Surveys in which U.S. adults report their current weight have shown that the share of those reporting they weigh 200 pounds or more has increased over the past few decades. In 2024, around 28 percent of respondents reported their weight as 200 pounds or more, compared to 15 percent in 1990. However, the same surveys show the share of respondents who report they are overweight has decreased compared to figures from 1990. What percentage of the U.S. population is obese? Obesity is an increasing problem in the United States that is expected to become worse in the coming decades. As of 2023, around one third of adults in the United States were considered obese. Obesity is slightly more prevalent among women in the United States, and rates of obesity differ greatly by region and state. For example, in West Virginia, around 41 percent of adults are obese, compared to 25 percent in Colorado. However, although Colorado is the state with the lowest prevalence of obesity among adults, a quarter of the adult population being obese is still shockingly high. The health impacts of being obese Obesity increases the risk of developing a number of health conditions including high blood pressure, heart disease, type 2 diabetes, and certain types of cancer. It is no coincidence that the states with the highest rates of hypertension are also among the states with the highest prevalence of obesity. West Virginia currently has the third highest rate of hypertension in the U.S. with 45 percent of adults with the condition. It is also no coincidence that as rates of obesity in the United States have increased so have rates of diabetes. As of 2022, around 8.4 percent of adults in the United States had been diagnosed with diabetes, compared to six percent in the year 2000. Obesity can be prevented through a healthy diet and regular exercise, which also increases overall health and longevity.

In 2024, the mean average weight reported by men was 195 pounds, while the mean average weight for women was 164 pounds. This statistic shows the mean self-reported weight among U.S. adults from 1990 to 2024, by gender, in pounds.

This statistic depicts the average body weight of U.S. men aged 20 years and over from 1999 to 2016, by ethnicity. According to the data, the average male body weight for those that identified as non-Hispanic white has increased from 192.3 in 1999-2000 to 202.2 in 2015-2016.

Data on overweight and obesity among adults aged 20 and over in the United States, by selected characteristics, including sex, age, race, Hispanic origin, and poverty level. Data are from Health, United States. SOURCE: National Center for Health Statistics, National Health and Nutrition Examination Survey. Search, visualize, and download these and other estimates from over 120 health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.

This statistic depicts the average male body weight of U.S. adults aged 20 years and over from 1999 to 2016. According to the data, the average male body weight for those aged 40-59 years was ***** in 1999-2000 and increased to ***** as of 2015-2016.

Data on normal weight, overweight, and obesity among adults aged 20 and over by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time.

SOURCE: NCHS, National Health and Nutrition Examination Survey. For more information on the National Health and Nutrition Examination Survey, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

Observed and age-standardised proportion of adults with a Body Mass Index (BMI) greater than 30 kg/m2. To help reduce the prevalence of obesity. Legacy unique identifier: P00848

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Epidemiological BMI of Children by Gender (2001-2023)

"Epidemiological" refers to the study of how health conditions, such as diseases and BMI, spread and affect populations.

"Body Mass Index" (BMI) helps assess if an individual has a healthy body weight relative to their height. It categorizes people as underweight, normal weight, overweight, or obese.

This dataset analyzes BMI data by gender from 2001-2023, offering insights into trends across different populations and genders, with a focus on identifying health patterns and risks.

Why Use This Dataset?

This dataset offers valuable insights into childhood BMI trends, categorized by gender, hospital board, and school year. It is useful for analyzing health trends, building predictive models, and offering actionable health insights.

Key Features

• Regional & Demographic Analysis: Explore BMI trends across hospitals
• Time-Based Trends: Observe changes in BMI categories over time
• Predictive Modeling: Build models to predict future BMI distributions

Source: Open Data from NHS – UK Open Government Licence (OGL)

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Background and aimsIncreased body mass index (BMI = weight/height2; kg/m2) and weight gain is associated with increased mortality, wherefore weight loss and avoided weight gain should be followed by lower mortality. This is achieved in clinical settings, but in the general population weight loss appears associated with increased mortality, possibly related to the struggles with body weight control (BWC). We investigated whether attitudes to and experiences with BWC in combination with recent changes in body weight influenced long-term mortality among normal weight and overweight individuals.Population and methodsThe study population included 6,740 individuals attending the 3rd cycle in 1991–94 of the Copenhagen City Heart Study, providing information on BMI, educational level, health behaviours, well-being, weight half-a-year earlier, and answers to four BWC questions about caring for body weight, assumed benefit of weight loss, current and past slimming experiences. Participants reporting previous unintended weight loss (> 4 kg during one year) were excluded. Cox regression models estimated the associations of prior changes in BMI and responses to the BWC questions with approximately 22 years all-cause mortality with age as ‘time scale’. Participants with normal weight (BMI < 25.0 kg/m2) and overweight (BMI ≥ 25.0 kg/m2) were analysed separately, and stratified by gender and educational level, health behaviours and well-being as co-variables.ResultsCompared with stable weight, weight loss was associated with significantly increased mortality in the normal weight group, but not in the overweight group, and weight gain was not significantly associated with mortality in either group. Participants with normal weight who claimed that it would be good for their health to lose weight or that they were currently trying to lose weight had significantly higher mortality than those denying it. There were no other significant associations with the responses to the BWC questions in either the normal weight or the overweight group. When combining the responses to the BWC questions with the weight changes, using the weight change as either a continuous or categorical variable, there were no significant interaction in their relation to mortality in either the normal weight or the overweight group.ConclusionAttitudes to and experiences with BWC did not notably modify the association of changes in body weight with mortality in either people with normal weight or people with overweight.

In 2024, around 16 percent of U.S. women reported weighing 200 pounds or more. This statistic shows the average self-reported weight among U.S. women from 1990 to 2024.

The spreadsheet contains regional level obesity trend data from the the HSE, BMI data from Understanding Society, and adjusted prevalence of underweight, healthy weight, overweight, and obesity by local authority from the Active People Survey.

Understanding Society data shows the percentage of the population aged 10 and over by their Body Mass Index Classification, covering underweight, normal weight, overweight, and three classes of obesity.

Questions on self-reported height and weight were added to the Sport England Active People Survey (APS) in January 2012 to provide data for monitoring excess weight (overweight including obesity, BMI ≥25kg/m2) in adults (age 16 and over) at local authority level for the Public Health Outcomes Framework (PHOF).

Health Survey for England (HSE) results at a national level are available on the NHS Information Centre website.

Other NHS indicators on obesity are available for Strategic Health Authorities (SHA).

Relevant links: http://discover.ukdataservice.ac.uk/series/?sn=2000053

http://www.noo.org.uk/visualisation/adult_obesity

ObjectiveTo investigate the association of dynamic weight change in adulthood with leukocyte telomere length among U.S. adults.MethodsThis study included 3,886 subjects aged 36-75 years from the National Health and Nutrition Examination Survey (NHANES) 1999-2002 cycle. Survey-weighted multivariable linear regression with adjustments for potential confounders was utilized.Results3,386 individuals were finally included. People with stable obesity had a 0.130 kbp (95% CI: 0.061-0.198, P=1.97E-04) shorter leukocyte telomere length than those with stable normal weight (reference group) during the 10-year period, corresponding to approximately 8.7 years of aging. Weight gain from non-obesity to obesity shortened the leukocyte telomere length by 0.094 kbp (95% CI: 0.012-0.177, P=0.026), while normal weight to overweight or remaining overweight shortened the leukocyte telomere length by 0.074 kbp (95% CI: 0.014-0.134, P=0.016). The leukocyte telomere length has 0.003 kbp attrition on average for every 1 kg increase in weight from a mean age of 41 years to 51 years. Further stratified analysis showed that the associations generally varied across sex and race/ethnicity.ConclusionsThis study found that weight changes during a 10-year period was associated with leukocyte telomere length and supports the theory that weight gain promotes aging across adulthood.

BMI (body mass index) has long been used as an indicator to classify one's weight. It is calculated as weight (in kilograms)/(height (in meters)). The Centers for Disease Control and Prevention (CDC) website states "Growth charts are percentile curves showing the distribution of selected body measurements in children. Growth charts are used by pediatricians, nurses, and parents to track the growth of infants, children, and adolescents." For adults, one's weight classification is calculated simply by using their BMI, but for people aged 2 to 20 years of age, the weight classification is calculated differently - it uses percentiles. This is because it accounts for natural growth in children. The dataset is split into two - one for males, and one for females. This is because the percentiles for each gender are different. The weight classifications for children aged 2-20 are as follows: 1. BMI below the 5th percentile is Underweight 2. BMI falls somewhere from the 5th to 85th percentile is Normal weight 3. BMI between the 85th and 95th (inclusive) percentile is At risk of overweight 4. BMI above the 95th percentile is Overweight

The datasets' Age column is given in months, and not years. This allows for a more accurate diagnosis.

Over the past 35 years there has been a near doubling in the worldwide prevalence of obesity. Body Mass Index (BMI) distributions in high-income societies have increasingly shifted rightwards, corresponding to increases in average BMI that are due to well-studied changes in the socioeconomic environment. However, in addition to this shift, BMI distributions have also shown marked changes in their particular shape over time, exhibiting an ongoing right-skewed broadening that is not well understood. Here, we compile and analyze the largest data set so far of year-over-year BMI changes. The data confirm that, on average, heavy individuals become lighter while light individuals become heavier year-over-year, and also show that year-over-year BMI evolution is characterized by fluctuations with a magnitude that is linearly proportional to BMI. We find that the distribution of human BMIs is intrinsically dynamic—due to the short-term variability of human weight—and its shape is determined by a balance between deterministic drift towards a natural set point and diffusion resulting from random fluctuations in, e.g., diet and physical activity. We formulate a stochastic mathematical model for BMI dynamics, deriving a theoretical shape for the BMI distribution and offering a mechanism that may explain the right-skewed broadening of BMI distributions over time. An extension of the base model investigates the hypothesis that peer-to-peer social influence plays a role in BMI dynamics. While including this effect improves the fit with the data, indicating that correlations in the behavior of individuals with similar BMI may be important for BMI dynamics, testing social transmission against other plausible unmodeled effects and interpretations remains the subject of future work. Implications of our findings on the dynamics of BMI distributions for public health interventions are discussed.

Dataset Description: AI-Generated Person Data

This dataset contains 1,001,000 synthetic records representing demographic and physical attributes of individuals. The data is AI-generated and designed to simulate realistic human characteristics without using personally identifiable information (PII).

Structure

Rows: 1,001,000

Columns: 6

Features

id – Unique identifier for each individual (1 to 1,001,000).

dob – Date of birth (ranging across ~32,000 unique values).

age – Age of the person (from -1 to 95, mostly realistic ages but may include outliers like -1).

gender – Binary category (Male, Female), nearly evenly distributed.

height_cm – Height in centimeters (ranging from 50 cm to 209.7 cm).

weight_kg – Weight in kilograms (ranging from 3 kg to 198.9 kg).

Statistical Highlights

Average age: ~38.1 years (with some anomalies).

Average height: ~159.3 cm (std dev ~24.7 cm).

Average weight: ~65.7 kg (std dev ~27.4 kg).

Gender distribution: ~50% Male, ~50% Female.

Applications

This dataset can be used for:

Testing and benchmarking machine learning models.

Simulating healthcare, biometric, or demographic analytics.

Data visualization and statistical analysis practice.

Building and validating data pipelines without real PII.

Objective: We examined the effects of one month of a eucaloric, high-fat (48% of calories) diet (HFD) on gonadotropin secretion in normal weight women to interrogate the role of free fatty acids and insulin in mediating the relative hypogonadotropic hypogonadism of obesity. Methods: Eighteen eumenorrheic women (BMI 18-25 kg/m2) were studied in the early follicular phase of the menstrual cycle before and after exposure to a HFD with frequent blood sampling for LH and FSH, followed by an assessment of pituitary sensitivity to GnRH. Mass spectrometrybased plasma metabolomic analysis was also performed. Paired testing and time series analysis were performed as appropriate. Results: Mean endogenous LH (unstimulated) was significantly decreased after the HFD (4.3 ±1.0 vs 3.8 ± 1.0, P<0.01); mean unstimulated FSH was not changed. Both LH (10.1 ± 1.0 vs 7.2 ± 1.0, P<0.01), and FSH (9.5 ± 1.0 vs 8.8 ± 1.0, P<0.01) response to 75 ng/kg of GnRH were reduced after the HFD. Mean LH pulse amplitude and LH interpulse interval were unaffected by the dietary exposure. Eucaloric HFD exposure did not cause weight change. Plasma metabolomics confirmed adherence with elevation of fasting free fatty acids (especially long-chain mono-, poly- and highly-unsaturated fatty acids) by the last day of the HFD. Conclusion: One-month exposure to a HFD successfully induced key reproductive and metabolic features of Reprometabolic Syndrome in normal weight women. Dietary factors may underly the gonadotrope compromise seen in obesity related subfertility and that therapeutic dietary interventions, independent of weight loss, may be possible.

ABSTRACT Background Obesity is a well known risk factor for the development of metabolic abnormalities. However, some obese people are healthy and on the other hand some people with normal weight have adverse metabolic profile, therefore it can be assumed that there is a difference in physical characteristics amongst these people. The aim of this study was to establish whether there are somatotype differences between metabolically healthy and metabolically obese women who are obese or of normal weight. Subjects and methods Study included 230 women aged 44.76 ± 11.21y. Metabolic status was assessed according to IDF criteria, while somatotype was obtained using Heath & Carter method. Results Significant somatotype differences were observed in the group of women with normal-weight: metabolically healthy women had significantly lower endomorphy, mesomorphy and higher ectomorphy compared to metabolically obese normal-weight women (5.84-3.97-2.21 vs. 8.69-6.47-0.65). Metabolically healthy obese women had lower values of endomorphy and mesomorphy and higher values of ectomorphy compared to ‘at risk’ obese women but the differences were not statistically significant (7.59-5.76-0.63 vs. 8.51-6.58-0.5). Ectomorphy was shown as an important determinant of the favorable metabolic profile (cutoff point was 0.80). Conclusion We concluded that, in addition to fat mass, metabolic profile could be predicted by the structure of lean body mass, and in particular by body linearity.

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

ObjectivesWe aimed to assess the dose–response association between weight gain from young to middle adulthood and odds of metabolic syndrome, across body mass index (BMI) categories at young adulthood.MethodsBased on a national population-based screening project, middle-aged (35–64 years) participants who recalled weight at age 25 years and received standardized measurements were included. Multivariable adjusted restricted cubic splines and logistic regression models were applied.ResultsIn total, 437,849 participants were included (62.1% women, 52.0 ± 7.6 years). Larger weight gains from young to middle adulthood were associated with higher odds of metabolic syndrome at middle adulthood, with odds of 2.01 (1.98–2.05), 1.93 (1.92–1.94), and 1.67 (1.64–1.7) per 5-kg weight gain across participants who were underweight, normal-weight, and overweight/obese at young adulthood, respectively. After further adjusting for current BMI, larger weight gains still correlated with higher odds of metabolic syndrome among underweight and normal-weight participants, while an inverted U-shaped association was observed in overweight/obese participants.ConclusionsWeight maintenance from young to middle adulthood could be effective to mitigate metabolic syndrome burden, especially among underweight and normal-weight people. Historical weight gain confers varied information about metabolic syndrome risk independent of attained BMI across BMI categories at young adulthood.

In 2023, Japanese women who were ** years old were the age group with the highest average body weight, amounting to 57.9 kilograms. Women aged 26 to 29 years old had an average body weight of 52.8 kilograms.