搜索结果：Obesity (Silver Spring, Md.)

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

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

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

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

Obesity is a critical global health issue driven by dietary, physiological, and environmental factors, and is strongly associated with chronic diseases such as diabetes, cardiovascular disorders, and cancer. Machine learning has emerged as a promising approach for early obesity risk prediction, yet a comparative evaluation of ensemble techniques -- particularly hybrid majority voting and ensemble stacking -- remains limited. This study aims to compare hybrid majority voting and ensemble stacking methods for obesity risk prediction, identifying which approach delivers higher accuracy and efficiency. The analysis seeks to highlight the complementary strengths of these ensemble techniques in guiding better predictive model selection for healthcare applications. Two datasets were utilized to evaluate three ensemble models: Majority Hard Voting, Weighted Hard Voting, and Stacking (with a Multi-Layer Perceptron as meta-classifier). A pool of nine Machine Learning (ML) algorithms, evaluated across a total of 50 hyperparameter configurations, was analyzed to identify the top three models to serve as base learners for the ensemble methods. Preprocessing steps involved dataset balancing, and

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

Reliable prediction of pediatric obesity can offer a valuable resource to providers, helping them engage in timely preventive interventions before the disease is established. Many efforts have been made to develop ML-based predictive models of obesity, and some studies have reported high predictive performances. However, no commonly used clinical decision support tool based on existing ML models currently exists. This study presents a novel end-to-end pipeline specifically designed for pediatric obesity prediction, which supports the entire process of data extraction, inference, and communication via an API or a user interface. While focusing only on routinely recorded data in pediatric electronic health records (EHRs), our pipeline uses a diverse expert-curated list of medical concepts to predict the 1-3 years risk of developing obesity. Furthermore, by using the Fast Healthcare Interoperability Resources (FHIR) standard in our design procedure, we specifically target facilitating low-effort integration of our pipeline with different EHR systems. In our experiments, we report the effectiveness of the predictive model as well as its alignment with the feedback from various stakehol

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

The Internet of Things (IoT) connects people, devices, and information resources, in various domains to improve efficiency. The healthcare domain has been transformed by the integration of the IoT, leading to the development of digital healthcare solutions such as health monitoring, emergency detection, and remote operation. This integration has led to an increase in the health data collected from a variety of IoT sources. Consequently, advanced technologies are required to analyze health data, and artificial intelligence has been employed to extract meaningful insights from the data. Childhood overweight and obesity have emerged as some of the most serious global public health challenges, as they can lead to a variety of health-related problems and the early development of chronic diseases. To address this, a self-adaptive framework is proposed to prevent childhood obesity by using lifelog data from IoT environments, with human involvement being an important consideration in the framework. The framework uses an ensemble-based learning model to predict obesity using the lifelog data. Empirical experiments using lifelog data from smartphone applications were conducted to validate th

More than one-third of the adult population in the United States is obese. Obesity has been linked to factors such as, genetics, diet, physical activity and the environment. However, evidence indicating associations between the built environment and obesity has varied across studies and geographical contexts. Here, we used deep learning and approximately 150,000 high resolution satellite images to extract features of the built environment. We then developed linear regression models to consistently quantify the association between the extracted features and obesity prevalence at the census tract level for six cities in the United States. The extracted features of the built environment explained 72% to 90% of the variation in obesity prevalence across cities. Outof-sample predictions were considerably high with correlations greater than 80% between predicted and true obesity prevalence across all census tracts. This study supports a strong association between the built environment and obesity prevalence. Additionally, it also illustrates that features of the built environment extracted from satellite images can be useful for studying health indicators, such as obesity. Understanding

The "obesity paradox" has been reported in several observational studies, where obesity was shown to be associated to a decreased mortality in individuals suffering from a chronic disease, such as diabetes or heart failure. Causal arguments have recently been given to explain this apparently paradoxical fact: because the chronic disease is caused by obesity, the observed "protective effect" of obesity among patients with, say, diabetes, actually has no causal value. Recently, Sperrin et al. (2016) relaunched the debate and claimed that the resulting bias, the so-called collider bias, was unlikely to be the main explanation for the obesity paradox. However, a number of issues in their work make their conclusions questionable. In this article, we first study the bias between (i) the association between obesity and early death among patients suffering from the chronic disease $Δ_{AS}$ and (ii) the causal effect considered by Sperrin et al. Under the usual framework of structural causal models, we explain why this bias can be much higher than what these authors reported. We further consider alternative causal effects of potential interest and study their difference with $Δ_{AS}$. Numer

Because obesity is a risk factor for many serious illnesses such as diabetes, better understandings of obesity and eating disorders have been attracting attention in neurobiology, psychiatry, and neuroeconomics. This paper presents future study directions by unifying (i) economic theory of addiction and obesity (Becker and Murphy, 1988; Levy 2002; Dragone 2009), and (ii) recent empirical findings in neuroeconomics and neurobiology of obesity and addiction. It is suggested that neurobiological substrates such as adiponectin, dopamine (D2 receptors), endocannabinoids, ghrelin, leptin, nesfatin-1, norepinephrine, orexin, oxytocin, serotonin, vasopressin, CCK, GLP-1, MCH, PYY, and stress hormones (e.g., CRF) in the brain (e.g., OFC, VTA, NAcc, and the hypothalamus) may determine parameters in the economic theory of obesity. Also, the importance of introducing time-inconsistent and gain/loss-asymmetrical temporal discounting (intertemporal choice) models based on Tsallis' statistics and incorporating time-perception parameters into the neuroeconomic theory is emphasized. Future directions in the application of the theory to studies in neuroeconomics and neuropsychiatry of obesity at the

Social media provide a platform for users to express their opinions and share information. Understanding public health opinions on social media, such as Twitter, offers a unique approach to characterizing common health issues such as diabetes, diet, exercise, and obesity (DDEO), however, collecting and analyzing a large scale conversational public health data set is a challenging research task. The goal of this research is to analyze the characteristics of the general public's opinions in regard to diabetes, diet, exercise and obesity (DDEO) as expressed on Twitter. A multi-component semantic and linguistic framework was developed to collect Twitter data, discover topics of interest about DDEO, and analyze the topics. From the extracted 4.5 million tweets, 8% of tweets discussed diabetes, 23.7% diet, 16.6% exercise, and 51.7% obesity. The strongest correlation among the topics was determined between exercise and obesity. Other notable correlations were: diabetes and obesity, and diet and obesity DDEO terms were also identified as subtopics of each of the DDEO topics. The frequent subtopics discussed along with Diabetes, excluding the DDEO terms themselves, were blood pressure, hear

Childhood obesity is a major public health challenge. Early prediction and identification of the children at a high risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage obesity. Most existing predictive tools for childhood obesity primarily rely on traditional regression-type methods using only a few hand-picked features and without exploiting longitudinal patterns of children data. Deep learning methods allow the use of high-dimensional longitudinal datasets. In this paper, we present a deep learning model designed for predicting future obesity patterns from generally available items on children medical history. To do this, we use a large unaugmented electronic health records dataset from a large pediatric health system. We adopt a general LSTM network architecture which are known to better represent the longitudinal data. We train our proposed model on both dynamic and static EHR data. Our model is used to predict obesity for ages between 2-20 years. We compared the performance of our LSTM model with other machine learning methods that aggregate over sequential data and ignore temporality. To add interpretability

Obesity, the leading cause of many non-communicable diseases, occurs mainly for eating more than our body requirements and lack of proper activity. So, being healthy requires heathy diet plans, especially for patients with comorbidities. But it is difficult to figure out the exact quantity of each nutrient because nutrients requirement varies based on physical and disease conditions. In our study we proposed a novel machine learning based system to predict the amount of nutrients one individual requires for being healthy. We applied different machine learning algorithms: linear regression, support vector machine (SVM), decision tree, random forest, XGBoost, LightGBM on fluid and 3 other major micronutrients: carbohydrate, protein, fat consumption prediction. We achieved high accuracy with low root mean square error (RMSE) by using linear regression in fluid prediction, random forest in carbohydrate prediction and LightGBM in protein and fat prediction. We believe our diet recommender system, OBESEYE, is the only of its kind which recommends diet with the consideration of comorbidities and physical conditions and promote encouragement to get rid of obesity.

Globally, the number of obese patients has doubled due to sedentary lifestyles and improper dieting. The tremendous increase altered human genetics, and health. According to the world health organization, Life expectancy dropped from 80 to 75 years, as obese people struggle with different chronic diseases. This report will address the problems of obesity in children and adults using ML datasets to feature, predict, and analyze the causes of obesity. By engaging neural ML networks, we will explore neural control using diffusion tensor imaging to consider body fats, BMI, waist \& hip ratio circumference of obese patients. To predict the present and future causes of obesity with ML, we will discuss ML techniques like decision trees, SVM, RF, GBM, LASSO, BN, and ANN and use datasets implement the stated algorithms. Different theoretical literature from experts ML \& Bioinformatics experiments will be outlined in this report while making recommendations on how to advance ML for predicting obesity and other chronic diseases.

We present a large-scale analysis of Instagram pictures taken at 164,753 restaurants by millions of users. Motivated by the obesity epidemic in the United States, our aim is three-fold: (i) to assess the relationship between fast food and chain restaurants and obesity, (ii) to better understand people's thoughts on and perceptions of their daily dining experiences, and (iii) to reveal the nature of social reinforcement and approval in the context of dietary health on social media. When we correlate the prominence of fast food restaurants in US counties with obesity, we find the Foursquare data to show a greater correlation at 0.424 than official survey data from the County Health Rankings would show. Our analysis further reveals a relationship between small businesses and local foods with better dietary health, with such restaurants getting more attention in areas of lower obesity. However, even in such areas, social approval favors the unhealthy foods high in sugar, with donut shops producing the most liked photos. Thus, the dietary landscape our study reveals is a complex ecosystem, with fast food playing a role alongside social interactions and personal perceptions, which often

Gaussian Process (GP) regression is a powerful nonparametric Bayesian framework, but its performance depends critically on the choice of covariance kernel. Selecting an appropriate kernel is therefore central to model quality, yet remains one of the most challenging and computationally expensive steps in probabilistic modeling. We present a Bayesian optimization framework built on kernel-of-kernels geometry, using expected divergence-based distances between GP priors to explore kernel space efficiently. A multidimensional scaling (MDS) embedding of this distance matrix maps a discrete kernel library into a continuous Euclidean manifold, enabling smooth BO. In this formulation, the input space comprises kernel compositions, the objective is the log marginal likelihood, and featurization is given by the MDS coordinates. When the divergence yields a valid metric, the embedding preserves geometry and produces a stable BO landscape. We demonstrate the approach on synthetic benchmarks, real-world time-series datasets, and an additive manufacturing case study predicting melt-pool geometry, achieving superior predictive accuracy and uncertainty calibration relative to baselines including L

Obesity and diabetes epidemics are affecting about a third and tenth of US population, respectively, capturing the attention of the nation and its institutions. Social media provides an open forum for communication between individuals and health organizations, a forum which is easily joined by parties seeking to gain profit from it. In this paper we examine 1.5 million tweets mentioning obesity and diabetes in order to assess (1) the quality of information circulating in this conversation, as well as (2) the behavior and information needs of the users engaged in it. The analysis of top cited domains shows a strong presence of health information sources which are not affiliated with a governmental or academic institution at 41% in obesity and 50% diabetes samples, and that tweets containing these domains are retweeted more than those containing domains of reputable sources. On the user side, we estimate over a quarter of non-informational obesity discourse to contain fat-shaming -- a practice of humiliating and criticizing overweight individuals -- with some self-directed toward the writers themselves. We also find a great diversity in questions asked in these datasets, spanning def