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 27.6 in 1999-2000 and increased to 29.1 as of 2015-2016.

This statistic depicts the average body mass index (BMI) of U.S. females aged 20 years and over from 1999 to 2016, by age. According to the data, the average female BMI for those aged 40-59 years was 29 in 1999-2000 and increased to 30.4 as of 2015-2016.

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.

This statistic depicts the average body mass index (BMI) of U.S. adults aged 20 years and over as of 2016, by gender. According to the data, the average male BMI has increased from 27.8 in 1999-2000 to 29.1 as of 2015-2016.

United States US: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data was reported at 6.900 % in 2012. This records an increase from the previous number of 6.400 % for 2009. United States US: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 6.900 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 8.700 % in 2005 and a record low of 5.100 % in 1991. United States US: 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 USA – Table US.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

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.

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.

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

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.

Maternal obesity has been associated with a higher risk of pregnancy-related complications in mothers and offspring; however, effective interventions have not yet been developed. We tested two common interventions, calorie restriction and pravastatin administration, during pregnancy in a rhesus macaque model with the hypothesis that these interventions would normalize metabolic dysregulation in pregnant mothers leading to an improvement in infant metabolic and cognitive/social development. A total of 19 obese mothers were assigned to either one of the two intervention groups (n=5 for calorie restriction; n=7 for pravastatin) or an obese control group (n=7) with no intervention, and maternal gestational samples and postnatal infant samples were compared with lean control mothers (n=6). Gestational calorie restriction normalized one-carbon metabolism dysregulation in obese mothers but altered energy metabolism in their offspring. Although administration of pravastatin during pregnancy ten..., Study population Pregnant female rhesus macaques (Macaca mulatta) from an indoor breeding colony at the California National Primate Research Center with appropriate social behavior and previous successful pregnancies were enrolled. Animal handling was approved by the UC Davis Institutional Animal Care and Use Committee (IACUC) (#19299). A qualitative real-time PCR assay (Jimenez & Tarantal, 2003) was used to identify mothers with male fetuses to include in this study. Since obesity is defined as subjects with body fat above 30% for women, according to guidelines from the American Society of Bariatric Physicians, American Medical Association, and in some publications (Okorodudu et al., 2010; Shah & Braverman, 2012), a Body Condition Score (BCS) of 3.5 (32.8 % body fat on average (Summers et al., 2012)) was used as the cutoff. Therefore, mothers with BCS of 3.5 and above were categorized as obese. Obese mothers were randomly assigned to the Obese Control (OC) group, OR group (rece...,

3D meshes of adults in the United States covering 1 to 99 percentile variation in body mass index and height. Based on manikins generated from The National Health and Nutrition Examination Survey (NHANES) using the Manikin Fetcher tool from Open Design Lab. All manikins have simplified "mitten-finger" hands with effective radiation areas corresponding to the "average hand." The manikin files are in the following format: "MB01P01"--referring to "M"-male "B01"-BMI percentile, "P01"-posture percentile. The percentile to absolute values are specified in the document "percentile to value" (PDF). See the README for additional information.

BackgroundCharacterization of abdominal and intra-abdominal fat requires imaging, and thus is not feasible in large epidemiologic studies. ObjectiveWe investigated whether biomarkers may complement anthropometry (body mass index [BMI], waist circumference [WC], and waist-hip ratio [WHR]) in predicting the size of the body fat compartments by analyzing blood biomarkers, including adipocytokines, insulin resistance markers, sex steroid hormones, lipids, liver enzymes and gastro-neuropeptides. MethodsFasting levels of 58 blood markers were analyzed in 60 healthy, Caucasian or Japanese American postmenopausal women who underwent anthropometric measurements, dual energy X-ray absorptiometry (DXA), and abdominal magnetic resonance imaging. Total, abdominal, visceral and hepatic adiposity were predicted based on anthropometry and the biomarkers using Random Forest models. ResultsTotal body fat was well predicted by anthropometry alone (R2 = 0.85), by the 5 best predictors from the biomarker model alone (leptin, leptin-adiponectin ratio [LAR], free estradiol, plasminogen activator inhibitor-1 [PAI1], alanine transaminase [ALT]; R2 = 0.69), or by combining these 5 biomarkers with anthropometry (R2 = 0.91). Abdominal adiposity (DXA trunk-to-periphery fat ratio) was better predicted by combining the two types of predictors (R2 = 0.58) than by anthropometry alone (R2 = 0.53) or the 5 best biomarkers alone (25(OH)-vitamin D3, insulin-like growth factor binding protein-1 [IGFBP1], uric acid, soluble leptin receptor [sLEPR], Coenzyme Q10; R2 = 0.35). Similarly, visceral fat was slightly better predicted by combining the predictors (R2 = 0.68) than by anthropometry alone (R2 = 0.65) or the 5 best biomarker predictors alone (leptin, C-reactive protein [CRP], LAR, lycopene, vitamin D3; R2 = 0.58). Percent liver fat was predicted better by the 5 best biomarker predictors (insulin, sex hormone binding globulin [SHBG], LAR, alpha-tocopherol, PAI1; R2 = 0.42) or by combining the predictors (R2 = 0.44) than by anthropometry alone (R2 = 0.29). ConclusionThe predictive ability of anthropometry for body fat distribution may be enhanced by measuring a small number of biomarkers. Studies to replicate these data in men and other ethnic groups are warranted.

Maternal gestational obesity is associated with elevated risks for neurodevelopmental disorder, including autism spectrum disorder. However, the mechanisms by which maternal adiposity influences fetal developmental programming remain to be elucidated. We aimed to understand the impact of maternal obesity on the metabolism of both pregnant mothers and their offspring, as well as on metabolic, brain, and behavioral development of offspring by utilizing metabolomics, protein, and behavioral assays in a non-human primate model. We found that maternal obesity was associated with elevated inflammation and significant alterations in metabolites of energy metabolism and one-carbon metabolism in maternal plasma and urine, as well as in placenta. Infants born to obese mothers were significantly larger at birth compared to those born to lean mothers. Additionally, they exhibited significantly reduced novelty preference and significant alterations in their emotional response to stress situations. These changes coincided with differences in phosphorylation of enzymes in the brain mTOR signaling pathway between infants born to obese and lean mothers and correlated with the concentration of maternal plasma betaine during pregnancy. In summary, gestational obesity significantly impacted the infant systemic and brain metabolome and adaptive behaviors. Methods 2.1 Study population Animal handling was approved by the UC Davis Institutional Animal Care and Use Committee (IACUC protocol#19299) and all experiments were performed in accordance with relevant guidelines and regulations. Pregnant female rhesus macaques (Macaca mulatta) with appropriate social behavior and a previous successful pregnancy were selected from an indoor breeding colony at the California National Primate Research Center (CNPRC). Fetal sex was determined by a qualitative real time PCR assay to detect the Y chromosome and only those with male fetuses were chosen for this study. Animals used in this study had maintained a consistent BCS for at least one year prior to the study. Obesity is defined when subjects have body fat above 30% for women (American Medical Association). A BCS of 3.5 was chosen as the cut off for inclusion in the obese group for this study as a BCS of 3.5 is correlated with 32 % body fat [14]. Mothers with a BCS of 2.5 or lower comprised the Lean group. The sample size of the biological samples was not balanced due to fetal deaths for unknown reasons, misidentification of female offspring, technical issues in collecting enough sample volume for analysis, or recruitment of additional animals into the study in the middle of pregnancy to account for the sample loss (Supplementary Table S1). The final number of mothers and their offspring included six for the Lean and seven for the Obese groups (Supplementary Table S2). 2.2 Feeding and rearing of animals Adult animals were fed seven biscuits (#5047; LabDiet, St. Louis, MO, USA) twice daily between 6-9 am and 1-3 pm. All mothers were provided nine biscuits twice daily once pregnancy was determined, and twelve biscuits twice daily while nursing infants 4 months and older. Fresh produce was provided biweekly and water was available ad libitum. More detailed description is available in Appendix A. 2.3 Sample collection and processing All animals were coded by IDs, and therefore, the animal care takers and researchers who collected samples were blinded for group assignment. The collected biological samples were randomized using random numbers generated in R in conducting assessments, and the group assignment was blinded until the data was analyzed. On the day prior to sample collection, food was removed approximately 30 min after the feeding in the afternoon, and biological samples were collected before the morning feeding. Pregnancy in rhesus macaques lasts for 166.5 days on average. Plasma and urine samples were collected from mothers once during the 1st and 2nd trimesters, and twice during the 3rd trimester on GD45, 90, 120, and 150 after anesthetizing animals with 5-30 mg/kg ketamine or 5-8 mg/kg telazol. Blood samples were collected in 5 mL lavender top (EDTA) or green (heparin) tubes and the supernatant was collected. Urine was collected from the bladder by ultrasound-guided transabdominal cystotomy using a 22-gauge needle and subsequently centrifuged to collect supernatant. Within 15 days prior to delivery, a placental sample was collected transabdominally under ultrasound guidance using an 18-gauge needle attached to a sterile syringe and centrifuged to collect the pellet. Infant plasma was collected at PD30, 90, and 110, and plasma, urine, and brain tissues were collected at PD180 when necropsy was conducted between 9:30 am-1:30 pm. Infants were anesthetized with ketamine and plasma and urine were collected, followed by euthanasia with 120 mg/kg pentobarbital. Heparin injection and clamping of the descending aorta was followed by flushing with saline at room temperature for 2 min and then by saline at 4 °C for 5 min at 250 mL/min until clear. The brain was extracted, and four regions (amygdala, hippocampus, hypothalamus, and prefrontal cortex) were dissected and immediately frozen. All collected samples were stored at -80 °C. 2.4 Metabolite extraction and insulin, cytokine, and cortisol measurement The plasma and urine samples were thawed on ice and filtered by Amicon Ultra Centrifugal Filter (3k molecular weight cutoff; Millipore, Billerica, MA, USA), and the filtrate was used for metabolomics analysis. Samples were stored at 4 °C overnight and pH was adjusted to 6.8 ± 0.1. For the placental samples, polar metabolites were extracted as described with the following modification: after lyophilization of the polar metabolite layer, the dried sample was reconstituted in 270 µL of 10 mM phosphate buffer (pH 6.85) prepared in deuterium oxide. Samples were transferred to 3 mm Bruker NMR tubes and kept at 4 °C until NMR data acquisition within 24 hours of sample preparation. A multiplex Bead-Based Kit (Millipore) was used to measure insulin as well as 17 cytokine and chemokine levels in heparin-treated plasma samples including hs-CRP, Granulocyte-macrophage colony-stimulating factor (GMCSF), interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), transforming growth factor-α (TGF-α), monocyte chemoattractant protein-1 (MCP-1), macrophage inflammatory protein-1β (MIP-1β), interleukin (IL)-1β, IL-1 receptor antagonist (IL-1ra), IL-2, IL-6, IL-8, IL-10, IL-12/23 p40, IL-13, IL-15, and IL-17A. The assay was performed following the manufacturer’s protocol. Assessment of infant plasma cortisol was conducted as previously described [17,18]. Briefly, infants were separated from their mothers at 9 am and blood samples were collected at 11 am (Sample 1). Another blood collection was done at 4 pm (Sample 2), followed by intramuscular injection of 500 μg/kg Dex. Blood was collected at 8:30 am of the following day (Sample 3) and 2.5 IU of ACTH was then injected intramuscularly. After 30 min, the last blood was collected (Sample 4). More detailed description is available in Appendix A. 2.5 1H nuclear magnetic resonance (NMR) spectroscopy data acquisition We conducted an untargeted metabolomics analysis using 1H NMR spectroscopy. All spectra were acquired at 25 °C using the noesypr1d pulse sequence on a Bruker Avance 600 MHz NMR spectrometer (Bruker, Billerica, MA, USA). Identification and quantification of metabolites were completed using Chenomx NMRSuite (version 8.1, Chenomx Inc., Edmonton, Canada). 2.6 Protein analysis Protein was extracted from the cell layer collected after metabolite extraction of brain samples, and protein quantification and western blots were done as previously described. The following antibodies from Cell Signaling Technology (Danvers, MA, USA) were used for the western blots: rabbit anti-Akt (#9272), anti-phospho-Akt (#9275; Thr308), anti-AMPKα (#2603), anti-phospho-AMPKα (#2535; Thr172), anti-p70 S6K (#9202), anti-phospho-p70 S6K (#9234; Thr389), as well as goat anti-rabbit IgG antibody conjugated to horseradish peroxidase (#7074). Either Clarity Western ECL Blotting Substrates (Bio-Rad) or Radiance Plus (Azure biosystems, Dublin, CA, USA) were used depending on the strength of signal for chemiluminescent detection. Refer to Appendix A for more detailed description. 2.7 VPC Test Recognition memory was tested with infants on post-conception day 200 ± 3 days using a VPC test conducted between 8:30-10:30 am. Briefly, infants were hand-held in front of a testing booth to look at two identical black and white high contrast abstract pictures (Fagan Test of Infant Intelligence; Infantest Corporation, Cleveland, OH, USA) placed to the right and left of center for a total of 20 sec of cumulative looking time (familiarization trial). Then, the familiar and novel pictures were placed either right or left of center according to a pre-decided random order for a 10 sec test period from the time of the first fixation (preference trial 1). The side of the pictures was switched and a second 10 sec test period was conducted (preference trial 2). Four problems were presented to each infant. The trials were video recorded for later scoring of frequency and duration of looking patterns using The Observer software (Noldus, Inc., Wageningen, The Netherlands). Novelty preference was calculated as: number of fixations at the novel stimulus/number of fixations at both the novel and familiar stimulus. 2.8 HI test The HI test was conducted as described previously. In short, we examined the frequency of scratch (as an indicator of anxiety) in response to the following four graded levels of stress (1 min each): Profile-Far (technician presented the left profile from ~1 m away from an infant in a cage), Profile-Near (presented left profile from ~0.3 m), Stare-Far (made direct eye contact with the animal from far), and Stare-Near (direct eye contact from near position). 2.9 Statistics The

In 2022, men aged 55 to 64 years had an average body mass index (BMI) of 29 kg/m2 and women in the same age group had a BMI of 28.8 kg/m2, the highest mean BMI across all the age groups. Apart from individuals aged 16 to 24 years, every demographic in England had an average BMI which is classified as overweight.An increasing problem It is shown that the mean BMI of individuals for both men and women has been generally increasing year-on-year in England. The numbers show in England, as in the rest of the United Kingdom (UK), that the prevalence of obesity is an increasing health problem. The prevalence of obesity in women in England has increased by around nine percent since 2000, while for men the share of obesity has increased by six percent. Strain on the health service Being overweight increases the chances of developing serious health problems such as diabetes, heart disease and certain types of cancers. In the period 2019/20, England experienced over 10.7 thousand hospital admissions with a primary diagnosis of obesity, whereas in 2002/03 this figure was only 1,275 admissions. Furthermore, the number of bariatric surgeries taking place in England, particularly among women, has significantly increased over the last fifteen years. In 2019/20, over 5.4 thousand bariatric surgery procedures were performed on women and approximately 1.3 thousand were carried out on men.

Objective and aimsIdentification of associations between the obese category of weight in the general US population will continue to advance our understanding of the condition and allow clinicians, providers, communities, families, and individuals make more informed decisions. This study aims to improve the prediction of the obese category of weight and investigate its relationships with factors, ultimately contributing to healthier lifestyle choices and timely management of obesity.MethodsQuestionnaires that included demographic, dietary, exercise and health information from the US National Health and Nutrition Examination Survey (NHANES 2017–2020) were utilized with BMI 30 or higher defined as obesity. A machine learning model, XGBoost predicted the obese category of weight and Shapely Additive Explanations (SHAP) visualized the various covariates and their feature importance. Model statistics including Area under the receiver operator curve (AUROC), sensitivity, specificity, positive predictive value, negative predictive value and feature properties such as gain, cover, and frequency were measured. SHAP explanations were created for transparent and interpretable analysis.ResultsThere were 6,146 adults (age > 18) that were included in the study with average age 58.39 (SD = 12.94) and 3122 (51%) females. The machine learning model had an Area under the receiver operator curve of 0.8295. The top four covariates include waist circumference (gain = 0.185), GGT (gain = 0.101), platelet count (gain = 0.059), AST (gain = 0.057), weight (gain = 0.049), HDL cholesterol (gain = 0.032), and ferritin (gain = 0.034).ConclusionIn conclusion, the utilization of machine learning models proves to be highly effective in accurately predicting the obese category of weight. By considering various factors such as demographic information, laboratory results, physical examination findings, and lifestyle factors, these models successfully identify crucial risk factors associated with the obese category of weight.

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.

Baseline characteristics by BMI category of 731,014 men and women accessing into the US Army, 2001–2011.

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.

According to the body mass index (BMI) computed by the source with the weight and height of the respondents, approximately 24 percent of male Brazilian interviewees were considered obese (BMI≥30kg/m2) in 2023, up from 22 percent of the men surveyed two years earlier. This figure was slightly lower than the share of female respondents who were found obese, which reached nearly 25 percent as of 2023.