West Virginia, Mississippi, and Arkansas are the U.S. states with the highest percentage of their population who are obese. The states with the lowest percentage of their population who are obese include Colorado, Hawaii, and Massachusetts. Obesity in the United States Obesity is a growing problem in many countries around the world, but the United States has the highest rate of obesity among all OECD countries. The prevalence of obesity in the United States has risen steadily over the previous two decades, with no signs of declining. Obesity in the U.S. is more common among women than men, and overweight and obesity rates are higher among African Americans than any other race or ethnicity. Causes and health impacts Obesity is most commonly the result of a combination of poor diet, overeating, physical inactivity, and a genetic susceptibility. Obesity is associated with various negative health impacts, including an increased risk of cardiovascular diseases, certain types of cancer, and diabetes type 2. As of 2022, around 8.4 percent of the U.S. population had been diagnosed with diabetes. Diabetes is currently the eighth leading cause of death in the United States.

Obesity prevalence among children and adolescents (crude estimate) (%)

  Dataset Description

This dataset provides information on 'Obesity prevalence among children and adolescents' for countries in the WHO African Region. The data is disaggregated by the 'Sex' dimension, allowing for analysis of health inequalities across different population subgroups. Units: crude estimate

  Dimensions and Subgroups

Dimension: Sex Available Subgroups: Female, Male

  Data… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/obesity-prevalence-among-children-and-adolescentsby-sex-for-african-countries.

This report presents information on obesity, physical activity and diet drawn together from a variety of sources for England. More information can be found in the source publications which contain a wider range of data and analysis. Each section provides an overview of key findings, as well as providing links to relevant documents and sources. Some of the data have been published previously by NHS Digital. A data visualisation tool (link provided within the key facts) allows users to select obesity related hospital admissions data for any Local Authority (as contained in the data tables), along with time series data from 2013/14. Regional and national comparisons are also provided. The report includes information on: Obesity related hospital admissions, including obesity related bariatric surgery. Obesity prevalence. Physical activity levels. Walking and cycling rates. Prescriptions items for the treatment of obesity. Perception of weight and weight management. Food and drink purchases and expenditure. Fruit and vegetable consumption. Key facts cover the latest year of data available: Hospital admissions: 2018/19 Adult obesity: 2018 Childhood obesity: 2018/19 Adult physical activity: 12 months to November 2019 Children and young people's physical activity: 2018/19 academic year

Obesity prevalence among adults, BMI>=30 (crude estimate) (%)

  Dataset Description

This dataset provides information on 'Obesity prevalence among adults, BMI>=30' for countries in the WHO African Region. The data is disaggregated by the 'Sex' dimension, allowing for analysis of health inequalities across different population subgroups. Units: crude estimate

  Dimensions and Subgroups

Dimension: Sex Available Subgroups: Female, Male

  Data Structure

The… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/obesity-prevalence-among-adults-bmi-30by-sex-for-african-countries.

In 2023, it was estimated that around 37 percent of adults with an annual income of less than 15,000 U.S. dollars were obese, compared to 29 percent of those with an annual income of 75,000 dollars or more. This statistic shows the percentage of U.S. adults who were obese in 2023, by income.

BackgroundIn India, the prevalence of overweight and obesity has increased rapidly in recent decades. Given the association between overweight and obesity with many non-communicable diseases, forecasts of the future prevalence of overweight and obesity can help inform policy in a country where around one sixth of the world’s population resides.MethodsWe used a system of multi-state life tables to forecast overweight and obesity prevalence among Indians aged 20–69 years by age, sex and urban/rural residence to 2040. We estimated the incidence and initial prevalence of overweight using nationally representative data from the National Family Health Surveys 3 and 4, and the Study on global AGEing and adult health, waves 0 and 1. We forecasted future mortality, using the Lee-Carter model fitted life tables reported by the Sample Registration System, and adjusted the mortality rates for Body Mass Index using relative risks from the literature.ResultsThe prevalence of overweight will more than double among Indian adults aged 20–69 years between 2010 and 2040, while the prevalence of obesity will triple. Specifically, the prevalence of overweight and obesity will reach 30.5% (27.4%-34.4%) and 9.5% (5.4%-13.3%) among men, and 27.4% (24.5%-30.6%) and 13.9% (10.1%-16.9%) among women, respectively, by 2040. The largest increases in the prevalence of overweight and obesity between 2010 and 2040 is expected to be in older ages, and we found a larger relative increase in overweight and obesity in rural areas compared to urban areas. The largest relative increase in overweight and obesity prevalence was forecast to occur at older age groups.ConclusionThe overall prevalence of overweight and obesity is expected to increase considerably in India by 2040, with substantial increases particularly among rural residents and older Indians. Detailed predictions of excess weight are crucial in estimating future non-communicable disease burdens and their economic impact.

Overweight prevalence among adults, BMI>=25 (crude estimate) (%)

  Dataset Description

This dataset provides information on 'Overweight prevalence among adults, BMI>=25' for countries in the WHO African Region. The data is disaggregated by the 'Sex' dimension, allowing for analysis of health inequalities across different population subgroups. Units: crude estimate

  Dimensions and Subgroups

Dimension: Sex Available Subgroups: Female, Male

  Data Structure… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/overweight-prevalence-among-adults-bmi-25by-sex-for-african-countries.

Context: Currently it is not well understood to what extent there are obesity inequalities by socioeconomic status (SES) in urban Latin America. Objective: This study reviewed the literature assessing associations between overweight, obesity and SES in adults. Data sources: Pubmed and Scielo databases. Data extraction: Data extraction was conducted using the PRISMA guidelines. We extracted data on the direction of the association between SES (e.g. education and income), overweight (BMI ≥25 and <30 kg/m2) and obesity (BMI≥30 kg/m2) in Latin American urban regions. Relative differences between low and high SES groups were assessed and defined a priori as significant at p<0.05. Data analysis: Thirty-one studies met our inclusion criteria and most were conducted in Brazil (22) and Mexico. Only one study presented just non-significant associations. Fifty percent of associations between education or income and overweight were negative/inverse. Regarding obesity, 80% were negative and 20% positive. Most negative associations were found in women. Associations between BMI and SES usually followed the same pattern, except in men where they varied depending on the indicator used. Conclusion: Low SES individuals in urban Latin America, especially women, have higher BMI levels highlighting the need for interventions.

IntroductionThe Centers for Disease Control and Prevention (CDC) and World Health Organization (WHO) created separate growth charts for girls and boys because growth patterns and rates differ between sexes. However, scenarios exist in which this dichotomizing “girls versus boys” approach may not be ideal, including the care of non-binary youth or transgender youth undergoing transitions consistent with their gender identity. There is therefore a need for growth charts that age smooth differences in pubertal timing between sexes to determine how youth are growing as “children” versus “girls or boys” (e.g., age- and sex-neutral, compared to age- and sex-specific, growth charts).MethodsEmploying similar statistical techniques and datasets used to create the CDC 2000 growth charts, we developed age-adjusted, sex non-specific growth charts for height, weight, and body mass index (BMI), and z-score calculators for these parameters. Specifically, these were created using anthropometric data from five US cross-sectional studies including National Health Examination Surveys II-III and National Health and Nutrition Examination Surveys I-III. To illustrate contemporary clinical practice, we overlaid our charts on CDC 2000 girls and boys growth charts.Results39,119 youth 2-20 years old (49.5% female; 66.7% non-Hispanic White; 21.7% non-Hispanic Black) were included in the development of our growth charts, reference ranges, and z-score calculators. Respective curves were largely superimposable through around 10 years of age after which, coinciding with pubertal onset timing, differences became more apparent.DiscussionWe conclude that age-adjusted, sex non-specific growth charts may be used in clinical situations such as transgender youth in which standard “girls versus boys” growth charts are not ideal. Until longitudinal auxological data are available in these populations, our growth charts may help to assess a transgender youth’s growth trajectory and weight classification, and expectations surrounding these.

Tanzania TZ: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data was reported at 5.000 % in 2010. This records an increase from the previous number of 4.300 % for 2004. Tanzania TZ: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 4.300 % from Dec 1991 (Median) to 2010, with 5 observations. The data reached an all-time high of 5.600 % in 1991 and a record low of 1.800 % in 1999. Tanzania TZ: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tanzania – Table TZ.World Bank: Health Statistics. Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

This Data set is from the Behavioral Risk Factor Surveillance System survey of the United States. "The Behavioral Risk Factor Surveillance System (BRFSS) is the worlds largest, on-going telephone health survey system, tracking health conditions and risk behaviors in the United States yearly since 1984. Conducted by the 50 state health departments as well as those in the District of Columbia, Puerto Rico, Guam, and the U.S. Virgin Islands with support from the CDC, BRFSS provides state-specific information about issues such as asthma, diabetes, health care access, alcohol use, hypertension, obesity, cancer screening, nutrition and physical activity, tobacco use, and more." (http://www.cdc.gov/brfss/index.htm) Data URL: http://www.cdc.gov/brfss/maps/gis_data.htm All values a percentage from 0-100

Introduction: The prevalence of hyperuricemia is increasing world widely; the understanding of population attributable faction of modifiable risk factors is important for disease prevention. Given the sparse evidence on how modifiable risk factors influence hyperuricemia in mainland China, we aim to explore the effect of excess weight and alcohol consumption and the population attributable fractions of hyperuricemia based on a national survey in mainland China. Methods: Using data from China National Health Survey which included 31746 Han Chinese aged 20-80 from ten provinces, we estimated the prevalence and modifiable risk factors (overweight/obesity and alcohol consumption)of hyperuricemia. Hyperuricemia was defined as serum uric acid > 417 μmol/L in men and > 340 μmol/L in women. Restricted cubic spline models were used to demonstrate the linear and non-linear association between exposures and hyperuricemia. The adjusted population attributable risk (PAR) was calculated to understand the relative importance of each modifiable risk factor. Results: The prevalence of hyperuricemia was 25.1% in men and 15.9% in women. The population fraction of hyperuricemia cases that could be avoided by weight loss was 20.6% (19.2% to 22.0%) in men and 18.1% (17.1% to 19.0%) in women. The PAR of alcohol consumption was 12.8% (8.5% to 17.1%) in men. Participants from southwest China had the highest hyperuricemia prevalence (47.9% in men and 29.9% in women), but with lower PAR of modifiable risk factors, especially in men (16.7%). Subjects in North China had lower hyperuricemia prevalence but higher PAR of modifiable risk factors. 44.8% male hyperuricemia cases in Inner Mongolia (26.9% of hyperuricemia prevalence) and 37.7% cases in men from Heilongjiang (34.4% of hyperuricemia prevalence) were attributable to overweight/obesity and alcohol consumption. Conclusion: There are significant sex and geographic difference on population attributable risk of hyperuricemia due to modifiable risk factors. More tailored prevention strategies are needed to prevent hyperuricemia through weight loss and the reduction of alcohol consumption.

BackgroundExposure to light at night (LAN) is a potent disruptor of the circadian system. Whether LAN exposure exerts a sex- or age-specific influence on obesity needs investigation.ObjectivesTo estimate the sex- and age-specific associations of exposure to outdoor LAN and obesity based on a national and cross-sectional survey.MethodsThe study included a nationally representative sample of 98,658 adults aged ≥ 18 years who had lived in their current residence for ≥ 6 months from 162 study sites across mainland China in 2010. Outdoor LAN exposure was estimated from satellite imaging data. General obesity was defined as body-mass index (BMI) ≥ 28 kg/m2 and central obesity was defined as waist circumference ≥ 90 cm in men and ≥ 85 cm in women. Linear and logistic regression models were used to examine the associations between LAN exposure and prevalent obesity in sex and age categories.ResultsA monotonically increasing association of outdoor LAN with BMI, waist circumference was observed in all sex and age categories, except for adults aged 18-39 years. Significant associations of LAN exposure with prevalent obesity were observed in each sex and age category, especially in men and older people. Per 1-quintile increase in LAN was associated with 14% increased odds of general obesity in men (odds ratio, OR=1.14, 95% confidence interval, CI=1.07-1.23) and 24% in adults aged ≥ 60 years (OR=1.24, 95% CI=1.14-1.35). Per 1-quintile increase in LAN was associated with 19% increased odds of central obesity in men (OR=1.19, 95% CI=1.11-1.26) and 26% in adults aged ≥ 60 years (OR=1.26, 95% CI=1.17-1.35).ConclusionsIncreased chronic outdoor LAN exposure was associated with increased prevalence of obesity in sex- and age- specific Chinese populations. Public health policies on reducing light pollution at night might be considered in obesity prevention.

Joint classification on obesity prevalence by waist-to-height ratio (WHtR) and body mass index (BMI) in men and women.

Abstract

Purpose: The multi-country Study on Global Ageing and Adult Health (SAGE) is run by the World Health Organization's Multi-Country Studies unit in the Innovation, Information, Evidence and Research Cluster. SAGE is part of the unit's Longitudinal Study Programme which is compiling longitudinal data on the health and well-being of adult populations, and the ageing process, through primary data collection and secondary data analysis. SAGE baseline data (Wave 0, 2002/3) was collected as part of WHO's World Health Survey http://www.who.int/healthinfo/survey/en/index.html (WHS). SAGE Wave 1 (2007/10) provides a comprehensive data set on the health and well-being of adults in six low and middle-income countries: China, Ghana, India, Mexico, Russian Federation and South Africa. Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions

Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults

Methods: SAGE's first full round of data collection included both follow-up and new respondents in most participating countries. The goal of the sampling design was to obtain a nationally representative cohort of persons aged 50 years and older, with a smaller cohort of persons aged 18 to 49 for comparison purposes. In the older households, all persons aged 50+ years (for example, spouses and siblings) were invited to participate. Proxy respondents were identified for respondents who were unable to respond for themselves. Standardized SAGE survey instruments were used in all countries consisting of five main parts: 1) household questionnaire; 2) individual questionnaire; 3) proxy questionnaire; 4) verbal autopsy questionnaire; and, 5) appendices including showcards. A VAQ was completed for deaths in the household over the last 24 months. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey.

Content Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations

Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions and Vignettes 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilization 6000 Social Cohesion 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

Geographic coverage

National coverage

Analysis unit

households and individuals

Universe

The household section of the survey covered all households in 19 of the 28 states in India which covers 96% of the population. Institutionalised populations are excluded. The individual section covered all persons aged 18 years and older residing within individual households.

Kind of data

Sample survey data [ssd]

Sampling procedure

World Health Survey Sampling India has 28 states and seven union territories. 19 of the 28 states were included in the design representing 96% of the population. India used a stratified multistage cluster sample design. Six states were selected in accordance with their geographic location and level of development. Strata were defined by the 6 states:(Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh and West Bengal), and locality (urban or rural). There are 12 strata in total. The 2000 Census demarcation was used as the sampling frame. Two stage and three stage sampling was adopted in rural and urban areas, respectively. In rural areas PSUs(villages) were selected probability proportional to size. The measure of size being the 2001 Census population in the village. SSUs (households) were selected using systematic sampling. TSUs (individuals) were selected using Kish tables. In urban areas, PSUs(city wards) were selected probability proportional to size. SSUs(census enumeration blocks), two were randomly selected from each PSU. TSU (households) were selected using systematic sampling. QSU (individuals) were selected as in rural areas. A sample of 379 EAs was selected as the primary sampling units(PSU).

SAGE Sampling The SAGE sample was pre-determined as all PSUs and households selected for the WHS/SAGE Wave 0 survey were included. Exceptions are three PSUs in Assam which were replaced as they were inaccessible due to flooding. And a further six PSUs were omitted for which the household roster information was not available. In each selected EA, a listing of the households was conducted to classify each household into the following mutually exclusive categories: 1)Households with a WHS/SAGE Wave 0 respondent aged 50-plus: all members aged 50-plus including the WHS/SAGE Wave 0 respondent were eligible for the individual interview. 2)Households with a WHS/SAGE Wave 0 respondent aged 47-49: all members aged 50-plus including the WHS/SAGE Wave 0 respondent aged 47-49 was eligible for the individual interview. 3)Households with a WHS/SAGE Wave 0 female respondent aged 18-46: all females members aged 18-49 including the WHS/SAGE Wave 0 female respondent aged 18-46 were eligible for the individual interview. 4)Households with a WHS/SAGE Wave 0 male respondent aged 18-46: three households were selected using systematic sampling and one male aged 18-49 was eligible for the individual interview. In the households not selected, all members aged 50-plus were eligible for the individual interview.

Stages of selection Strata: State, Locality=12 PSU: EAs=375 surveyed SSU: Households=10424 surveyed TSU: Individual=12198 surveyed

Mode of data collection

Face-to-face [f2f] PAPI

Research instrument

The questionnaires were based on the WHS Model Questionnaire with some modification and many new additions. A household questionnaire was administered to all households eligible for the study. A Verbal Autopsy questionnaire was administered to households that had a death in the last 24 months. An Individual questionniare was administered to eligible respondents identified from the household roster. A Proxy questionnaire was administered to individual respondents who had cognitive limitations. A Womans Questionnaire was administered to all females aged 18-49 years identified from the household roster. The questionnaires were developed in English and were piloted as part of the SAGE pretest in 2005. All documents were translated into Hindi, Assamese, Kanada and Marathi. SAGE generic questionnaires are available as external resources.

Cleaning operations

Data editing took place at a number of stages including: (1) office editing and coding (2) during data entry (3) structural checking of the CSPro files (4) range and consistency secondary edits in Stata

Response rate

Household Response rate=88% Cooperation rate=92%

Individual: Response rate=68% Cooperation rate=92%

Indicators also include behavioral risk factors (tobacco use, alcohol consumption, physical inactivity, overweight/obesity, cholesterol) and hypertension awareness, treatment, and control estimates. This table provides the quantitative foundation for regional comparisons of cardiometabolic disease burden and risk profiles across Caribbean and North American countries. (XLSX)

BackgroundIn Spain, differences in the prevalence of obesity and excess weight according to sex and sociodemographic factors have been described at the national level, although current data do not allow to delve into geographical differences for these conditions. The aim was to estimate national and regional prevalences of adult obesity and excess weight in Spain by sex and sociodemographic characteristics, and to explore difference sources of inequalities in its distribution, as well as its geographical pattern.MethodENE-COVID study was a nationwide representative seroepidemiological survey with 57,131 participants. Residents in 35,893 households were selected from municipal rolls using a two-stage random sampling stratified by province and municipality size (April–June 2020). Participants (77.0% of contacted individuals) answered a questionnaire which collected self-reported weight and height, as well as different socioeconomic variables, that allowed estimating crude and standardized prevalences of adult obesity and excess weight.ResultsCrude prevalences of obesity and excess weight were higher in men (obesity: 19.3% vs. 18.0%; excess weight: 63.7% vs. 48.4%), while severe obesity was more prevalent in women (4.5% vs. 5.3%). These prevalences increased with age and disability, and decreased with education, census tract income and municipality size. Differences by educational level, relative census income, nationality or disability were clearly higher among women. Obesity by province ranged 13.3–27.4% in men and 11.4–28.1% in women; excess weight ranged 57.2–76.0% in men and 38.9–59.5% in women. The highest prevalences were located in the southern half of the country and some north-western provinces. Sociodemographic characteristics only explained a small part of the observed geographical variability (25.2% obesity).ConclusionObesity and overweight have a high prevalence in Spain, with notable geographical and sex differences. Socioeconomic inequalities are stronger among women. The observed geographical variability suggests the need to implement regional and local interventions to effectively address this public health problem.

The Diabetes Prevention Program (DPP) is a clinical trial that investigated whether modest weight loss through dietary changes and increased physical activity or treatment with the oral diabetes drug metformin (Glucophage) could prevent or delay the onset of type 2 diabetes in high risk individuals with prediabetes.

The study enrolled overweight persons with elevated fasting and post-load plasma glucose concentrations. Participants were randomized to placebo, metformin (850 mg twice daily), or a lifestyle-modification program with the goals of at least a 7 percent weight loss and at least 150 minutes of physical activity per week. The primary outcome measure was development of diabetes, diagnosed on the basis of an annual oral glucose-tolerance test or a semiannual fasting plasma glucose test, according to the 1997 criteria of the American Diabetes Association: a value for plasma glucose of 126 mg per deciliter (7.0 mmol per liter) or higher in the fasting state, or 200 mg per deciliter (11.1 mmol per liter) or higher two hours after a 75-g oral glucose load. Participation in DPP continued after a diagnosis of diabetes was made, although study medication was discontinued and participants were sent to their local primary care provider for treatment of diabetes once fasting glucose was > 140 mg/dl.

Results showed that both lifestyle changes and treatment with metformin reduced the incidence of diabetes in persons at high risk compared with placebo. Furthermore, the lifestyle intervention was more effective than metformin in preventing the onset of diabetes.

Supplemental measurements were collected using biospecimens that were obtained during the original DPP clinical trial. These measurements included antibodies, biomarkers, hormones, and vitamin D levels to assess the relationships between sex hormones, diabetes risk factors, and the progression to diabetes. The supplemental data showed that sex hormones were associated with diabetes risk in men, but these associations were not found in women. Furthermore, obesity and glycemia were more important predictors of diabetes risk than sex hormones.

BackgroundAlthough the prevalence of obesity and metabolic syndrome (MetS) among dialysis patients has been exceeding than general population, little is known regarding obesity and MetS in non-dialysis chronic kidney disease (CKD). We aimed to find the magnitude of obesity and MetS and their associations with impaired renal function among type 2 diabetes mellitus (T2DM) patients.MethodsA national survey of T2DM patients was collected in the Thai National Health Security Office database during 2014–5. The sampling frame was designated as distinct geographic regions throughout the country. A stratified two-stage cluster sampling was used to select the study population. Anthropometry and 12-hour fasting blood samples were obtained by trained personnel. BMI of ≥25 kg/m2 was classified as obesity. MetS was defined as having elevated waist circumference (>90 and >80 cm in men and women, respectively) plus any two of the followings: triglyceride ≥150 mg/dL, HDL-C

We aimed to investigate the association of various obesity parameters and phenotypes with hypertension in nationally representative Korean adults. Among adults aged 19 years and older who participated in the Korea National Health and Nutrition Examination Survey in 2008–2010, a total of 16,363 subjects (8,184 men and 8,179 women) were analyzed. Hypertension was defined as blood pressure of 140/90 mm Hg or higher or taking antihypertensive medication. Multiple logistic regression analysis was used to estimate multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Higher obesity parameters [body mass index (BMI) representing general obesity, waist circumference (WC) representing central obesity, and percentage body fat (PBF) representing elevated body fat] were consistently associated with increased odds of prevalent hypertension (OR, 7.54; 95% CI, 5.89–9.65 for BMI ≥30 vs. 18.5–23; OR, 3.97; 95% CI, 3.41–4.63 for WC ≥95 cm in males and ≥90 cm in females vs.