crucial for mitigating harmful content, yet existing methods often compromise generative quality. We introduce Semantic Surgery, a novel training-free, zero-shot framework for concept erasure that operates directly on text embeddings before the diffusion process. It dynamically estimates the presence of target concepts in a prompt and performs a calibrated vector subtraction to neutralize their influence at the source, enhancing both erasure completeness and locality. The framework includes a Co-Occurrence Encoding module for robust multi-concept erasure and a visual feedback loop to address latent concept persistence. As a training-free method, Semantic Surgery adapts dynamically to each prompt, ensuring precise interventions. Extensive experiments on object, explicit content, artistic style, and multi-celebrity erasure tasks show our method significantly outperforms state-of-the-art approaches. We achieve superior completeness and robustness while preserving locality and image quality (e.g., 93.58 H-score in object erasure, reducing explicit content to just 1 instance, and 8.09 H_a in style erasure with no quality degradation).

Obesity is a global epidemic causing at least 2.8 million deaths per year. This complex disease is associated with significant socioeconomic burden, reduced work productivity, unemployment, and other social determinants of Health (SDoH) disparities. Objective: The objective of this study was to investigate the effects of SDoH on obesity prevalence among adults in Shelby County, Tennessee, USA using a geospatial machine-learning approach. Obesity prevalence was obtained from publicly available CDC 500 cities database while SDoH indicators were extracted from the U.S. Census and USDA. We examined the geographic distributions of obesity prevalence patterns using Getis-Ord Gi* statistics and calibrated multiple models to study the association between SDoH and adult obesity. Also, unsupervised machine learning was used to conduct grouping analysis to investigate the distribution of obesity prevalence and associated SDoH indicators. Results depicted a high percentage of neighborhoods experiencing high adult obesity prevalence within Shelby County. In the census tract, median household income, as well as the percentage of individuals who were black, home renters, living below the poverty