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.

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

From 2019 to 2021, obesity among pregnant women in the United States was highest among American Indian and Alaska Native women and Black women. This statistic depicts the percentage of pregnant women in the United States from 2019 to 2021 who were obese, overweight, normal weight, or underweight, by race/ethnicity.

NA: Not applicable, for cells where the zero percent of the population fell into that category.(1) Prevalences and standard errors are calculated using the survey weights from the 5-year visit provided with the dataset. These adjust for unequal probability of selection and response. Survey and subclass estimation commands were used to account for complex sample design.(2) Overweight/obesity is defined as body mass index (BMI) z-score >2 standard deviations (SD) above age- and sex- specific WHO Childhood Growth Standard reference mean at all time points except birth, where we define overweight/obesity as weight-for-age z-score >2 SD above age- and sex- specific WHO Childhood Growth Standard reference mean.(3) To represent socioeconomic status, we used a composite index to capture multiple of the social dimensions of socioeconomic status. This composite index was provided in the ECLS-B data that incorporates information about maternal and paternal education, occupations, and household income to create a variable representing family socioeconomic status on several domains. The variable was created using principal components analysis to create a score for family socioeconomic status, which was then normalized by taking the difference between each score and the mean score and dividing by the standard deviation. If data needed for the composite socioeconomic status score were missing, they were imputed by the ECLS-B analysts [9].(4) We created a 5-category race/ethnicity variable (American Indian/Alaska Native, African American, Hispanic, Asian, white) from the mothers' report of child's race/ethnicity, which originally came 25 race/ethnic categories. To have adequate sample size in race/ethnic categories, we assigned a single race/ethnic category for children reporting more than one race, using an ordered, stepwise approach similar to previously published work using ECLS-B (3). First, any child reporting at least one of his/her race/ethnicities as American Indian/Alaska Native (AIAN) was categorized as AIAN. Next, among remaining respondents, any child reporting at least one of his/her ethnicities as African American was categorized as African American. The same procedure was followed for Hispanic, Asian, and white, in that order. This order was chosen with the goal of preserving the highest numbers of children in the American Indian/Alaska Native group and other non-white ethnic groups in order to estimate relationships within ethnic groups, which is often not feasible due to low numbers.

In 2023, Black adults had the highest obesity rates of any race or ethnicity in the United States, followed by American Indians/Alaska Natives and Hispanics. As of that time, around ** percent of all Black adults were obese. Asians/Pacific Islanders had by far the lowest obesity rates. Obesity in the United States Obesity is a present and growing problem in the United States. An astonishing ** percent of the adult population in the U.S. is now considered obese. Obesity rates can vary substantially by state, with around ** percent of the adult population in West Virginia reportedly obese, compared to ** percent of adults in Colorado. The states with the highest rates of obesity include West Virginia, Mississippi, and Arkansas. Diabetes Being overweight and obese can lead to a number of health problems, including heart disease, cancer, and diabetes. Being overweight or obese is one of the most common causes of type 2 diabetes, a condition in which the body does not use insulin properly, causing blood sugar levels to rise. It is estimated that just over ***** percent of adults in the U.S. have been diagnosed with diabetes. Diabetes is now the seventh leading cause of death in the United States, accounting for ***** percent of all deaths.

Percent of live-born infants delivered with a birth weight of less than 2,500 grams (about 5 lbs, 8 oz)

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.

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.

Objective: We evaluated interindividual differences in adjusted energy expenditure (EE) during 24 h of carefully controlled energy balance conditions in an ethnically diverse cohort and tested whether the relationship between adjusted EE and free-living weight change at 1 year differed by race/ethnicity. Methods: Healthy individuals (n=120: 20 Blacks, 37 Whites, 46 Indigenous Americans, 17 Hispanics) had 24-h EE measured in a whole-room indirect calorimeter during eucaloric conditions and adjusted for body composition by DXA. Results: On average, adjusted 24-h EE (adj24hEE) was ~335 kJ/day lower in Blacks, reflecting a 20% lower metabolic rate per kg of fat free mass compared to other ethnic groups. Blacks self-reported lower mean perceived hunger, dietary disinhibition, and perceived stress. Among fifty-six individuals whose free-living weight change was assessed at 1 year, a 1 MJ/day higher adj24hEE at baseline predicted a mean weight gain of 3.6 kg only in races/ethnicities other than Blacks, whereas adj24hEE tended to be inversely associated with 1-year weight change in Blacks (race/ethnicity interaction p=0.02). Conclusions: Relative metabolic rate differs between races/ethnicities and differentially predicts future weight change. For individuals who do not self-identify as Black, relatively higher metabolic demands during sedentary conditions may drive energy sensing-mediated overeating and ultimately weight gain.

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.

This statistic depicts the average body mass index (BMI) of U.S. females aged 20 years and over from 1999 to 2016, by ethnicity. According to the data, the average female BMI for those that identified as white was **** in 1999-2000 and increased to **** as of 2015-2016.

BMI, body mass index; N, unweighted number; IQR, interquartile range.

BackgroundA significantly higher proportion of UK Black ethnic adults live with overweight or obesity, compared to their White British counterparts. The role of obesity in excess infection rates and mortality from COVID-19 has increased the need to understand if weight management interventions are appropriate and effective for Black ethnic groups. There is a paucity of existing research on weight management services in Black populations, and whether anticipated or experienced institutional and interpersonal racism in the healthcare and more widely affects engagement in these services. Understanding the lived experience of target populations and views of service providers delivering programmes is essential for timely service improvement.MethodsA qualitative study using semi-structured interviews was conducted in June–October 2021 among 18 Black African and Black Caribbean men and women interested in losing weight and 10 weight management service providers.ResultsThe results highlighted a positive view of life in the United Kingdom (UK), whether born in the UK or born abroad, but one which was marred by racism. Weight gain was attributed by participants to unhealthy behaviours and the environment, with improving appearance and preventing ill health key motivators for weight loss. Participants relied on self-help to address their overweight, with the role of primary care in weight management contested as a source of support. Anticipated or previously experienced racism in the health care system and more widely, accounted for some of the lack of engagement with services. Participants and service providers agreed on the lack of relevance of existing services to Black populations, including limited culturally tailored resources. Community based, ethnically matched, and flexibly delivered weight management services were suggested as ideal, and could form the basis of a set of recommendations for research and practice.ConclusionCultural tailoring of existing services and new programmes, and cultural competency training are needed. These actions are required within systemic changes, such as interventions to address discrimination. Our qualitative insights form the basis for advancing further work and research to improve existing services to address the weight-related inequality faced by UK Black ethnic groups.

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.

BackgroundMetabolic risk varies according to body mass index (BMI), body fat distribution and ethnicity. In recent years, epigenetics, which reflect gene-environment interactions have attracted considerable interest as mechanisms that may mediate differences in metabolic risk. The aim of this study was to investigate DNA methylation differences in abdominal and gluteal subcutaneous adipose tissues of normal-weight and obese black and white South African women.MethodsBody composition was assessed using dual-energy x-ray absorptiometry and computerized tomography, and insulin sensitivity was measured using a frequently sampled intravenous glucose tolerance test in 54 normal-weight (BMI 18–25 kg/m2) and obese (BMI ≥ 30 kg/m2) women. Global and insulin receptor (INSR) DNA methylation was quantified in abdominal (ASAT) and gluteal (GSAT) subcutaneous adipose depots, using the Imprint methylation enzyme-linked immunosorbent assay and pyrosequencing. INSR gene expression was measured using quantitative real-time PCR.ResultsGlobal DNA methylation in GSAT varied according to BMI and ethnicity, with higher levels observed in normal-weight white compared to normal-weight black (p = 0.030) and obese white (p = 0.012) women. Pyrosequencing of 14 CpG sites within the INSR promoter also showed BMI, adipose depot and ethnic differences, although inter-individual variability prevented attainment of statistical significance. Both global and INSR methylation were correlated with body fat distribution, insulin resistance and systemic inflammation, which were dependent on ethnicity and the adipose depot. Adipose depot and ethnic differences in INSR gene expression were observed.ConclusionWe show small, but significant global and INSR promoter DNA methylation differences in GSAT and ASAT of normal-weight and obese black and white South African women. DNA methylation in ASAT was associated with centralization of body fat in white women, whereas in black women DNA methylation in GSAT was associated with insulin resistance and systemic inflammation. Our findings suggest that GSAT rather than ASAT may be a determinant of metabolic risk in black women and provide novel evidence that altered DNA methylation within adipose depots may contribute to ethnic differences in body fat distribution and cardiometabolic risk.

The eastern Bering Sea has experienced rapid and intensive development of commercial trawl fisheries. Because of good record keeping and the relatively brief history of fishing it is possible to reconstruct the spatial and temporal patterns of exploitation. Previously unfished (UF) areas can be identified and directly compared with heavily fished (HF) areas to investigate long-term consequences for the benthos. Using this approach, macrofauna populations in a shallow (48 m average) soft-bottom area were studied during 1996.

Background: Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children. The Michaelis-Menten equation, used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. We investigated whether this equation could be used to interpolate missing growth data in children in the first three years of life and compared this interpolation to several common interpolation methods and pediatric growth models. Methods: We developed a modified Michaelis-Menten equation and compared expected to actual growth, first in a local birth cohort (N=97) and then in a large, outpatient, pediatric sample (N=14,695). Results: The modified Michaelis-Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22kg [IQR:0.19; 90%<0.43]; girls: 0.20kg [IQR:0.17; 90%<0.39]) and height (median RMSE: boys: 0.93cm [IQR:0.53; 90%<1.0]; girls: 0.91cm [IQR:0.50;90%<1.0]). Growth data were modeled accurately with as few as four values from routine well-baby visits in year 1 and seven values in years 1-3; birth weight or length was essential for best fit. Interpolation with this equation had comparable (for weight) or lower (for height) mean RMSE compared to the best-performing alternative models. Conclusions: A modified Michaelis-Menten equation accurately describes growth in healthy babies aged 0–36 months, allowing interpolation of missing weight and height values in individual longitudinal measurement series. The growth pattern in healthy babies in resource-rich environments mirrors an enzymatic saturation curve. Methods Sources of data: Information on infants was ascertained from two sources: the STORK birth cohort and the STARR research registry. (1) Detailed methods for the STORK birth cohort have been described previously. In brief, a multiethnic cohort of mothers and babies was followed from the second trimester of pregnancy to the babies’ third birthday. Healthy women aged 18–42 years with a single-fetus pregnancy were enrolled. Households were visited every four months until the baby’s third birthday (nine baby visits), with the weight of the baby at each visit recorded in pounds. Medical charts were abstracted for birth weight and length. (2) STARR (starr.stanford.edu) contains electronic medical record information from all pediatric and adult patients seen at Stanford Health Care (Stanford, CA). STARR staff provided anonymized information (weight, height and age in days for each visit through age three years; sex; race/ethnicity) for all babies during the period 03/2013–01/2022 followed from birth to at least 36 months of age with at least five well-baby care visits over the first year of life.
Inclusion of data for modeling: All observed weight and height values were evaluated in kilograms (kg) and centimeters (cm), respectively. Any values assessed beyond 1,125 days (roughly 36 months) and values for height and weight deemed implausible by at least two reviewers (e.g., significant losses in height, or marked outliers for weight and height) were excluded from the analysis. Additionally, weights assessed between birth and 19 days were excluded. At least five observations across the 36-month period were required: babies with fewer than five weight or height values after the previous criteria were excluded from analyses. Model: We developed our weight model using values from STORK babies and then replicated it with values from the STARR babies. Height models were evaluated in STARR babies only because STORK data on height were scant. The Michaelis-Menten equation is described as follows: v = Vmax ([S]/(Km + [S]) , where v is the rate of product formation, Vmax is the maximum rate of the system, [S] is the substrate concentration, and Km is a constant based upon the enzyme’s affinity for the particular substrate. For this study the equation became: P = a1 (Age/(b1+ Age)) + c1, where P was the predicted value of weight (kg) or height (cm), Age was the age of the infant in days, and c1 was an additional constant over the original Michaelis-Menten equation that accounted for the infant’s non-zero weight or length at birth. Each of the parameters a1, b1 and c1 was unique to each child and was calculated using the nonlinear least squares (nls) method. In our case, weight data were fitted to a model using the statistical language R, by calling the formula nls() with the following parameters: fitted_model <-nls(weights~(c1+(a1*ages)/(b1+ages)), start = list(a1 = 5, b1 = 20, c1=2.5)), where weights and ages were vectors of each subject’s weight in kg and age in days. The default Gauss-Newton algorithm was used. The optimization objective is not convex in the parameters and can suffer from local optima and boundary conditions. In such cases good starting values are essential: the starting parameter values (a1=5, b1=20, c1=2.5) were adjusted manually using the STORK dataset to minimize model failures; these tended to occur when the parameter values, particularly a1 and b1, increased without bound during the iterative steps required to optimize the model. These same parameter values were used for the larger STARR dataset. The starting height parameter values for height modeling were higher than those for weight modeling, due to the different units involved (cm vs. kg) (a1=60, b1=530, c1=50). Because this was a non-linear model, goodness of fit was assessed primarily via root mean squared error (RMSE) for both weight and height. Imputation tests: To test for the influence of specific time points on the models, we limited our analysis to STARR babies with all recommended well-baby visits (12 over three years). Each scheduled visit except day 1 occurred in a time window around the expected well-baby visit (Visit1: Day 1, Visit2: days 20–44, Visit3: 46–90, Visit4: 95–148, Visit5: 158–225, Visit6: 250–298, Visit7: 310–399, Visit8: 410–490, Visit9: 500–600, Visit10: 640–800, Visit11: 842–982, Visit12: 1024–1125). We considered two different sets: infants with all scheduled visits in the first year of life (seven total visits) and those with all scheduled visits over the full three-year timeframe (12 total visits). We fit these two sets to the model, identifying baseline RMSE. Then, every visit, and every combination of two to five visits were dropped, so that the RMSE or model failures for a combination of visits could be compared to baseline. Prediction: We sought to predict weight or height at 36 months (Y3) from growth measures assessed only up to 12 months (Y1) or to 24 months (Y1+Y2), utilizing the “last value” approach. In brief, the last observation for each child (here, growth measures at 36 months) is used to assess overall model fit, by focusing on how accurately the model can extrapolate the measure at this time point. We identified all STARR infants with at least five time points in Y1 and at least two time points in both Y2 and Y3, with the selection of these time points based on maximizing the number of later time points within the constraints of the well-baby visit schedule for Y2 and Y3. The per-subject set of time points (Y1-Y3) was fitted using the modified Michaelis-Menten equation and the mean squared error was calculated, acting as the “baseline” error. The model was then run on the subset of Y1 only and of Y1+Y2 only. To test predictive accuracy of these subsets, the RMSE was calculated using the actual weights or heights versus the predicted weights or heights of the three time series. Comparison with other models: We examined how well the modified Michaelis-Menten equation performed interpolation in STARR babies compared to ten other commonly used interpolation methods and pediatric growth models including: (1) the ‘last observation carried forward’ model; (2) the linear model; (3) the robust linear model (RLM method, base R MASS package); (4) the Laird and Ware linear model (LWMOD method); (5) the generalized additive model (GAM method); (6) locally estimated scatterplot smoothing (LOESS method, base R stats package); (7) the smooth spline model (smooth.spline method, base R stats package); (8) the multilevel spline model (Wand method); (9) the SITAR (superimposition by translation and rotation) model and (10) fast covariance estimation (FACE method). Model fit used the holdout approach: a single datapoint (other than birth weight or birth length) was randomly removed from each subject, and the RMSE of the removed datapoint was calculated as the model fitted to the remaining data. The hbgd package was used to fit all models except the ‘last observation carried forward’ model, the linear model and the SITAR model. For the ‘last observation carried forward’ model, the holdout data point was interpolated by the last observation by converting the random holdout value to NA and then using the function na.locf() from the zoo R package. For the simple linear model, the holdout-filtered data were used to determine the slope and intercept via R’s lm() function, which were then used to calculate the holdout value. For the SITAR model, each subject was fitted by calling the sitar() function with df=2 to minimize failures, and the RMSE of the random holdout point was subsequently calculated with the predict() function. For this analysis, set.seed(1234) was used to initialize the pseudorandom generator.

In 2023, around 36 percent of Hispanic adults in Puerto Rico were considered obese. Being obese can increase one's chances of developing a number of diseases, such as type 2 diabetes and heart disease. Obesity The mean target body mass index among all people is said to be around 18.5 to 24.9 with anything over this number being considered overweight or obese. Several diseases may occur as a result of being overweight or obese. Diabetes, cardiovascular disease, and stroke are some of the common diseases that are caused by or worsened by weight gain and obesity. The United States has higher rates of obesity among both men and women compared to other OECD countries. Obesity-related Hispanic health Diabetes is a prevalent health issue among the Hispanic community. Diabetes is among the top 10 leading causes of death among Hispanics in the United States. Hispanics in the U.S. are more likely to die from diabetes than white U.S. residents.

We study the determinants of season of birth for married women aged 20-45 in the USA, using birth certificate and Census data. We also elicit the willingness to pay for season of birth through discrete-choice experiments implemented on the Amazon Mechanical Turk platform. We document that the probability of a spring first birth is significantly related to mother's age, education, race, ethnicity, smoking status during pregnancy, receiving WIC (Women, Infants & Children) food benefits during pregnancy, prepregnancy obesity, and the mother working in education, training, and library occupations; whereas among unmarried women without a father acknowledged on their child's birth certificate, all our findings are muted. A summer first birth does not depend on socioeconomic characteristics, although it is the most common birth season in the USA. Among married women aged 20-45, we estimate the average marginal willingness to pay (WTP) for a spring birth to be 877 USD. This implies a willingness to trade-off 560 grams of birth weight in the normal range to achieve a spring birth. Finally, we estimate that an increase of 1,000 USD in the predicted marginal WTP for a spring birth is associated with a 15 pp (percentage points) increase in the probability of obtaining an actual spring birth.

These data represent the predicted (modeled) prevalence of being Overweight or Obese among adults (Age 18+) for each census tract in Colorado. Overweight is defined as a BMI of 25 or greater. Obese is defined as a BMI of 30 or greater. BMI is calculated from self-reported height and weight.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."