Trends and projections of overweight and obesity among midlife women in the United States,1990–2050: A sub‑study of the Global Burden of Disease 2021

Abstract

Background:

Overweight and obesity are rising globally. During perimenopause years, declining estrogen and changes in body composition increase visceral fat and insulin resistance, increasing cardiometabolic risk.

Objective:

To estimate trends in overweight and obesity among U.S. women aged 40–64 years, examine variations by age and state, and project prevalence to 2050.

Design:

Secondary analysis of publicly available body mass index (BMI) data accessed through the Global Burden of Disease (GBD) Health Data Exchange, using pooled cross-sectional estimates and hierarchical logistic growth modeling.This sub‑study is not part of the official GBD analysis.

Methods:

Harmonized data from the GBD Health Data Exchange integrated National Health Interview Survey, National Health and Nutrition Examination Survey (NHANES), and Behavioral Risk Factor Surveillance System microdata (1990–2021) for nonpregnant women aged 40–64 years, with calibration of self-reported BMI to measured NHANES data. BMI categories followed WHO cutoffs. Weighted estimates were age-standardized, and hierarchical logistic models projected state-specific trends to 2050. The pooled sample included 150,842 women, representing about 90 million weighted person-years. All data were de-identified and publicly available; ethics approval and consent were not required.

Results:

Combined overweight/obesity prevalence increased from 49.2% (95% uncertainty interval (UI) 45.8–52.2; 17.7 million (95% UI 16.5–18.8 million)) in 1990 to 74.2% (95% UI 68.8–79.1; 33.4 million (95% UI 31.0–35.6 million)) in 2021, and projected to reach 83.4% (95% UI 75.5–88.3; 41.7 million (95% UI 37.8–44.2 million)). In 2021, rates ranged from 68.3% in Colorado to 82.8% in Mississippi.

Conclusion:

Overweight and obesity among midlife women have increased sharply with a continuing shift from overweight to obesity. Menopause-specific preventive strategies and policies promoting physical activity and healthy diet are critical to slow future increases.

Plain language summary

Obesity and overweight have increased sharply in U.S. women ages 40 to 64 over the past 30 years. This study shows where rates are highest, how they have changed in each state, and what these trends may look like by 2050.

Overweight and obesity have increased sharply among U.S. women aged 40–64 over the past three decades and are expected to keep rising. In 1990, about one in two women in this age group were overweight or obese; by 2021, more than seven in ten were affected. National projections suggest that by 2050, over eight in ten midlife women will be living with excess weight. Rates are not the same everywhere. Women in Southern states such as Mississippi and Alabama have some of the highest obesity levels, while those in states like Colorado and Hawaii have lower rates – likely due to differences in access to healthy foods, physical activity, and local health policies. This trend matters because carrying excess weight increases the risk of serious conditions such as diabetes, heart disease, stroke, and several cancers. Hormonal and metabolic changes that occur during midlife and the menopause transition also make weight management more challenging. The study emphasizes that prevention and intervention should begin earlier in life and continue through midlife, with programs that address both biological and social factors influencing health. New medications, such as GLP-1 receptor agonists (including semaglutide and tirzepatide), show promise for effective weight reduction. However, these treatments are expensive, not equally accessible to all women, and their long-term outcomes are still being studied. Together, these findings highlight an urgent need for better access to prevention programs, affordable and equitable treatments, and supportive policies that can help women maintain a healthy weight and improve their quality of life as they age.

Keywords

midlife women obesity menopause prevalence forecast health disparities

Introduction

Obesity in midlife women aged 40 to 64 has increased sharply in recent decades.^1,2 This life stage is marked by menopause, reduced basal metabolic rate, and declining lean body mass, which contribute to central adiposity and metabolic changes that increase the risk for chronic disease.^3–5 During perimenopause, falling estrogen levels shift fat distribution from the hips and thighs toward abdominal stores, increase insulin resistance, and reduce energy expenditure.^6,7 These physiologic changes are accompanied by mood disturbance, sleep disruption, and reduced physical activity, all of which make sustained weight management difficult.⁸ Behavioral challenges, including less physical activity and dietary changes, further complicate weight management in midlife.⁹ Consequently, obesity during this period accelerates the development of cardiometabolic diseases, musculoskeletal disorders, and certain cancers, including morbidity and long-term health care costs.^4,10

Understanding obesity among midlife women is critical because it represents the intersection of biology, behavior, and environment. Weight gain in this group is often persistent, and its prevention is more effective than later treatment. Evidence shows that even modest midlife weight reduction can lower risks for cardiovascular disease, diabetes, and cancer.^3–5,10 Despite its importance, the epidemiology of obesity among midlife women has not been well characterized in population-based data, especially at the subnational level.

Obesity rates in the United States have risen across successive generations. At age 50, prevalence increased from 33.2% in women born in 1945 to 48.2% in the 1970 cohort, representing an average increase of approximately 3% across successive 5-year intervals.⁶ These patterns reflect environmental and societal drivers, including greater access to energy-dense foods, more sedentary lifestyles, and persistent disparities in prevention and healthcare access.^2,11 Midlife women are particularly affected because hormonal change amplifies the impact of obesogenic environments, while work and caregiving responsibilities constrain opportunities for physical activity and self-care.

Obesity in midlife women is also further influenced by geographic and socioeconomic factors. States in the South report the highest prevalence, shaped by barriers to healthcare, economic disadvantage, and limited opportunities for physical activity.^2,12–16 Broader social determinants – including income, education, access to healthy food, and reliable transportation – reinforce these disparities, particularly in low-resource settings.^15,17–20 Structural barriers such as food deserts, limited public transit, and neighborhood safety further restrict options for healthy living. These inequalities are reflected in regional differences across the United States, where environmental and policy factors shape opportunities for physical activity and access to healthy foods.

Forecasting future obesity trends among midlife women has strong public-health relevance. Obesity is a leading cause of preventable illness and premature death. By 2022, 43% of adults globally were overweight and 16% were living with obesity, and projections suggest that over half of the world’s population will be affected by 2035.²¹ The United States already exceeds these averages, with obesity prevalence surpassing 40% among adults and continuing to climb.²² Modeling future patterns can help anticipate the healthcare burden, guide resource allocation, and identify regions and population where interventions will yield the greatest benefit.^23–25 Such forecasts are particularly important for women in midlife, as weight gain during this stage contributes substantially to postmenopausal metabolic risk and later-life disability.

Prior research has described national obesity trends, but few analyses have focused specifically on midlife women or offered detailed state-level projections. Moreover, physiologic and hormonal transitions during perimenopause, including declining estrogen levels and redistribution of body fat, make this group uniquely susceptible to obesity and its long-term consequences. This study addresses that gap by estimating trends in overweight and obesity among U.S. women aged 40 to 64 from 1990 to 2021, and forecasting prevalence to 2050 at both national and state levels.

Materials and methods

Study design

We conducted a systematic analysis of overweight and obesity prevalence among midlife women (aged 40–64 years) in the United States, estimating trends from 1990 to 2021 and projecting prevalence through 2050. This analysis used publicly available, de-identified data accessed through Global Burden Disease (GBD) Health Data Exchange.²⁶ The research team independently applied GBD-consistent analytic methods, but was not part of the official GBD collaboration. The study design represents a secondary, cross-sectional time-series analysis using nationally representative datasets and state-level modeled estimates.

Data sources

We obtained harmonized data from three major U.S. surveillance systems, including the GBD 2021 Adult body mass index (BMI) dataset: the National Health Interview Survey (NHIS), the National Health and Nutrition Examination Survey (NHANES), and the Behavioral Risk Factor Surveillance System (BRFSS). NHANES provided measured anthropometric data and served as the reference standard for BMI calibration. NHIS and BRFSS contributed self-reported height and weight data, which were adjusted to measure values using meta-regression Bayesian regularized trimmed (MR-BRT) models. Each dataset includes sample weights and design variables, permitting nationally and state-representative estimates.^11,26 Summary of the national datasets used in this analysis is presented in Table 1.

Table 1.

Data sources included in the analysis.

Data source	Survey (years)	Population (women)	Age (range)	BMI measurement	Key strengths
NHIS	1990–2021 (annual)	~60,000 women per year	⩾18 years (subset 40–64 years used)	Self‑reported height and weight	Large sample, consistent methods for trend analysis
NHANES	1999–2021 (biennial)	~5000 women per cycle	⩾2 years (subset 40–64 years used)	Measured height and weight; laboratory data	High measurement accuracy, allows calibration of self‑report
BRFSS	1990–2021 (annual)	>200,000 women per year	⩾18 years (subset 40–64 years used)	Self‑reported height and weight	State‑level estimates and large sample for sub‑state analyses

NHIS: National Health Interview Survey; NHANES: National Health and Nutrition Examination Survey; BRFSS: Behavioral Risk Factor Surveillance System; BMI: body mass index.

Participants

We included nonpregnant women aged 40–64 years at the time of the survey participation. Participants with missing or implausible BMI values (<10 or >70 kg/m²) were excluded. For NHANES, measured BMI was used; for NHIS and BRFSS, calibrated BMI values were used.

Sample size and power

The pooled dataset comprised approximately 150,800 women aged 40–64 years across 32 years of data, representing more than 90 million weighted person-years. No a priori power calculation was performed because all available nationally representative data were included; the large sample ensured adequate precision for national and state estimates.

Definition of overweight and obesity

Overweight and obesity were defined using BMI, calculated as weight in kilograms divided by height in meters squared (kg/m²). For adults 18 years or older, overweight was defined as a BMI from 25.0 to less than 30.0 kg/m², and obesity as BMI 30.0 kg/m² or higher. These cutoffs followed the World Health Organization (WHO) classification to maintain comparability with previous U.S. and GBD reports.

Statistical analysis

We estimated age-standardized prevalence using the spatiotemporal Gaussian process regression (ST-GPR) framework implemented by the GBD consortium and replicated for this sub-study. This framework integrated nationally representative survey data, including NHANES, NHIS, and BRFSS, and adjusted for temporal and spatial dependencies. Self-reported BMI from BRFSS and NHIS was bias-corrected against measured NHANES data using MR-BRT cross-walk models with fixed effects for age group and decade.

The ST-GPR model generated smoothed annual estimates for national and state prevalence. Covariates included educational attainment, urbanization, healthcare access, and demographic composition (age and race/ethnicity). Missing data were imputed using predictive modeling within the ST-GPR framework. Uncertainty intervals (UIs) were calculated from the 2.5th and 97.5th percentiles of 1000 posterior simulations, which incorporated sampling, bias-adjustments, and covariate uncertainty. Model performance was assessed by leave-one-out cross-validation.

For the projection period (2022–2050), overweight and obesity prevalence were forecast using a generalized ensemble modeling approach that integrated three predictive submodels: (1) annualized rate-of-change extrapolation, (2) spline-based regression for non-linear trajectories, and (3) Bayesian hierarchical modeling incorporating state-level covariates. Each submodel was weighted by out-of-sample predictive accuracy. Projections assumed continuation of current trajectories and incorporated forecasts of population growth, education, and healthcare coverage. Uncertainty for projected estimates was quantified by 1000 posterior draws for each state-year.

Analyses were performed using R (version 4.4.0) and Python (version 3.13.9). All statistical code was verified by independent review for consistency with published GBD procedures.

Ethical considerations

All data were publicly available and de-identified; therefore, institutional review board approval and informed consent were not required. The analysis complied with Standardised Reporting of Burden of Disease studies (STROBOD)²⁷ and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)²⁸ guidelines, with completed checklists provided as Supplemental Material.

Results

National- and state-level prevalence of overweight and obesity

From 1990 to 2021, the age-standardized prevalence of overweight and obesity among U.S. women aged 40–64 increased from 49.2% (95% UI 45.8–52.2), representing an estimated 17.7 million (95% UI 16.5–18.8 million), to 74.2% (95% UI 68.8–79.1), or 33.4 million (95% UI 31.0–35.6 million). By 2050, the projected prevalence is 83.4% (95% UI 75.5–88.3), corresponding to 41.7 million midlife women (95% UI 37.8–44.2 million; Figure 1).

Figure 1.

Estimated and projected prevalence of overweight and obesity in Midlife women from 1990 to 2050.

Prevalence varied markedly across states. In 2021, Colorado had the lowest rate (68.3%, 95% UI 60.3–76.3) and Mississippi, the highest (82.8%, 95% UI 77.3–88.3). By 2050, Alabama and Mississippi are projected to exceed 89%, whereas Colorado and Vermont are expected to remain below 80% (Figure 1). Overall, Southern states showed the steepest absolute increases. Full state-level estimates are presented in Table 2.

Table 2.

Estimated and projected age-standardized prevalence of overweight and obesity in Midlife women (40–64 years) for 1990, 2021, and 2050 in the United States at the national level and across 50 states and Washington, DC.

U.S. States	1990	2021	2050	Percentage change, 1990–2021	Percentage change, 2022–2050
USA	49.2 (45.8–52.2)	74.2 (68.8–79.1)	83.4 (75.5–88.3)	82.5% (56.8–108.1)	36.6% (17.3–53.7)
Alabama	56.0 (50.3–61.7)	80.4 (74.3–86.5)	89.8 (84.7–94.9)	104.0% (67.9–137.2)	39.5% (20.9–57.5)
Alaska	60.8 (52.2–69.4)	73.6 (65.6–81.6)	80.2 (71.4–89.0)	59.2% (8.8–109.1)	27.8% (8.5–53.0)
Arizona	52.0 (45.3–58.7)	74.0 (66.0–82.0)	84.8 (76.0–93.6)	95.8% (49.5–139.8)	45.1% (17.0–74.1)
Arkansas	58.3 (50.1–66.5)	79.5 (73.0–86.0)	88.6 (82.5–94.7)	90.4% (45.9–133.5)	38.4% (19.4–60.4)
California	53.9 (49.0–58.8)	73.5 (66.8–80.2)	81.3 (72.9–89.7)	86.5% (51.5–120.4)	32.8% (8.7–58.9)
Colorado	49.5 (42.1–56.9)	68.3 (60.3–76.3)	78.6 (67.8–89.4)	85.5% (37.9–130.5)	43.4% (10.6–69.5)
Connecticut	50.4 (43.0–57.8)	71.2 (62.6–79.8)	81.6 (71.2–92.0)	96.0% (44.5–144.0)	43.5% (17.4–75.0)
Delaware	59.4 (51.6–67.2)	77.3 (70.2–84.4)	85.8 (78.5–93.1)	77.4% (32.7–121.6)	36.0% (15.2–58.9)
District of Columbia	66.5 (58.7–74.3)	71.8 (62.4–81.2)	77.7 (67.9–87.5)	23.9% (−29.5–74.2)	24.6% (4.2–41.1)
Florida	56.1 (47.5–64.7)	72.4 (62.6–82.2)	80.6 (70.6–90.6)	69.9% (12.5–125.4)	34.6% (13.2–63.2)
Georgia	55.1 (49.4–60.8)	77.6 (71.3–83.9)	86.8 (80.1–93.5)	99.2% (63.4–133.9)	38.1% (14.9–59.1)
Hawaii	45.1 (39.0–51.2)	65.4 (57.2–73.6)	75.4 (64.8–86.0)	90.1% (45.5–131.4)	40.7% (15.0v72.6)
Idaho	56.5 (50.4–62.6)	73.4 (66.0–80.8)	82.2 (74.0–90.4)	73.0% (30.5–112.6)	37.1% (16.3–62.8)
Illinois	57.6 (52.3–62.9)	75.8 (69.1–82.5)	84.3 (76.1–92.5)	79.2% (42.4–114.7)	36.1% (12.9–61.0)
Indiana	60.8 (55.7–65.9)	77.9 (71.8–84.0)	86.9 (80.6–93.2)	69.8% (35.5–102.8)	38.0% (18.3–57.6)
Iowa	59.4 (52.9–65.9)	77.6 (70.9–84.3)	87.6 (80.9–94.3)	79.3% (39.9–118.8)	42.0% (18.5–63.1)
Kansas	55.9 (47.5–64.3)	77.4 (70.9–83.9)	87.9 (80.6–95.2)	92.6% (48.7–136.4)	43.8% (17.0–66.7)
Kentucky	58.2 (52.7–63.7)	79.0 (72.9–85.1)	88.0 (82.5–93.5)	88.4% (51.6–122.6)	37.8% (21.4–56.7)
Louisiana	62.1 (55.2–69.0)	80.0 (73.7–86.3)	88.2 (81.9–94.5)	79.2% (39.4–117.9)	34.5% (16.3–56.0)
Maine	55.7 (47.5–63.9)	73.8 (65.0–82.6)	82.7 (74.5–90.9)	79.1% (28.6–129.1)	37.0% (17.2–61.7)
Maryland	56.2 (50.1–62.3)	77.0 (70.5–83.5)	84.9 (76.9–92.9)	92.0% (54.2–128.9)	32.5% (12.7–60.9)
Massachusetts	51.9 (46.0–57.8)	69.8 (62.5–77.1)	79.1 (69.7–88.5)	76.4% (35.2–116.5)	39.7% (15.8–69.2)
Michigan	61.1 (55.4–66.8)	78.0 (71.9–84.1)	86.7 (80.2–93.2)	72.2% (36.9–106.8)	36.8% (16.2–57.0)
Minnesota	55.3 (50.2–60.4)	73.7 (66.8–80.6)	82.7 (74.3–91.1)	81.0% (44.3–115.3)	37.5% (12.5–62.5)
Mississippi	61.1 (54.2–68.0)	82.8 (77.3–88.3)	90.3 (85.0–95.6)	93.6% (56.8–128.0)	31.3% (13.9–49.5)
Missouri	58.2 (51.7–64.7)	77.1 (69.8–84.4)	85.9 (78.8–93.0)	84.2% (41.9–123.5)	36.4% (16.8–58.2)
Montana	54.1 (48.0–60.2)	72.7 (65.4–80.0)	82.3 (73.5–91.1)	78.3% (36.9–118.6)	40.2% (15.1–65.9)
Nebraska	58.9 (52.8–65.0)	78.1 (72.0–84.2)	88.0 (81.1–94.9)	84.8% (48.7–120.8)	41.4% (15.9–61.6)
Nevada	54.4 (45.8–63.0)	74.1 (66.3–81.9)	84.2 (75.6–92.8)	85.1% (34.9–133.0)	42.1% (18.7–68.0)
New Hampshire	51.4 (44.7–58.1)	70.9 (63.1–78.7)	81.3 (71.3–91.3)	84.0% (39.8–126.5)	43.3% (15.4–73.3)
New Jersey	53.7 (46.3–61.1)	71.6 (64.3–78.9)	80.6 (71.8–89.4)	77.2% (33.2–120.5)	38.5% (14.6–65.1)
New Mexico	51.9 (45.2–58.6)	78.1 (71.2–85.0)	87.8 (80.5–95.1)	116.3% (74.3–155.3)	40.3% (16.2–63.8)
New York	56.8 (50.3–63.3)	72.0 (64.2–79.8)	79.3 (70.3–88.3)	67.3% (23.5–107.8)	30.7% (11.6–59.6)
North Carolina	58.7 (53.6–63.8)	78.2 (71.9–84.5)	87.6 (81.3–93.9)	85.2% (50.1–119.1)	40.1% (17.8–60.6)
North Dakota	58.8 (52.5–65.1)	76.9 (69.5–84.3)	85.7 (77.7–93.7)	81.9% (39.4–122.8)	37.1% (11.5–60.4)
Ohio	59.5 (54.0–65.0)	78.5 (72.4–84.6)	88.3 (82.4–94.2)	83.7% (47.4–117.6)	41.3% (20.2–61.0)
Oklahoma	56.7 (50.2–63.2)	79.6 (73.3–85.9)	89.2 (82.7–95.7)	98.3% (59.5–136.1)	40.2% (17.3–62.8)
Oregon	56.1 (50.2–62.0)	73.2 (66.3–80.1)	82.0 (73.4–90.6)	74.8% (35.0–112.1)	36.9% (12.4–62.5)
Pennsylvania	61.0 (55.1–66.9)	76.3 (70.0–82.6)	84.5 (77.4–91.6)	66.4% (30.0–101.2)	34.2% (15.3–54.7)
Rhode Island	52.6 (46.5–58.7)	73.1 (65.5–80.7)	83.5 (74.7–92.3)	91.5% (49.4–131.4)	43.6% (17.8–73.2)
South Carolina	58.9 (52.6–65.2)	79.3 (72.2–86.4)	88.0 (81.3–94.7)	88.7% (46.6–127.4)	36.6% (16.6–58.7)
South Dakota	57.4 (49.8–65.0)	76.8 (68.8–84.8)	85.9 (77.7–94.1)	83.6% (34.4–129.4)	38.4% (13.7–63.4)
Tennessee	56.2 (50.3–62.1)	78.9 (72.0–85.8)	88.0 (81.3–94.7)	98.1% (58.7–134.9)	38.1% (16.7–60.6)
Texas	56.6 (50.9–62.3)	79.4 (73.7–85.1)	87.3 (80.6–94.0)	97.5% (62.4–131.1)	33.1% (11.1–56.1)
Utah	54.5 (48.6–60.4)	73.1 (65.8–80.4)	82.6 (74.0–91.2)	80.1% (38.9–118.3)	39.7% (14.0–64.7)
Vermont	54.2 (46.8–61.6)	68.9 (61.1–76.7)	77.2 (67.8–86.6)	67.6% (21.4–113.9)	34.6% (13.0–60.6)
Virginia	54.1 (47.4–60.8)	76.7 (70.4–83.0)	86.5 (78.7–94.3)	99.6% (59.5–138.6)	41.4% (15.2–66.6)
Washington	53.3 (47.6–59.0)	72.6 (65.7–79.5)	80.2 (71.2–89.2)	82.5% (43.8–119.3)	31.9% (9.6–63.2)
West Virginia	61.6 (56.3–66.9)	81.3 (75.4–87.2)	89.8 (85.1–94.5)	87.0% (52.2–119.0)	35.5% (19.7–53.8)
Wisconsin	59.7 (53.8–65.6)	75.3 (68.4–82.2)	84.6 (77.3–91.9)	67.2% (27.6–104.5)	39.6% (17.3–60.5)
Wyoming	56.5 (46.7–66.3)	73.6 (66.0–81.2)	81.9 (72.7–91.1)	76.8% (22.8–129.9)	34.2% (12.2–61.5)

Age-specific prevalence of overweight and obesity

Prevalence of overweight and obesity rose across all age groups. In 1990, overall prevalence affected 49.2% (95% UI 46.0–52.4) of women aged 40–64 years, ranging from 53.6% (95% UI 50.2–57.0) at ages 40–44 years to 61.1% (95% UI 57.9–64.3) at ages 60–64 years. By 2021, overall prevalence reached 75.5% (95% UI 70.6–80.4), with 74.7% (95% UI 69.9–79.5) in the 40–44 age group and 76.1% (95% UI 71.7–80.5) in the 60–64 age group. The 75.5 % estimate represents the pooled age-specific mean across midlife age groups, whereas the age-standardized national prevalence is slightly lower at 74.2 % (95 % UI 68.8–79.1). Age-specific trends are shown in Figure 2.

Figure 2.

Estimated and projected age-specific prevalence of (a) overweight, (b) obesity and (c) overweight and obesity combined.

Projections to 2050

Forecasts indicate a continued upward trajectory nationwide. By 2050, the combined prevalence of overweight and obesity in projected at 83.4% (95% UI 75.5–88.3). Prevalence increased with age through 2021 but is projected to plateau above 80% by 2050. Obesity alone is expected to rise from 35.9% in 2021 to approximately 45% in 2050, while overweight declines from 38.3% to about 38%–35% nationally. Projected increases are driven primarily by rising obesity, whereas overweight stabilizes or declines in most states.

Trends in overweight and obesity projections by state

Overweight prevalence is projected to decline across most states by 2050. For example, in Mississippi, overweight prevalence is expected to fall from 23.7% (95% UI 19.6–27.8) in 2022 to 18.5% (95% UI 14.7–22.3) in 2050. Alabama is projected to decline from 24.6% (95% UI 20.9–28.3) to 18.6% (95% UI 15.1–22.1). States with lower baseline prevalence, such as Hawaii and Vermont, are also projected to see slight reductions, with estimates of 28.2% (95% UI 23.4–33.0) in Hawaii and 22.7% (95% UI 18.4–27.0) in Vermont by 2050.

Obesity prevalence is expected to rise in all states. By 2050, Mississippi is projected to reach 71.8% (95% UI 66.8–76.8), Alabama 71.1% (95% UI 66.0–76.2), and West Virginia 71.7% (95% UI 67.1–76.3). States such as Hawaii and Colorado are expected to have the lowest obesity prevalence, at 47.1% (95% UI 40.6–53.6) and 52.4% (95% UI 45.5–59.3), respectively.

Combined overweight and obesity prevalence is projected to exceed 80% in most states by 2050. Mississippi is expected to have the highest combined prevalence at 90.3% (95% UI 87.6–93.0), followed by Alabama and West Virginia at 89.8% (95% UI 86.8–92.8) and 89.8% (95% UI 87.4–92.2), respectively. Northeastern and Western states, including Vermont and Hawaii, are projected to maintain lower combined prevalence at 77.2% (95% UI 72.4–82.0) and 75.4% (95% UI 69.4–81.4; Figures 3(a) –(c)).

Figure 3.

Estimated age-standardized prevalence of overweight and obesity in 50 US states and Washington, DC, in 2021 and by 2050. (a) Overweight. (b) Obesity. (c) Overweight and obesity.

Uncertainty and model performance

All estimates include 95 % UIs derived from 1000 posterior simulations. Model validation through leave-one-out cross-validation confirmed strong internal consistency (mean R² = 0.94). Observed and predicted trends were closely aligned for both national and state levels, supporting the reliability of long-term projections.

Discussion

This study provides the most comprehensive analysis to date of overweight and obesity prevalence and trends among midlife women in the United States, using national and state-level data from 1990 to 2021, with forecasts through 2050. Findings indicate that the combined prevalence of overweight and obesity among U.S. women aged 40–64 years has increased sharply, nearly doubling over three decades. This pattern reflects the transition of individuals with overweight into the obesity range rather than overall weight improvement. These increases are not uniform; significant regional disparities persist and are projected to widen.

Obesity prevalence among midlife women has risen steadily across generations. In this analysis, the prevalence of obesity at age 50 increased by roughly 3% across successive 5-year birth intervals, consistent with long-term national cohort trends.¹¹ This trend underscores the impact of shifting environmental and societal factors, including the widespread availability of calorie-dense foods, reductions in physical activity, and longstanding disparities in healthcare access and prevention.¹⁴ The present analysis extends prior work by examining both national and subnational patterns using the GBD Health Data Exchange framework. The modeling approach aligns with GBD 2021 U.S. obesity estimates but represents an independent secondary analysis of the publicly available dataset. Southern states, such as Mississippi and Alabama, continue to report the highest obesity rates and are projected to exceed 85% by 2050, reflecting the persistent clustering of metabolic risk in high-deprivation regions. These findings are consistent with earlier reports documenting the disproportionate burden of obesity in resource-limited regions.²⁹ In contrast, states like Colorado and Hawaii have maintained lower prevalence, reflecting the influence of healthier built environments, physical activity culture, and sustained policy investments.^30–33 These geographic differences highlight the need for regionally adapted prevention strategies that address state-specific barriers and policy contexts.

Parallel increases in childhood and adolescent obesity mirror this pattern. Global GBD analyses show a four-fold increase since 1990,³⁴ reinforcing the life-course continuum of obesity risk. Early-life obesity remains one of the strongest predictors of adult and midlife obesity, driven by complex interactions among biology, environment, and behavior.^35,36 Preventing excess weight gain from early life onward is crucial to mitigating the cumulative burden observed in midlife women.

The underlying drivers of these trends are multifactorial. Physiologic changes during midlife–including estrogen declines, loss of lean body mass, and reduced basal metabolism–promote central adiposity and insulin resistance, increasing cardiometabolic vulnerability.^3,4,12 These changes are compounded by social determinants such as income inequality, educational, and access to healthcare and nutritious foods.^13,15–18 Structural barriers including food deserts, transportation limitations, and socioeconomic deprivation – remain dominant in high-burden states and continue to undermine prevention.^18,23 Midlife represents a pivotal window for intervention, when metabolic decline accelerates, but prevention remains feasible. Prevention efforts during this stage can yield substantial long-term benefits for cardiometabolic health and disability reduction.

These results emphasize the need for integrated prevention approaches that address both biologic and structural determinants. Midlife offers a critical window for intervention, when women remain engaged in the workforce and health behaviors are still modifiable. Primary care providers and menopause specialists should incorporate counseling on diet, physical activity, and sleep as standard components of care. State and federal agencies can support prevention through built-environment improvements, workplace wellness initiatives, and insurance coverage for evidence-based obesity treatments. Tailoring programs to the needs of midlife women – particularly in high-burden regions could yield substantial reductions in future morbidity and healthcare costs.

The Women’s Preventive Services Initiative guidelines provide a structured framework for prevention among midlife women.³ Behavioral counseling to promote healthy diets, physical activity, and stress management is recommended for all women at elevated risk. Culturally and contextually tailored interventions are essential, particularly in states with high obesity prevalence and limited resources. Technology-enabled strategies including telehealth, digital coaching, and mobile applications, can extend reach and continuity of care, especially where preventive services are underdeveloped. Partnerships between community organizations and workplaces can further promote sustainable improvement in diet and physical activity.

Psychological and social stressors are also critical. Chronic stress, caregiving roles, and depression are prevalent during midlife and are strongly linked to obesity risk.³⁷ Integration of behavioral counseling into routine primary care and menopause care can improve uptake and continuity. Persistent racial and ethnic disparities, especially among Black and Hispanic women, reflect enduring structural inequalities and sociocultural influences continue to experience higher rates of obesity compared with White women, which reflects both structural inequities and cultural influences on health behavior.²⁹ Addressing these disparities requires community-informed and equity-focused interventions that strengthen access, trust, and engagement in preventive care.

Clinical management of obesity in midlife women is changing rapidly with the introduction of glucogon-like peptide-1 receptor agonists such as semaglutide, and tirzepatide. Randomized trials and real-world evidence indicate that these agents produce substantially greater weight loss than traditional behavioral interventions.³⁸ For example, semaglutide (2.4 mg weekly) has been associated with up to 13.9% weight loss, and tirzepatide (15 mg weekly) with up to 17.8% in adults without diabetes.³⁹ Integration of pharmacologic and behavioral approaches will be necessary to ensure comprehensive, equitable, and sustainable obesity management. Ongoing surveillance and implementation research should evaluate long-term effectiveness, safety, and affordability in diverse populations.^40,41

Strengths and limitations

This study’s strengths include its use of validated GBD-based modeling approaches integrating nationally representative datasets (NHIS, NHANES, BRFSS) to generate granular, state-level estimates and long-term projections. The focus on midlife women addresses an important evidence gap, providing disaggregated, actionable data for policymakers and clinicians.

However, reliance on self-reported BMI in NHIS and BRFSS may introduce measurement bias, even after calibration using NHANES. Projections assume continuation of historical trends and may not account for policy interventions, emerging pharmacotherapeutics, or post-pandemic behavior changes that could modify future trajectories. Finally, while the quantitative modeling offers robust trend estimations, qualitative insights into the women’s lived experiences with weight management remain limited and should be explored in future research.

Conclusions

Overweight and obesity in U.S. women aged 40–64 have risen sharply over the past three decades, with persistent and widening regional disparities projected through 2050. These findings underscore the urgent need for life-course-based prevention that begins early and continues through midlife, supported by regionally adopted public health strategies. Emerging pharmacologic therapies expand the toolkit for obesity management but also require policies to ensure equitable access and affordability. Future research should incorporate direct measures of central adiposity and assess population-level impact of these interventions. Sustained, data-driven prevention and treatment efforts are essential to reversing obesity trends and improving long-term health outcomes for U.S. women.

Supplemental Material

sj-pdf-1-whe-10.1177_17455057261430197 – Supplemental material for Trends and projections of overweight and obesity among midlife women in the United States, 1990–2050: A sub-study of the Global Burden of Disease 2021

Supplemental material, sj-pdf-1-whe-10.1177_17455057261430197 for Trends and projections of overweight and obesity among midlife women in the United States, 1990–2050: A sub-study of the Global Burden of Disease 2021 by Akshaya Srikanth Bhagavathula, Wafa Ali Aldhaleei and Tadesse Melaku Abegaz in Women's Health

Supplemental Material

sj-pdf-2-whe-10.1177_17455057261430197 – Supplemental material for Trends and projections of overweight and obesity among midlife women in the United States, 1990–2050: A sub-study of the Global Burden of Disease 2021

Supplemental material, sj-pdf-2-whe-10.1177_17455057261430197 for Trends and projections of overweight and obesity among midlife women in the United States, 1990–2050: A sub-study of the Global Burden of Disease 2021 by Akshaya Srikanth Bhagavathula, Wafa Ali Aldhaleei and Tadesse Melaku Abegaz in Women's Health

Footnotes

Acknowledgements

We thank the Global Burden of Disease, Injuries and Risk Factors Study 2021 by the Institute of Health Metrics and Evaluation for collecting and providing high-quality harmonized data. ASB is a GBD Lead Collaborator and WAA is a senior GBD Collaborator.

ORCID iD

Akshaya Srikanth Bhagavathula

Ethical considerations

The study used publicly available, de-identified data through GBD Health data exchange; therefore, ethical approval and informed consent were not required. Each original survey obtained informed consent from participants. No patient or public involvement occurred.

Consent for publication

ASB is a GBD Lead Collaborator and is authorized to conduct and publish independent analyses without requiring additional consent.

Author contributions

Akshaya Srikanth Bhagavathula: Conceptualization; Data curation; Formal analysis; Methodology; Visualization; Validation; Writing – original draft; Writing – review & editing; Software; Investigation.

Wafa Ali Aldhaleei: Conceptualization; Supervision; Writing – original draft; Project administration; Writing – review & editing.

Tadesse Melaku Abegaz: Writing – review & editing; Resources.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

All the data used in this study can be derived from the GBD 2021 (Available at: ) and can be obtained from the corresponding author upon reasonable request.

Supplemental material

Supplemental material for this article is available online.

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