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6,931 results for “obesity”
Inter-Chemical Correlation results for the study: HHEARx2017-1967 (Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial)
Title: Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial <br>Species: Homo sapiens <br>Number of samples: 1085 <br>Number of named analytes: 8 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=36 <br>
Data from: "Alteration of the gut microbiota's composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters"
<p>This dataset contains all data collected and used for the publication : "Alteration of the gut microbiota’s composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters". Besides the Readme, it contains 11 files.</p> <p><br> Excel files with classification (i.e. genes according to their fold induction or repression) are provided. Data include different conditions with varying number of samples per group. Data are structured according to employed methods and then stratify the data obtained within the individual work packages.</p>
Inter-Chemical Correlation results for the study: HHEARx2018-2537 (Phthalates and childhood obesity in a racially, ethnically and geographically diverse cohort.)
Title: Phthalates and childhood obesity in a racially, ethnically and geographically diverse cohort. <br>Species: Homo sapiens <br>Number of samples: 630 <br>Number of named analytes: 16 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=66 <br>
Obesity, Suicides and Unemployment by Country
<p>This dataset contains data about obesity, suicides and unemployment segregated by Country. The sources of data are wikipedia tables as updated on 11/04/2022. More information can be found in project's github: <a href="https://github.com/martinsanc/wikipedia_scraper"><strong>https://github.com/martinsanc/wikipedia_scraper</strong></a></p> <p> </p> <ul> <li> <p><strong>Países (<a href="https://en.wikipedia.org/wiki/List_of_countries_by_population_(United_Nations)">List of countries by population (United Nations) - Wikipedia</a>)</strong></p> <ul> <li> <p><strong>Country</strong></p> </li> <li> <p><strong>UN continental region</strong></p> </li> <li> <p><strong>UN statistical subregion</strong></p> </li> <li> <p><strong>Population 1 July 2018</strong></p> </li> <li> <p><strong>Population 1 July 2019</strong></p> </li> <li> <p><strong>Change</strong></p> </li> </ul> </li> <li> <p><strong>Desempleo (<a href="https://en.wikipedia.org/wiki/List_of_countries_by_unemployment_rate">List of countries by unemployment rate - Wikipedia</a>)</strong></p> <ul> <li> <p><strong>Unemployment Rate</strong></p> </li> <li> <p><strong>Sourcedate of information</strong></p> </li> </ul> </li> <li> <p><strong>Suicidios (<a href="https://en.wikipedia.org/wiki/List_of_countries_by_suicide_rate">List of countries by suicide rate - Wikipedia</a>)</strong></p> <ul> <li> <p><strong>All</strong></p> </li> <li> <p><strong>Male</strong></p> </li> <li> <p><strong>Female</strong></p> </li> </ul> </li> <li> <p><strong>Tasa de obesidad por país (<a href="https://en.wikipedia.org/wiki/List_of_countries_by_suicide_rate">List of countries by suicide rate - Wikipedia</a>)</strong></p> <ul> <li> <p><strong>Rank</strong></p> </li> <li> <p><strong>Obesity rate</strong></p> </li> </ul> </li> </ul>
Data and analysis supplement for: Functional imagery training versus motivational interviewing for weight loss: a randomised controlled trial of brief individual interventions for overweight and obesity.
<p>This submission provides the data and code for analyses reported in our publication.</p>
Gerational issues in linking family farming production, traditional food in diet, physical activity and obesity in Pacific Islands countries and territories: the case of the Melanesian population on Lifou Island
<p>In the Melanesian culture, traditional activities are organized around family farming, although the lifestyle transition taking place over the last several decades has led to imbalances in diet and physical activity, with both leading to obesity. The aim of this interdisciplinary study was to understand the links between family farming (produced, exchanged, sold, and consumed food), diet (focused on produced, hunted, and caught food), physical activity (sedentary, light, and moderate-to-vigorous physical activity) and obesity in Melanesian Lifou Island families (parents and children). Forty families, including 142 adults and children, completed individual food frequency questionnaires, wore tri-axial accelerometers for seven continuous days, and had weight and height measured with a bio-impedance device. Qualitative and quantitative interviews were conducted at the household level concerning family farming practices and sociodemographic variables. Multinomial regression analyses and logistic regression models were used to analyze the data. Results showed that family farming production brings a modest contribution to diet and active lifestyles for the family farmers of Lifou Island. The drivers for obesity in these tribal communities were linked to diet in the adults, whereas parental socioeconomic status and moderate-to-vigorous physical activity were the main factors associated to overweight and obesity in children. These differences in lifestyle behaviors within families suggest a transition in cultural practices at the intergenerational level. Future directions should consider seasonality and a more in-depth analysis of diet including macro- and micro- nutrients to acquire more accurate information on the intergenerational transition in cultural practices and its consequences on health outcomes in the Pacific region.</p>
Multidimensional pain profiling in people living with obesity and attending weight management services: a protocol for a longitudinal cohort study
<p><strong>Please note: The final dataset will not be available until data collection has been completed in the Autumn of 2024.<br> This dataset currently contains the following:</strong></p> <p>1. Details of the study, including authorship, ethical approval, funding, registration details, an abstract for the protocol of the study and details of data being collected (both in Microsoft Word and open access .txt formats)</p> <p>2. Outline of the data in the process of collection (Microsoft Excel)</p> <p>3. Ethical approval letters from relevant Research Ethics Committees (PDF)<br> <br> </p> <p> </p> <p><strong>Project Title: </strong>Multidimensional pain profiling in people living with obesity and attending weight management services: a longitudinal cohort study</p> <p> </p> <p><strong>Authors:</strong> Keith M. Smart<sup>1,2</sup>, Natasha Hinwood<sup>1</sup>, Colin G. Dunlevy<sup>3</sup>, Catherine Doody<sup>1</sup>, Catherine Blake<sup>1</sup>, Brona Fullen<sup>1</sup>, Jean O’Connell<sup>3</sup>, Carel W. Le Roux<sup>4</sup>, Clare Gilsenan<sup>5</sup>, Francis M. Finucane<sup>6,7</sup>, Gráinne O’Donoghue<sup>1</sup>.</p> <p> </p> <p><strong>Corresponding author</strong>: Natasha Hinwood</p> <p><strong>Address:</strong> UCD School of Public Health, Physiotherapy and Sport Science, University College Dublin, Dublin, Ireland</p> <p><strong>Email:</strong> <a href="mailto:natasha.hinwood@ucdconnect.ie">natasha.hinwood@ucdconnect.ie</a></p> <p><strong>Phone:</strong> +353 1 716 6511</p> <p> </p> <p>Full name, department, institution, city, and country of all co-authors.</p> <p><sup>1</sup>UCD School of Public Health, Physiotherapy and Sport Science, University College Dublin, Dublin, Ireland</p> <p><sup>2</sup>Physiotherapy Department, St. Vincent’s University Hospital, Dublin, Ireland</p> <p><sup>3</sup>Weight Management Service, St Columcille’s Hospital, Dublin, Ireland</p> <p><sup>4</sup>Diabetes Complications Research Centre, University College Dublin, Dublin, Ireland</p> <p><sup>5</sup>Physiotherapy Department, Beaumont Hospital, Dublin, Ireland</p> <p><sup>6</sup> School of Medicine, College of Nursing and Health Sciences, University of Galway</p> <p><sup>7</sup>Bariatric Medicine Service, Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals</p> <p> </p> <p><strong>ORCID</strong></p> <p>1. Keith M. Smart: 0000-0002-1598-5215</p> <p>2. Natasha Hinwood: 0000-0001-9382-716X</p> <p>3. Colin G. Dunlevy:</p> <p>4. Catherine Doody:</p> <p>5. Catherine Blake: 0000-0002-0600-629X</p> <p>6. Brona Fullen: 0000-0003-4408--2063</p> <p>7. Carel W. Le Roux: 0000-0001-5521-5445</p> <p>8. Jean O’Connell: 0000-0001-7241-8025</p> <p>9. Clare Gilsenan:</p> <p>10. Francis M. Finucane: 0000-0002-5374-7090</p> <p>11. Gráinne O’Donoghue: 0000-0002-9126-2094</p> <p> </p> <p> </p> <p><strong>Project abstract (Protocol): </strong></p> <p><em>Introduction</em>:</p> <p>Pain is prevalent in people living with overweight and obesity. Obesity is associated with increased self-reported pain intensity and pain-related disability, reductions in physical functioning and poorer psychological well-being. People living with obesity tend to respond less well to pain treatments or management compared to people living without obesity. Mechanisms linking obesity and pain are complex and may variously include contributions from and interactions between physiological, behavioural, psychological, socio-cultural, biomechanical, and genetic factors. Our aim is to study the multidimensional pain profiles of people living with obesity, over time, in an attempt to better understand the relationship between obesity and pain.<br> </p> <p><em>Methods and analysis: </em></p> <p>This longitudinal observational cohort study will recruit (n=216) people living with obesity and who are newly attending three weight management services in Ireland. Participants will complete questionnaires that assess their multidimensional biopsychosocial pain experience at baseline and at 3, 6, 12 and 18-months post-recruitment. Quantitative analyses will characterise the multidimensional pain experiences and trajectories of the cohort as a whole and in defined sub-groups.<br> </p> <p><em>Ethics and dissemination: </em></p> <p>The study protocol has been approved by the Ethics and Medical Research Committee of St Vincent’s Healthcare Group, Dublin, Ireland (Reference No.: RS21-059) and the University College Dublin Human Research Ethics Committee (Reference No.: LS-E-22-41-Hinwood-Smart). Findings will be disseminated through peer-reviewed journals, conference presentations, public and patient advocacy groups, and social media.</p>
Gestational Cd exposure in the CD-1 mouse induces sex-specific hepatic insulin insensitivity, obesity and metabolic syndrome in adult female offspring
<p>There is compelling evidence that developmental exposure to some toxic metals increases risk for obesity and obesity-related morbidity including cardiovascular disease and type 2 diabetes in adults. To explore the hypothesis that developmental Cd exposure increased risk of obesity later in life, male and female CD-1 mice were maternally exposed to 500 ppb CdCl<sub>2</sub> in drinking water during a human gestational equivalent period (GD0 - PND10). Hallmark indicators of metabolic disruption, hepatic steatosis, and metabolic syndrome were evaluated prior to birth through adulthood. Blood Cd levels in dams were similar to those observed in human pregnancy cohorts. There were no observed impacts of exposure on dams or pregnancy-related outcomes. Results of glucose and insulin tolerance testing revealed that Cd-exposure impaired glucose homeostasis in young adult offspring. Exposure-related increases in circulating triglycerides and hepatic steatosis were apparent only in females. By PND120, Cd-exposed females had become 30% heavier with 700% more perigonadal fat than unexposed control females. There was no evidence of dyslipidemia, steatosis, increased weight gain, nor increased adiposity in Cd-exposed male offspring. Hepatic transcriptome analysis at PND1, PND21, and PND42 revealed evidence for female-specific increases in oxidative stress and mitochondrial dysfunction with significant early disruption of retinoic acid signaling and altered insulin receptor signaling consistent with hepatic insulin sensitivity in adult females. The observed steatosis and metabolic syndrome-like phenotypes resulting from exposure to 500 ppb CdCl<sub>2</sub> during the pre- and perinatal period of development equivalent to human gestation indicate that Cd acts developmentally as a sex-specific delayed obesogen.</p>
Classification of obesity levels based on eating habits and physical condition
<p>This dataset encompasses information intended for the assessment of obesity levels among individuals in the nations of Mexico, Peru, and Colombia.</p><p>The main dataset is prepared by other authors in the article (https://doi.org/10.1016/j.dib.2019.104344) I have only used this dataset to perform my final project related to the Homework Assignment 6: Machine Learning Application in Project Dataset. <br>Here is some detaied explanation about the dataset:<br> </p><p><strong>The attributes related with eating habits are:</strong></p><ol><li>Frequent consumption of high caloric food (FAVC)</li><li>Frequency of consumption of vegetables (FCVC)</li><li>Number of main meals (NCP)</li><li>Consumption of food between meals (CAEC)</li><li>Consumption of water daily (CH20)</li><li>Consumption of alcohol (CALC)</li></ol><p><strong>The attributes related with the physical condition are:</strong></p><ol><li>Calories consumption monitoring (SCC)</li><li>Physical activity frequency (FAF)</li><li>Time using technology devices (TUE)</li><li>Transportation used (MTRANS)</li></ol><p><strong>other variables obtained were:</strong></p><ol><li>Gender</li><li>Age</li><li>Height</li><li>Weight</li><li>family history with overweight</li><li>SMOKE activity</li></ol><p>Finally, all data was labeled and the class variable NObesity was created with the values of:</p><p>a) Insufficient Weight</p><p>b) Normal Weight</p><p>c) Overweight Level I</p><p>d) Overweight Level II</p><p>e) Obesity Type I</p><p>f) Obesity Type II</p><p>g) Obesity Type III</p>
Summary statistics from "Sex-Specific Causal Relations between Steroid Hormones and Obesity—A Mendelian Randomization Study"
<p>GWAMA summary statistics of four steroid hormone levels and one steroid hormone ratio using fixed-effect model.</p> <p>When using this data, please cite: Pott J, Horn K, Zeidler R, et al.. Sex-Specific Causal Relations between Steroid Hormones and Obesity - A Mendelian Randomization Study. <em>Metabolites</em> <strong>2021</strong>, <em>11</em>, 738. https://doi.org/10.3390/metabo11110738</p> <p>All txt files contain the following columns:</p> <ul> <li>markername</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>ea (effect allele)</li> <li>oa (other allele)</li> <li>eaf (effect allele frequency)</li> <li>info (minimal info score across all used studies)</li> <li>nSamples (sample size per SNP)</li> <li>nStudies (number of studies)</li> <li>beta (effect estimate)</li> <li>se (standard error)</li> <li>p (p-value)</li> <li>I2 (SNP heterogeneity across studies)</li> <li>phenotype (phenotyp setting)</li> </ul>
Perceived academic stress, hair and saliva cortisol concentrations, and their relationship with anthropometric measures associated with obesity in first-year medical students.
<p>Cortisol plays an important role between stress, weight gain, and the development of obesity. Therefore, we investigated the association between stress, eating behavior, cortisol, and anthropometric measures related to obesity in a sample of medical students. We determined cortisol concentrations by ELISA and related it to self-reported stress, eating behavior, and anthropometric measurements throughout the academic period. We report an increase in hair cortisol, higher self-reported stress scores, and BMI mainly in females. Finally, we found evidence of positive associations between capillary cortisol and BMI. Also, eating behavior is affected by perceived stress mainly in females.</p> <p>In this database, we provide weight variables, BMI, psychometric tests, and hormonal determinations.</p> <p><br> You can see two sheets in the Excel file. Sheet 1 includes the raw data and sheet 2 includes a description of each variable, as well as the bibliography (article or book) where each survey or protocol was obtained.</p> <p><br> In addition, the data specify the units of the hormonal variables, as well as the units of the anthropometric variables. If you have questions about the interpretation of psychometric test scores, you can contact our team for further details.</p> <p> </p>
Obesity reshapes the microbial population structure along the gut-liver-lung axis in mice
<p>Data repository for the paper: Galaris A., Fanidis D. et al. <em>Obesity reshapes the microbial population structure along the gut-liver-lung axis in mice</em>.<em> </em>2021</p> <p>For further data requests and questions please contact the corresponding author of the respective publication.</p> <p>All fastq files have been processed to remove human and mouse sequences.</p>
Dataset: Tema Obesity & Cardiometabolic ETF (HRTS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Figure 2 in The effects of feeding obese rats with bee bread on leptin and ghrelin expression
Figure 2. Cells stained with ghrelin (A) and leptin (B) in hypothalamus tissue are shown with black arrows. A: Group 1 (fed with standard rat food), B: Group 2 (obese - fed with high-fat diet), C: Group 3 (high-fat diet and 100 mg/kg/day bee bread), D: Group 4 (high-fat diet and 200 mg/kg/day bee bread), E: Group 5 (high-fat diet and 300 mg/kg/day metformin). Image zoom 400×.
Figure 1 in The effects of feeding obese rats with bee bread on leptin and ghrelin expression
Figure 1. Cells, stained with hematoxylin and eosin in hypothalamus tissue are shown with black arrows. Image zoom 400×. A: Group 1 (fed with standard rat food), B: Group 2 (obese - fed with high-fat diet), C: Group 3 (high-fat diet and 100 mg/kg/day bee bread), D: Group 4 (high-fat diet and 200 mg/kg/day bee bread), E: Group 5 (high-fat diet and 300 mg/kg/day metformin).
Metallomics in childhood obesity, insulin resistance, and related susceptibility factors
<p>Metallomics data from plasma and red blood cell (RBC) samples collected from a population-based cohort of children with obesity and healthy controls</p>
Metabolomics in childhood obesity, insulin resistance, and related susceptibility factors
<p>Metabolomics data from plasma and red blood cell (RBC) samples collected from a population-based cohort of children with obesity and healthy controls</p>
CHILDHOOD OBESITY IN URBAN AND RURAL INDIA: A SYSTEMATIC REVIEW AND META-ANALYSES OF PREVALENCE STUDIES
<p><strong><span>Background:</span></strong><span> Childhood obesity has become a pressing global public health issue, particularly in low- and middle-income countries like India. This systematic review aims to investigate the prevalence of childhood obesity and its associated risk factors in urban and rural regions of India.</span></p> <p><strong><span>Methods:</span></strong><span> A comprehensive systematic search was conducted in PubMed, Embase, and Scopus databases to identify relevant English-language studies published within the past decade. Inclusion criteria included studies conducted in India, focusing on children and adolescents aged 0-18, and reporting either the prevalence of childhood obesity or related risk factors. Ten studies, comprising both cross-sectional and quantitative research designs, met these criteria.</span></p> <p><strong><span>Results:</span></strong><span> The findings reveal a significant disparity in childhood obesity prevalence between urban and rural areas of India. Urban regions exhibit notably higher rates, with a pooled prevalence estimated at 9.0% (95% CI: 2.0 to 17), compared to 4.0% (95% CI: 4.0 to 5.0) in rural areas. Risk factors associated with childhood obesity in urban settings include unhealthy dietary habits, limited physical activity, higher income levels, parental education, and attendance at private schools. In rural areas, gender, age, and household size emerged as potential risk factors.</span></p> <p><strong><span>Discussion:</span></strong><span> These findings underscore the urgent need for geographically tailored interventions to address the urban-rural disparities in childhood obesity. Lifestyle-oriented strategies promoting healthier dietary patterns and increased physical activity are essential. Gender-inclusive programs targeting both boys and girls are crucial. Future research should consider regional and cultural diversity to design more effective public health responses.</span></p> <p><strong><span>Conclusion:</span></strong><span> This systematic review provides valuable insights into the prevalence and risk factors of childhood obesity in India. It highlights the necessity for customized interventions and lifestyle adjustments to combat this escalating public health challenge and reduce disparities in health outcomes.</span></p>
Underestimation of Body Size in Patients with Obesity
<p>The database includes real and estimated measures (in centimeters) of shoulders, waist, and hips widths and circumferences of 22 female inpatients with obesity and 21 healthy-weight women. The database includes also their age and years of education, and a measure of estimation accuracy (Delta), that is the difference between the real and estimated size of each body part.</p>
Parent-reported child appetite moderates relationships between child genetic obesity risk and parental feeding practices
<p>Data used in the manuscript "Parent-reported child appetite moderates relationships between child genetic obesity risk and parental feeding practices" by Jansen E., Naymik M., Thapaliya G., Huentelman M., Beauchemin J., D'Sa V., Lewis C.R., Deoni S., RESONANCE consortium, & Carnell S.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.