{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T09:21:07Z","timestamp":1793524867627,"version":"4.5.0"},"reference-count":50,"publisher":"American Medical Association (AMA)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA"],"published-print":{"date-parts":[[2026,3,17]]},"abstract":"\n Importance<\/jats:title>\n The prevalence of obesity in the US has risen steeply over the past decades, representing a large public health burden with substantial variation by population. There is a lack of detailed population-level estimates and projections of obesity necessary for informing health policy and reducing disparities.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To estimate US obesity prevalence from 1990 to 2022 and predict trends through 2035 by race and ethnicity, state, sex, and age (\u226520 years).<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n Analysis of measured body mass index data from the National Health and Nutrition Examination Survey and bias-corrected body mass index values calculated from self-reported height and weight data from the Behavioral Risk Factor Surveillance System and Gallup Daily Survey using spatiotemporal gaussian process regression and an ensemble of annualized rate of change and meta-regression bayesian spline models. Surveys for input data were conducted using population-based sampling by state and by race and ethnicity group with a total of 11\u202f315\u202f421 US participants. Results are reported for Hispanic, any race; non-Hispanic Black; and non-Hispanic White populations.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n Obesity prevalence (BMI \u226530).<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n In 2022, there were an estimated 107 (95% uncertainty interval [UI], 101-113) million adults living with obesity in the US (42.5% [95% UI, 40.2%-45.0%] of the adult population), an increase from 34.7 (95% UI, 31.1-38.3) million in 1990 (19.3% [95% UI, 17.3%-21.3%] of the adult population). By 2035, this is projected to increase to 126 (95% UI, 118-134) million (46.9% [95% UI, 43.9%-49.9%] of the adult population). Nationally, age-standardized prevalence by race and ethnicity group and sex in 2022 ranged from 40.1% (95% UI, 37.8%-42.5%) for non-Hispanic White males to 56.9% (95% UI, 54.1%-59.9%) for non-Hispanic Black females. There were substantial state-level differences, with prevalence highest in Midwestern and Southern states, as well as within-state disparities by race and ethnicity, which were larger for females than males. Prevalence also varied by age, with obesity prevalence highest among middle-aged adults and large increases in the youngest adult ages, especially for females.<\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n While there are large differences by race and ethnicity, sex, age, and state, the prevalence of obesity is high and forecasted to continue increasing for all groups.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jama.2025.26817","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T16:31:12Z","timestamp":1769617872000},"page":"975","source":"Crossref","is-referenced-by-count":39,"title":["US State-Level Prevalence of Adult Obesity by Race and Ethnicity From 1990 to 2022 and Forecasted to 2035"],"prefix":"10.1001","volume":"335","author":[{"given":"Nicole K.","family":"DeCleene","sequence":"first","affiliation":[{"name":"Institute for Health Metrics and Evaluation, University of Washington, Seattle"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ethan","family":"Kahn","sequence":"additional","affiliation":[{"name":"Institute for Health Metrics and Evaluation, University of Washington, Seattle"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-Wei","family":"Yuan","sequence":"additional","affiliation":[{"name":"Institute for Health Metrics and Evaluation, University of Washington, Seattle"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emmanuela","family":"Gakidou","sequence":"additional","affiliation":[{"name":"Institute for Health Metrics and Evaluation, University of Washington, Seattle"},{"name":"Department of Health Metrics Sciences, University of Washington, Seattle"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali H.","family":"Mokdad","sequence":"additional","affiliation":[{"name":"Institute for Health Metrics and Evaluation, University of Washington, Seattle"},{"name":"Department of Health Metrics Sciences, University of Washington, Seattle"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher J. 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