Global Burden of Key Components of Cardiovascular–Kidney–Metabolic Syndrome Among Women of Reproductive Age, 1990–2021

Cuiping Jiang; Jieyuzhen Qiu; Chuanli Wei; Enhui Zhou; Huaming Zhu; Weijun Huang; Tianjiao Zhou; Ji Hu

doi:10.1111/jebm.70180

. 2026 Aug 11;19(3):e70180. doi: 10.1111/jebm.70180

Global Burden of Key Components of Cardiovascular–Kidney–Metabolic Syndrome Among Women of Reproductive Age, 1990–2021

Cuiping Jiang ^1,², Jieyuzhen Qiu ², Chuanli Wei ², Enhui Zhou ^3,⁴, Huaming Zhu ^3,⁴, Weijun Huang ^3,^4,^✉, Tianjiao Zhou ^3,^4,^✉, Ji Hu ^1,^✉

PMCID: PMC13613239 PMID: 42576682

ABSTRACT

Objective

Cardiovascular–kidney–metabolic (CKM) syndrome links metabolic, kidney, and cardiovascular disorders, but its burden among women of reproductive age (WRA) remains poorly understood. We quantified the global burden and trends of key CKM‐related conditions and high body mass index (BMI)‐attributable burden among WRA.

Methods

Using global burden of disease data from 1990 to 2021, we quantified six CKM‐related disease outcomes and the burden attributable to high BMI. Trends were assessed using estimated annual percentage changes (EAPCs), and burden was projected to 2050 using Bayesian age–period–cohort models.

Results

In 2021, diabetes mellitus had the highest incidence rate (273.37 per 100,000 population; 95% uncertainty interval [UI], 204.50–353.52), ischemic heart disease the highest death rate (8.48 per 100,000 population; 95% UI: 7.68–9.35), high BMI the highest attributable disability‐adjusted life‐year (DALY) rate (641.53 per 100,000 population; 95% UI: 283.14–998.09), and chronic kidney disease the highest prevalence rate (6805.22; 95% UI: 5319.94–8575.90). From 1990 to 2021, the age‐standardized death rate attributable to high BMI increased (EAPC, 0.43%; 95% confidence interval [CI]: 0.37%–0.50%), whereas the age‐standardized DALY rate (ASDALYR) for stroke declined (−1.93%; 95% CI: −2.01% to −1.86%). Low–middle sociodemographic index regions had the highest ASDALYR in 2021 (2757.18; 95% UI: 2056.45–3505.64). Under historical‐trend assumptions, high‐BMI‐attributable DALYs were projected to remain dominant through 2050 (1051.49; 95% UI: 235.36–1868.06), with the fastest projected increase in lower extremity peripheral arterial disease (3.39%; 95% CI: 3.13%–3.66%).

Conclusions

Among WRA, CKM‐related burden was substantial and varied across sociodemographic index quintiles. These findings support intensified adiposity control and context‐specific prevention to reduce future burden through 2050.

Keywords: cardiovascular–kidney–metabolic syndrome, global burden of disease, key component, women of reproductive age

1. Introduction

Cardiovascular–kidney–metabolic (CKM) syndrome is a recently proposed multisystem disorder that interlinks cardiovascular disease, chronic kidney disease (CKD), and metabolic dysregulation. Departing from traditional siloed disease models, CKM syndrome highlights shared pathophysiological mechanisms—including obesity, insulin resistance, Type 2 diabetes, dyslipidemia, hypertension, and renal impairment [1]. The American Heart Association (AHA) conceptualizes CKM syndrome as a life‐course continuum, progressing from excess adiposity and early metabolic disturbances to moderate‐high risk of CKD, subclinical cardiovascular damage, and ultimately overt clinical disease [2]. Its complex interplay of organ systems poses a substantial public health challenge, compounding adverse health outcomes and straining healthcare resources. Early detection and intervention, especially during childhood and reproductive years, are essential to mitigate long‐term risks, with the AHA underscoring the role of social determinants of health in shaping disease trajectory [3].

Although the global burden of CKM syndrome is increasingly recognized, specific subgroups, such as women of reproductive age (WRA, 15–49 years), remain inadequately studied despite their distinct physiological and socioeconomic vulnerabilities [4]. This life stage is marked by hormonal fluctuations, metabolic changes, reproductive health exposures, and social determinants that may influence long‐term cardiovascular, kidney, and metabolic risk. Women in this age range also represent an important window for early prevention, because metabolic dysfunction, adiposity, hypertension, and diabetes may emerge before older adulthood and accumulate across the life course. The growing prevalence of obesity and metabolic dysfunction further heightens the vulnerability of WRA, underscoring the need for targeted preventive strategies across the life course.

A comprehensive understanding of the CKM burden in WRA is crucial for developing tailored interventions and optimizing healthcare delivery. The global burden of disease (GBD) study offers a valuable platform for quantifying the worldwide burden of key CKM‐related conditions, providing standardized, comparable estimates of incidence, mortality, and disability‐adjusted life years (DALYs) across geographic regions and over time. Although previous GBD reports have separately examined cardiovascular diseases [5], diabetes, CKD [6], and obesity, few studies have applied an integrated CKM framework. In 2021, key CKM‐related conditions collectively affected over 673.7 million people globally and ranked among the leading causes of health loss [7]. The burden is especially pronounced in middle‐sociodemographic index (SDI) regions such as Southeast Asia and the Western Pacific, whereas low‐SDI regions exhibit higher age‐standardized DALY rates (ASDALYRs).

To our knowledge, no comprehensive GBD‐based analysis has yet systematically characterized the burden of key components of the CKM framework among WRA. To address this gap, we analyzed data from the past three decades to estimate the incidence, mortality, and DALYs attributable to key CKM‐related conditions in WRA worldwide. We stratified these estimates by age, region, and SDI and projected future trends. By generating robust epidemiological evidence, this study aims to inform targeted prevention and control strategies for CKM syndrome in this vulnerable yet pivotal population group at both global and regional levels.

2. Methods

2.1. Data Source

We used publicly available data from the GBD study 1990–2021, which provides standardized, comparable estimates of incidence, prevalence, mortality, and DALYs for 371 causes of disease and injury across 204 countries and territories [8]. We performed a secondary analysis of three decades of GBD data to characterize temporal trends, regional and sociodemographic disparities, and the contribution of key drivers to the burden of CKM specifically among WRA. The comprehensive methodology underlying the GBD study, including data sources, statistical modeling, and uncertainty estimation, has been described in detail elsewhere [9, 10]. The Ethics Committee of the Sixth People's Hospital of Shanghai determined that the study did not need approval because it used publicly available data (MS‐2024‐001).

2.2. CKM Definition and Key Components

In this study, we assessed the population‐level burden of key components of the CKM framework, rather than individual‐level CKM syndrome or CKM staging. On the basis of the AHA CKM framework and recent GBD‐based CKM research, we included seven CKM indicators: diabetes mellitus (DM), CKD, ischemic heart disease (IHD), stroke, atrial fibrillation and flutter (AF/AFL), lower extremity peripheral arterial disease (LEAD), and high body mass index (BMI). DM, CKD, IHD, stroke, AF/AFL, and LEAD were analyzed as disease outcomes, whereas high BMI was analyzed as a metabolic risk factor within the CKM framework. For high BMI, GBD estimates represent attributable deaths, DALYs, years of life lost (YLLs), and years lived with disability (YLDs), rather than incidence or prevalence of a distinct disease. Because GBD provides aggregate‐level estimates, this study could not assess individual‐level CKM stages, multimorbidity, or progression across the CKM continuum [7, 11]. The included CKM indicators were not assumed to be mutually exclusive, and their estimates should not be interpreted as the total burden of an integrated CKM syndrome.

2.3. Burden Estimation

For the six CKM‐related disease outcomes, incidence, prevalence, deaths, DALYs, YLLs, and YLDs were extracted when available. For high BMI, which was extracted from the GBD risk factor estimates, only attributable deaths, DALYs, YLLs, and YLDs were analyzed; incidence and prevalence were not applicable. These estimates represent high‐BMI‐attributable burden derived from the GBD comparative risk assessment framework, rather than the burden of a distinct disease entity. All estimates were reported separately for each CKM indicator, and DALYs, YLLs, and YLDs were not summed across indicators. Age‐standardization was performed on the basis of the GBD standard population. For analyses restricted to women aged 15–49 years, the GBD standard population weights for the corresponding age groups were renormalized to sum to 1. Therefore, the age‐standardized rates (ASRs) represent ASRs within WRA and are not directly comparable with published all‐age GBD ASRs. ASRs were calculated according to the following formula [12]:

ASR = \frac{\sum_{i = 1}^{N} w_{i} • r_{i}}{\sum_{i = 1}^{N} w_{i}}

(1)

where r_i represents the age‐specific rate, and w_i represents the renormalized GBD standard population weight in each 5‐year age group. N is the total number of age groups from 15 to 49 years. Temporal trends were evaluated using the estimated annual percentage change (EAPC), derived from a log‐linear regression model: ln(ASR _t ) = α + βt + ϵ, with EAPC = 100 × (e ^β − 1) [4]. t is the calendar year. We considered an ASR to reflect an increasing or decreasing trend over time if both the EAPC and its 95% confidence interval (CI) were entirely above or below zero, respectively. When the 95% CI included zero, the change in ASR was regarded as statistically non‐significant. Estimates were stratified by 5‐year age groups (15–19, …, 45–49 years), 21 GBD regions, and SDI regions to examine temporal, geographic, and socioeconomic heterogeneity.

2.4. Health Inequality and Projection Analysis

Health disparities were assessed using the slope index of inequality (SII) and concentration index across 204 countries and territories [13]. Positive and negative SII values indicate concentration of disease burden in high‐ and low‐SDI regions, respectively. Future burden of the included CKM indicators from 2022 to 2050 was projected using a Bayesian age–period–cohort (BAPC) model [14]. Age‐specific counts were modeled using a Poisson distribution with population size as the offset. Age, period, and cohort effects were incorporated using second‐order random walk (RW2) priors, along with an overdispersion term. The main model used log‐gamma (1, 0.00005) priors for the precision parameters of age, period, and cohort effects and log‐gamma (1, 0.005) for overdispersion. Models were fitted using the BAPC package with integrated nested Laplace approximation (INLA). As INLA does not rely on Markov chain Monte Carlo (MCMC) sampling, conventional convergence diagnostics were not applicable; model stability was assessed based on fitting success and posterior summaries. ASRs were calculated from projected counts and populations, scaled per 100,000 and weighted by the standard population. Uncertainty was summarized using posterior means and 95% uncertainty intervals (UIs). Model performance was evaluated through retrospective validation by holding out data from 2017 to 2021 and comparing predicted and observed values. Sensitivity analyses were conducted using alternative prior settings and a scaled RW2 model to assess the robustness of projections.

2.5. Statistical Analysis

All statistical analyses were performed using R software, version 4.4.2 (R Foundation for Statistical Computing). EAPCs were estimated with log‐linear regression and reported with 95% CI. For all other estimates, 95% UIs were derived from 1000 posterior draws from the GBD Bayesian modeling framework. To illustrate disparities and temporal trends, figures stratified by age, region, and SDI were generated with the ggplot2 package and refined in Adobe Illustrator (Adobe Inc.).

3. Results

3.1. Global Burden and Trend

Globally, the cases and rate of key CKM components among WRA showed significant heterogeneity (Table 1). Among these components, DM exhibited the highest age‐standardized incidence rate (ASIR) at 273.37 (95% UI: 204.5–353.52) per 100,000 population in 2021. In contrast, IHD demonstrated the highest age‐standardized death rate (ASDR) at 8.48 (95% UI: 7.68–9.35) per 100,000 population. High BMI was associated with the highest ASDALYR at 641.53 (95% UI: 283.14–998.09). Meanwhile, CKD showed the highest age‐standardized prevalence rate (ASPR) at 6805.22 (95% UI: 5319.94–8575.9), reflecting its widespread occurrence in this population. Conversely, AF/AFL presented the lowest ASR of burden across all measured metrics among CKM components. From 1990 to 2021, AF/AFL, CKD, and high BMI showed increasing attributable health loss (rate change > 0), whereas stroke demonstrated a decline (rate change < 0). The EAPC across the included CKM indicators also demonstrated significant heterogeneity from 1990 to 2021. DM exhibited the highest EAPCs across multiple metrics, including the ASIR 1.92 (95% CI: 1.88–1.96), ASPR 2.25 (95% CI: 2.18–2.33), and ASDALYR 1.16 (95% CI: 1.09–1.24). Conversely, high BMI demonstrated the highest EAPC of ASDR at 0.43 (95% CI: 0.37–0.50). In contrast, stroke showed the lowest EAPCs of ASIR (−1.16 [95% CI: −1.28 to −1.05]), ASPR (−0.69 [95% CI: −0.73 to −0.64]), ASDR (−2.17 [95% CI: −2.25 to −2.08]), and ASDALYR (−1.93 [95% CI: −2.01 to −1.86]).

TABLE 1.

Global burden and trends of key CKM‐related conditions among women of reproductive age in 2021.

Cause name	Measure name	Number 1990 (×100,000)	Number 2021 (×100,000)	Rate 1990 (per 100,000)	Rate 2021 (per 100,000)	Rate Change (%)	ASR 1990 (per 100,000)	ASR 2021 (per 100,000)	EAPC (%)
Atrial fibrillation and flutter	DALYs	0.55 (0.37, 0.81)	1.07 (0.72, 1.55)	4.09 (2.77, 6.06)	5.48 (3.72, 7.95)	34.13	5.02 (3.3, 7.45)	5.17 (3.43, 7.61)	0.18 (0.13, 0.22)
	Deaths	0.00 (0.00, 0.01)	0.01 (0.01, 0.01)	0.03 (0.03, 0.04)	0.04 (0.03, 0.04)	26.97	0.07 (0.06, 0.09)	0.07 (0.06, 0.08)	−0.19 (−0.24, −0.14)
	Incidence	0.76 (0.49, 1.1)	1.47 (0.98, 2.12)	5.68 (3.66, 8.25)	7.54 (5.01, 10.9)	32.87	6.82 (3.71, 11.21)	7.15 (4.04, 11.52)	0.23 (0.19, 0.28)
	Prevalence	4.36 (2.63, 7.05)	8.81 (5.62, 13.54)	32.59 (19.64, 52.72)	45.2 (28.85, 69.46)	38.68	40.15 (23.49, 63.54)	42.59 (26.24, 65.46)	0.36 (0.28, 0.43)
	YLDs	0.36 (0.19, 0.62)	0.72 (0.39, 1.19)	2.68 (1.4, 4.64)	3.7 (1.99, 6.12)	38.36	3.29 (1.62, 5.63)	3.49 (1.78, 5.82)	0.36 (0.28, 0.43)
	YLLs	0.19 (0.16, 0.24)	0.35 (0.3, 0.39)	1.41 (1.18, 1.77)	1.78 (1.53, 2.02)	26.09	3.31 (2.73, 4.16)	3.22 (2.73, 3.66)	−0.18 (−0.23, −0.13)
Chronic kidney disease	DALYs	30.83 (27.45, 34.72)	48.5 (43.32, 54.45)	230.56 (205.25, 259.6)	248.88 (222.27, 279.41)	7.95	245.8 (215.69, 281.56)	243.3 (212.05, 281.21)	−0.16 (−0.23, −0.09)
	Deaths	0.43 (0.39, 0.48)	0.7 (0.63, 0.78)	3.22 (2.93, 3.62)	3.58 (3.21, 3.99)	11.31	3.51 (3.15, 3.96)	3.48 (3.1, 3.91)	−0.21 (−0.31, −0.11)
	Incidence	6.55 (5.36, 7.82)	13.55 (11.43, 15.85)	48.96 (40.05, 58.45)	69.51 (58.67, 81.32)	41.95	55.1 (34.02, 79.89)	67.07 (44.04, 91.72)	0.63 (0.59, 0.66)
	Prevalence	825.5 (738.88, 925.45)	1353.93 (1200.76, 1529.92)	6172.59 (5524.88, 6919.96)	6947.29 (6161.37, 7850.34)	12.55	6544.61 (5179.86, 8200.01)	6805.22 (5319.94, 8575.9)	0.14 (0.11, 0.17)
	YLDs	7.39 (5.08, 10.16)	11.94 (7.99, 16.23)	55.28 (38, 75.99)	61.26 (41.01, 83.26)	10.81	59.5 (37.02, 88.16)	59.69 (36.64, 89.38)	0.09 (0.02, 0.15)
	YLLs	23.44 (21.23, 26.38)	36.56 (32.69, 40.78)	175.28 (158.71, 197.24)	187.62 (167.75, 209.23)	7.04	186.3 (166.9, 210.77)	183.6 (163.41, 206.34)	−0.24 (−0.34, −0.14)
Diabetes mellitus	DALYs	31.5 (26.41, 37.7)	78.19 (61.35, 98.61)	235.54 (197.46, 281.91)	401.22 (314.79, 505.98)	70.34	260.77 (216.66, 317)	388.39 (300.74, 496.26)	1.16 (1.09, 1.24)
	Deaths	0.31 (0.29, 0.34)	0.53 (0.49, 0.57)	2.33 (2.17, 2.57)	2.71 (2.49, 2.95)	16.39	2.64 (2.45, 2.92)	2.6 (2.38, 2.85)	−0.26 (−0.37, −0.15)
	Incidence	18.27 (16.15, 20.68)	54.24 (48.41, 61.02)	136.62 (120.76, 154.61)	278.33 (248.39, 313.11)	103.72	146.45 (106.38, 196.22)	273.37 (204.5, 353.52)	1.92 (1.88, 1.96)
	Prevalence	240.7 (213.38, 271.67)	790.36 (710.61, 877.56)	1799.82 (1595.49, 2031.41)	4055.51 (3646.29, 4502.94)	125.33	1960.81 (1663.34, 2289.02)	3942.17 (3413.42, 4517.68)	2.25 (2.18, 2.33)
	YLDs	15.33 (10.17, 21.52)	51.73 (34.38, 72.15)	114.65 (76.04, 160.88)	265.42 (176.42, 370.2)	131.5	127.09 (83.69, 181.43)	256.83 (169.58, 363.93)	2.25 (2.19, 2.31)
	YLLs	16.17 (15.08, 17.92)	26.47 (24.37, 28.82)	120.88 (112.76, 134)	135.8 (125.06, 147.86)	12.34	133.67 (124.11, 148.33)	131.56 (120.03, 143.96)	−0.28 (−0.38, −0.17)
High body‐mass index	DALYs	51.97 (22.7, 84.77)	130.03 (58.43, 204.12)	388.63 (169.72, 633.86)	667.23 (299.8, 1047.4)	71.69	444.14 (194.54, 728.63)	641.53 (283.14, 998.09)	1.14 (1.1, 1.18)
	Deaths	0.58 (0.32, 0.89)	1.23 (0.65, 1.82)	4.3 (2.36, 6.66)	6.31 (3.33, 9.33)	46.77	5.06 (2.74, 7.84)	6.02 (3.22, 8.87)	0.43 (0.37, 0.5)
	YLDs	23.73 (7.18, 43.03)	70.37 (26.48, 121.3)	177.41 (53.71, 321.72)	361.1 (135.9, 622.41)	103.54	199.43 (59.61, 363.2)	348.42 (126.34, 602.51)	1.85 (1.81, 1.89)
	YLLs	28.25 (15.51, 43.97)	59.66 (31.41, 88.47)	211.22 (115.97, 328.81)	306.12 (161.2, 453.95)	44.93	244.71 (132.69, 380.67)	293.11 (156.78, 433.26)	0.46 (0.39, 0.52)
Ischemic heart disease	DALYs	67.62 (60.71, 74.86)	87.13 (79.95, 95.31)	505.64 (453.94, 559.79)	447.07 (410.25, 489.06)	−11.58	567.38 (508.67, 626.13)	431.36 (391.24, 475.8)	−0.98 (−1.03, −0.93)
	Deaths	1.31 (1.18, 1.44)	1.72 (1.58, 1.89)	9.78 (8.81, 10.79)	8.84 (8.08, 9.68)	−9.68	11.21 (10.08, 12.33)	8.48 (7.68, 9.35)	−1 (−1.05, −0.94)
	Incidence	6.78 (4.72, 9.12)	13.5 (9.25, 18.49)	50.71 (35.33, 68.2)	69.25 (47.44, 94.88)	36.56	58.65 (33.9, 90.25)	66.19 (38.34, 101.58)	0.43 (0.4, 0.47)
	Prevalence	43.34 (36.65, 51.2)	87.06 (70.67, 107.08)	324.04 (274.04, 382.85)	446.7 (362.61, 549.46)	37.85	381.37 (307.87, 467.62)	424.96 (332.14, 542.06)	0.31 (0.28, 0.33)
	YLDs	0.82 (0.54, 1.21)	1.57 (1.01, 2.29)	6.16 (4.05, 9.04)	8.04 (5.2, 11.77)	30.38	7.05 (4.42, 10.73)	7.71 (4.83, 11.76)	0.33 (0.31, 0.35)
	YLLs	66.8 (59.85, 73.94)	85.56 (78.25, 93.8)	499.48 (447.54, 552.87)	439.04 (401.54, 481.32)	−12.1	560.33 (501.81, 619.03)	423.65 (383.44, 467.81)	−1 (−1.05, −0.95)
Lower extremity peripheral arterial disease	DALYs	0.07 (0.06, 0.07)	0.12 (0.1, 0.14)	0.5 (0.46, 0.55)	0.61 (0.53, 0.71)	22.01	0.63 (0.58, 0.7)	0.58 (0.5, 0.67)	−0.86 (−1.1, −0.62)
	Deaths	0 (0, 0)	0 (0, 0)	0.01 (0.01, 0.01)	0.01 (0.01, 0.02)	23.15	0.06 (0.05, 0.06)	0.05 (0.05, 0.06)	−0.85 (−1.09, −0.6)
	Incidence	3.67 (2.76, 4.76)	6.3 (4.67, 8.22)	27.44 (20.62, 35.61)	32.31 (23.95, 42.17)	17.73	34.32 (25.05, 45.69)	30.47 (22.02, 40.52)	−0.43 (−0.46, −0.39)
	Prevalence	27.81 (22.52, 33.75)	46.21 (37.69, 55.87)	207.95 (168.37, 252.33)	237.1 (193.4, 286.67)	14.02	262.13 (209.94, 326.27)	222.69 (177.8, 275.54)	−0.58 (−0.6, −0.56)
	YLDs	0 (0, 0.01)	0 (0, 0.01)	0.02 (0.01, 0.04)	0.02 (0, 0.04)	−8.39	0.02 (0.01, 0.05)	0.01 (0, 0.04)	−1.37 (−1.42, −1.33)
	YLLs	0.06 (0.06, 0.07)	0.12 (0.1, 0.13)	0.48 (0.44, 0.53)	0.59 (0.51, 0.69)	23.12	2.58 (2.37, 2.85)	2.38 (2.05, 2.77)	−0.85 (−1.09, −0.6)
Stroke	DALYs	86.08 (78.7, 93.57)	84.24 (76.41, 92.1)	643.67 (588.49, 699.66)	432.28 (392.06, 472.61)	−32.84	723.45 (659.36, 791)	417.52 (375.87, 458.63)	−1.93 (−2.01, −1.86)
	Deaths	1.46 (1.34, 1.59)	1.38 (1.26, 1.52)	10.9 (10.01, 11.88)	7.07 (6.44, 7.79)	−35.2	12.6 (11.48, 13.8)	6.77 (6.13, 7.51)	−2.16 (−2.25, −2.08)
	Incidence	6.11 (5.27, 7.12)	7.79 (6.71, 9.07)	45.67 (39.39, 53.23)	39.99 (34.44, 46.54)	−12.43	50.77 (38.23, 66.34)	38.75 (28.92, 51.19)	−1.16 (−1.28, −1.05)
	Prevalence	75.42 (69.02, 81.69)	102.8 (94.9, 110.04)	563.97 (516.09, 610.83)	527.51 (486.94, 564.65)	−6.46	609.81 (551.47, 671.19)	514.18 (469.07, 561.65)	−0.69 (−0.73, −0.64)
	YLDs	12.41 (8.74, 15.88)	16.35 (11.64, 20.9)	92.78 (65.36, 118.77)	83.89 (59.7, 107.25)	−9.59	99.95 (70.19, 129.64)	81.77 (57.64, 105.82)	−0.77 (−0.81, −0.73)
	YLLs	73.67 (67.66, 80.39)	67.9 (61.75, 74.86)	550.89 (505.96, 601.07)	348.38 (316.84, 384.11)	−36.76	623.5 (567.13, 684.11)	335.74 (303.38, 372.98)	−2.16 (−2.24, −2.08)

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Note: Data are presented as estimates (95%UIs), except for EAPCs, which are presented as estimates (95%CIs). Rates are expressed per 100,000 population. High BMI is classified as a risk factor in GBD; therefore, its deaths, DALYs, YLDs, and YLLs represent attributable burden estimated using the comparative risk assessment framework. Incidence and prevalence are not applicable to high BMI.

Abbreviations: ASR, age‐standardized rate; BMI, body mass index; CI, confidence interval; CKM, cardiovascular–kidney–metabolic; DALYs, disability‐adjusted life years; EAPC, estimated annual percentage change; GBD, Global Burden of Disease; UI, uncertainty interval; YLDs, years lived with disability; YLLs, years of life lost.

3.2. Burden and Trends of 21 GBD Regions

Substantial regional disparities in the burden of key CKM‐related conditions were observed across the 21 GBD regions in 2021. Oceania exhibited the highest ASDALYR for high BMI at 1755.41 (95% UI: 876.41–2698.03), DM at 1385.63 (95% UI: 1080.95–1760.12), and stroke at 1089.66 (95% UI: 737.37–1570.01) (Figure S1A). Differences in age‐standardized YLD rate (ASYLDR) were more pronounced, with the highest ASYLDR for high BMI observed in Central Latin America at 682.65 (95% UI: 278.43–1129.64), and lowest for LEAD in high‐income North America at 0.07 (95% UI: 0.02–0.15) (Figure S1B). Regarding ASYLLR, Oceania showed the greatest burden from high BMI at 1159.87 (95% UI: 591.35–1812.82), whereas the lowest rate for LEAD was observed in southern sub‐Saharan Africa at 23.19 (95% UI: 14.72–33.93) (Figure S1C).

Notable regional variations in the EAPC of key CKM indicators were observed across the 21 GBD regions. For ASDALYR, the highest positive EAPC was found in southern sub‐Saharan Africa for LEAD at 2.40 (95% CI: 1.77–3.03), whereas the most pronounced decline was observed in the Central Europe for IHD at −3.9 (95% CI: −4.09 to −3.71) (Figure 1A). In terms of ASYLDR, the highest EAPC was reported for DM in North Africa and the Middle East at 3.19 (95% CI: 3.14–3.24), whereas LEAD in high‐income Asia Pacific showed a slight decline at −2.6 (95%CI: −2.99 to −2.22) (Figure 1B). Regarding ASYLLR, the largest increase was seen for CKD in high‐income North America at 3.57 (95% CI: 3.25–3.88), whereas the Central Europe exhibited the steepest decrease for stroke at −4.16 (95% CI: −4.41 to −3.91) (Figure 1C).

EAPC in age‐standardized rates of DALYs (A), YLDs (B), and YLLs (C) for key CKM components among WRA across 21 GBD regions, 1990–2021. AF/AFL, atrial fibrillation and flutter; BMI, body mass index; CKD, chronic kidney disease; CKM, cardiovascular–kidney–metabolic; DALYs, disability‐adjusted life years; DM, diabetes mellitus; EAPC, estimated annual percentage change; GBD, global burden of disease; IHD, ischemic heart disease; LEAD, lower extremity peripheral arterial disease; WRA, women of reproductive age; YLDs, years lived with disability; YLLs, years of life lost.

3.3. Global Burden and Trends by SDI Levels

Marked heterogeneity in the burden of included CKM indicators was observed across SDI levels. The ASDALYR peaked in low‐middle SDI regions at 2757.18 (95% UI: 2056.45–3505.64) and was lowest in high SDI regions at 1434.36 (95% UI: 894.68–2025.00) (Figure 2A). In contrast, ASYLDR was highest in high SDI regions at 864.79 (95% UI: 413.75–1385.54) and lowest in low SDI regions at 611.30 (95% UI: 334.47–937.18) (Figure 2B). The distribution of YLLs mirrored that of DALYs, with the highest ASRs observed in low–middle SDI regions at 2011.39 (95% UI: 1597.32–2467.39) and the lowest in high SDI regions at 573.99 (95% UI: 464.51–687.53) (Figure 2C). High BMI was the leading contributor to attributable ASDALYR in low–middle SDI regions at 730.87 (95% UI: 333.52–1131.60) and to attributable ASYLDR in high SDI regions at 487.13 (95% UI: 165.39–853.90). IHD remained the predominant cause of ASYLLR in low‐middle SDI regions, reaching 702.58 (95% UI: 608.46–808.19).

Age‐standardized rates of DALYs (A), YLDs (B), and YLLs (C) for key CKM components among WRA across the globe and five SDI‐level regions, 1990–2021. AF/AFL, atrial fibrillation and flutter; BMI, body mass index; CKD, chronic kidney disease; CKM, cardiovascular–kidney–metabolic; DALYs, disability‐adjusted life years; DM, diabetes mellitus; IHD, ischemic heart disease; LEAD, lower extremity peripheral arterial disease; SDI, sociodemographic index; WRA, women of reproductive age; YLDs, years lived with disability; YLLs, years of life lost.

Substantial disparities in trends of the included CKM indicators were observed across SDI regions from 1990 to 2021 (Figures S2 and S3). In high SDI regions, DM showed the most pronounced increase in ASDALYR, with an EAPC of 1.62 (95% CI: 1.52–1.71). In contrast, stroke in high–middle SDI regions experienced the steepest decline, with an EAPC of −2.77 (95% CI: −2.99 to −2.55) (Figure 3A). For ASYLDR, high SDI regions saw the greatest rise in DM, with an EAPC of 2.90 (95% CI: 2.83–2.96), whereas LEAD in high–middle SDI regions showed the largest decrease, with an EAPC of −1.10 (95% CI: −1.79 to −0.40) (Figure 3B). Regarding ASYLLR, CKD in high SDI regions recorded the highest increasing trend, with an EAPC of 1.61 (95% CI: 1.40–1.81), whereas stroke in high–middle SDI regions had the largest decline, with an EAPC of −3.28 (95% CI: −3.56 to −3.01) (Figure 3C).

EAPC in age‐standardized rates of DALYs (A), YLDs (B), and YLLs (C) for key CKM components among WRA across the global and five SDI‐level regions, 1990–2021. AF/AFL, atrial fibrillation and flutter; BMI, body mass index; CKD, chronic kidney disease; CKM, cardiovascular–kidney–metabolic; DALYs, disability‐adjusted life years; DM, diabetes mellitus; EAPC, estimated annual percentage change; IHD, ischemic heart disease; LEAD, lower extremity peripheral arterial disease; SDI, sociodemographic index; WRA, women of reproductive age; YLDs, years lived with disability; YLLs, years of life lost.

3.4. Health Inequities Analysis by SDI Index

In 2021, substantial health inequalities in the burden of included CKM indicators were observed across 204 countries and territories according to SDI. Among the included CKM indicators, stroke exhibited the largest inequality in ASDALYRs, with an SII of −734.56 (95% CI: −865.46 to −603.67) and a corresponding concentration index of −0.26 (95% CI: −0.38 to −0.14). This was followed by IHD, high BMI, DM, CKD, and AF/AFL. Notably, LEAD was the only condition for which both the ASDALYR SII and concentration index were greater than zero, with an SII of 0.08 (95% CI: −0.39 to 0.55) and a concentration index of 0.016 (95% CI: −0.03 to 0.06), indicating a slight concentration of LEAD burden among higher SDI regions (Figure 4A,D). For ASYLDRs, high BMI showed the highest inequality with an SII of 185.38 (95% CI: 75.21–295.56), whereas DM had the lowest SII at −43.28 (95% CI: −144.85 to 58.28) (Figure 4B,E). In terms of ASYLLR, stroke again demonstrated the greatest inequality with an SII of −708.04 (95% CI: −827.03 to −589.05), followed by high BMI, IHD, CKD, DM, and AF/AFL. LEAD remained the only component with a positive SII for YLL rates, at 0.24 (95% CI: −1.75 to 2.23) (Figure 4C,F).

SII of age‐standardized rates of DALYs (A), YLDs (B), and YLLs (C) and concentration index of age‐standardized rates of DALYs (D), YLDs (E), and YLLs (F) for key CKM components among WRA across countries and territories in 2021. AF/AFL, atrial fibrillation and flutter; BMI, body mass index; CKD, chronic kidney disease; CKM, cardiovascular–kidney–metabolic; DALYs, disability‐adjusted life years; DM, diabetes mellitus; IHD, ischemic heart disease; LEAD, lower extremity peripheral arterial disease; SII, slope index of inequality; WRA, women of reproductive age; YLDs, years lived with disability; YLLs, years of life lost.

3.5. Global Burden and Trends by Age Groups

Globally, WRA exhibited substantial age‐stratified disparities in the burden rates of included CKM indicators (Table S1, Figure S4). For DALYs, stroke predominated in the 15–19 years group (124.56; 95% UI: 109.56–139.41), whereas high BMI demonstrated the highest attributable DALY rate across all 20–49 years age groups, peaking at 45–49 years (1889.1; 95% UI: 847.76–2836.39) (Figure 5A). Regarding YLDs, DM showed the highest burden in 15–19 years (45.3; 95% UI: 28.5–70.52), whereas high‐BMI‐attributable YLD rates dominated in 20–49 years groups with an age‐dependent increase, reaching its zenith at 45–49 years (901.87; 95% UI: 320.41–1581.22) (Figure 5B). For YLLs, IHD consistently exhibited the highest rates throughout 15–49 years, escalating with age and peaking at 45–49 years (1255.47; 95% UI: 1145.15–1371.32) (Figure 5C). For DALYs rates, high BMI exhibited the highest EAPC among 15–34‐year‐olds, peaking in the 30–34 years group (EAPC 1.4, 95% CI: 1.33–1.47) (Figure 5D). Conversely, DM showed the most rapid increase among 35–49‐year‐olds, with the 35–39 years group demonstrating the highest EAPC (1.32; 95% CI: 1.25–1.39). For YLDs rates, DM predominated with the steepest growth throughout 15–49‐year‐olds, peaking at 25–29 years (2.45; 95% CI: 2.33–2.56) (Figure 5E). For YLLs rates, high BMI consistently displayed the highest EAPC across all 15–49‐year‐olds, reaching its zenith in the 20–24 years group (0.93; 95% CI: 0.83–1.04) (Figure 5F). For YLDs rates, DM predominated with the steepest growth throughout 15–49‐year‐olds, peaking at 25–29 years (2.45; 95% CI: 2.33–2.56) (Figure 5E). For YLLs rates, high BMI consistently displayed the highest EAPC across all 15–49‐year‐olds, reaching its zenith in the 20–24 years group (0.93; 95% CI: 0.83–1.04) (Figure 5F).

Age‐specific rates of DALYs (A), YLDs (B), and YLLs (C), and EAPCs in age‐specific rates of DALYs (D), YLDs (E), and YLLs (F), for the included CKM indicators among WRA globally by 5‐year age group, 1990–2021. AF/AFL, atrial fibrillation and flutter; BMI, body mass index; CKD, chronic kidney disease; CKM, cardiovascular–kidney–metabolic; DALYs, disability‐adjusted life years; DM, diabetes mellitus; EAPC, estimated annual percentage change; IHD, ischemic heart disease; LEAD, lower extremity peripheral arterial disease; WRA, women of reproductive age; YLDs, years lived with disability; YLLs, years of life lost.

3.6. Decomposition Analysis

For changes in DALYs across the included CKM indicators, epidemiological effects, age structure shifts, and population growth collectively exerted positive contributions. However, for stroke, age‐structure changes and population growth showed negative contributions, offsetting DALY growth (−70.1% and −121.4%, respectively). Concurrently, epidemiological effects counteracted increases in LEAD (−48.8%) and IHD (−380.1%), a pattern also observed for YLL trends across the included CKM indicators. Regarding YLDs, epidemiological effects uniquely mitigated burden growth for stroke (−180.7%), LEAD (−2034.3%), and high BMI (−535.4%) (Figure S5).

3.7. Projected Burden Trends by BAPC Model, 2022–2050

From 2022 to 2050, projections indicate varying trends in the ASDALYRs of key CKM‐related conditions (Table S2). By 2050, high BMI was projected to remain the largest contributor to attributable DALYs among the included CKM indicators, whereas LEAD was projected to have the lowest. Among the included CKM indicators, LEAD showed the steepest projected increase (EAPC 3.39; 95% CI: 3.13–3.66), whereas AF/AFL showed the steepest projected reduction (EAPC −1.25; 95% CI: −2.13 to −0.36) (Figure 6A). In terms of age‐standardized YLD rates, DM was projected to become the leading contributor by 2050, whereas LEAD was projected to contribute the least. DM showed the steepest projected increase (EAPC 3.63; 95% CI: 3.58–3.68), whereas IHD showed the steepest projected decline over time (EAPC −2.86; 95% CI: −3.16 to −2.55) (Figure 6B). For age‐standardized YLL rates, IHD was projected to remain the predominant cause of premature mortality by 2050, whereas AF/AFL was projected to contribute the least. LEAD was the only component with a projected upward trend (EAPC 3.49; 95% CI: 3.21–3.77). Stroke showed the steepest projected decline in YLL rates (EAPC −0.98; 95% CI: −1.07 to −0.89) (Figure 6C). Retrospective validation supported the predictive performance of the BAPC models, with close agreement between predicted and observed ASRs across the held‐out period of 2017–2021. Overall, the mean absolute percentage error was 1.34, and the 95% UI was 97.1% across 105 validation comparisons (Table S3). Sensitivity analyses using alternative prior specifications and a scaled RW2 model generally supported the main projection patterns, although several low‐burden or near‐null indicators showed model‐dependent directionality and were, therefore, interpreted cautiously.

Projected numbers and age‐standardized rates of DALYs (A), YLDs (B), and YLLs (C) for the included CKM indicators among WRA globally, 2022–2050. AF/AFL, atrial fibrillation and flutter; BAPC, Bayesian age–period–cohort; BMI, body mass index; CKD, chronic kidney disease; CKM, cardiovascular–kidney–metabolic; DALYs, disability‐adjusted life years; DM, diabetes mellitus; IHD, ischemic heart disease; LEAD, lower extremity peripheral arterial disease; WRA, women of reproductive age; YLDs, years lived with disability; YLLs, years of life lost.

4. Discussion

In this study, we used the GBD database to systematically analyze the burden and trends of key CKM‐related conditions and high‐BMI‐attributable health loss among WRA, a population particularly vulnerable due to biological, metabolic, and social factors, from 1990 to 2021, with projections to 2050. Our findings highlight marked heterogeneity in CKM burden across disease types, age groups, and geographic regions.

Globally, high‐BMI‐attributable health loss and DM emerged as dominant drivers of key components of CKM among WRA, consistent with prior evidence that obesity serves as a core driver of metabolic risk in young women [15, 16]. The elevated ASR for high‐BMI‐attributable burden underscores obesity as a central mediator of cardiometabolic and renal dysfunction in this demographic. DM also showed a rapid and steady increase, strengthening its threat to women's health [17]. In contrast, stroke remained the second leading cause of death globally [18], but its burden declined among WRA. The declining burden of stroke is encouraging and may reflect improvements in acute care and preventive strategies [19]. Meanwhile, the considerable prevalence of CKD—reflected in the highest ASPR—signals widespread subclinical renal impairment, often undetected in early stages yet conducive to long‐term morbidity. In addition, AF/AFL has emerged as an increasingly important condition with notable sex differences [20, 21]. Although AF/AFL currently accounts for the lowest burden among WRA, it exhibited the fastest growth rate, suggesting that the cumulative risk of arrhythmia in young women is escalating and strongly linked to rising BMI [22], hypertension [23], and other metabolic risk factors. Notably, substantial geographic disparities were observed. Oceania had the highest burden for many CKM diseases, in line with studies on nutrition transition and weak health systems [24, 25]. Socioeconomic development, proxied by the SDI, played a critical role in shaping the distribution and trends of CKM disease burden.

High‐income countries showed progress in IHD and stroke, but rising CKD suggests new public health challenges in high‐SDI regions [26]. Low‐ and middle‐SDI regions showed the highest DALY rates, driven largely by fatal conditions like IHD and stroke. Conversely, high‐SDI regions reported the highest YLD rates, indicative of a nonfatal burden dominated by high BMI and DM. The estimated inequalities further revealed that conditions such as stroke and IHD disproportionately affect lower SDI populations, highlighting systemic gaps in prevention, diagnosis, and treatment. Interestingly, LEAD was the only condition with a positive inequality index, suggesting a greater burden in higher SDI settings, possibly due to better detection and longer life expectancy allowing for manifestation of peripheral vascular disease. SDI stratification showed the heaviest CKM burden in low–middle SDI regions, with the highest DALYs and YLLs. This reflects economic gaps, limited health resources, poor screening, and restricted treatment access [27]. Interestingly, unlike prior studies where CKD burden was higher in high‐SDI regions [28], we found CKD burden among WRA was concentrated in low‐SDI areas. This suggests the need for tailored strategies for WRA in low‐SDI regions, likely compounded by limited healthcare access, nutritional transition, and health system fragmentation.

Age‐stratified analyses indicated that high BMI was the leading contributor to DALYs among women aged 20–49 years. The burden peaked in the 45–49‐year group. This pattern is likely driven by multiple physiological and metabolic factors. Hormonal fluctuations across the female life course play a critical role. During reproductive years, higher estrogen levels modulate lipid metabolism, vascular function, and fat distribution. However, declining estrogen levels during the perimenopausal period contribute to fat accumulation, insulin resistance, and elevated blood pressure [29]. These changes increase the risk of cardiovascular and metabolic disorders. Pregnancy and related complications further exacerbate metabolic burden. Gestational weight gain, physiologic insulin resistance, and conditions such as gestational diabetes or hypertension can lead to sustained post‐partum high‐BMI and long‐term metabolic dysfunction [30]. Furthermore, hormonal fluctuations during the perimenopausal period not only affect lipid metabolism but may also lead to elevated blood pressure, glycemic variability, and increased inflammation [31]. These changes further promote the development of CKM in WRA. IHD remained the predominant cause of YLLs across all adult age groups. This underscores the importance of early cardiovascular risk assessment, even before clinical disease manifests. Diabetes‐related YLDs increased rapidly among younger women. For example, the EAPC was 2.45 in 25–29 years old women. This trend suggests that metabolic dysfunction is emerging at increasingly younger ages. It may reflect rising childhood and adolescent obesity [32], suboptimal lifestyle factors, inadequate management of gestational weight gain, and hormonal or metabolic dysregulation. High BMI may progress to diabetes or metabolic syndrome. This further increases the risk of CKD, lower extremity arterial disease, and cardiovascular events. These developments have substantial implications for long‐term quality of life and productivity. These results support a life‐course approach to CKM management [33, 34]. High BMI may develop into diabetes or metabolic syndrome, suggesting age‐specific interventions are needed. Together, these findings highlight variation in CKM burden by disease type, age, and geography. Importantly, WRA face unique metabolic and hormonal exposures that amplify cardiometabolic risk. For instance, polycystic ovary syndrome is highly prevalent in this age group, frequently accompanied by insulin resistance and features of metabolic syndrome, thereby heightening susceptibility to DM and cardiovascular diseases [35]. Moreover, pregnancy and perinatal hormonal fluctuations can exacerbate disturbances in glucose, blood pressure, and lipid metabolism, further accelerating the trajectory of CKM‐related diseases. These female‐specific determinants may partly explain the persistent upward trends of DM and IHD observed among WRA. Taken together, the CKM disease burden among WRA reflects distinct, sex‐specific patterns that warrant prioritization in both public health strategies and clinical management.

Decomposition analysis clarified the drivers behind temporal changes: population growth and aging contributed significantly to the rising burden of most CKM components, whereas favorable epidemiological changes—likely due to improved healthcare and policies—counteracted the increase in IHD and LEAD burden. This underlines the potential of public health action to attenuate disease progression even amid demographic pressures. Our projections suggested a potential rise in high‐BMI‐attributable burden and LEAD‐related burden through 2050 under historical‐trend assumptions, whereas several cardiovascular outcomes showed declining or model‐sensitive projected patterns. However, these estimates should be interpreted as scenario‐based projections rather than deterministic predictions.

Importantly, BAPC projections assume that historical age, period, and cohort patterns, together with projected population structures, continue into the future [36]. Therefore, the projected persistence of high‐BMI‐attributable burden and the projected decline in stroke‐related burden should be understood under these historical‐trend assumptions. Future trajectories could be substantially modified by changes in obesity prevention and the population‐level uptake of weight‐management therapies. In particular, semaglutide and tirzepatide have shown clinically meaningful weight‐loss effects in adults with overweight or obesity [37, 38], and semaglutide has also been associated with reduced cardiovascular events among selected patients with overweight or obesity and established cardiovascular disease [39]. Future burden may also be influenced by access to cardiovascular, kidney, and metabolic care, as reflected by variation in effective universal health coverage [40], and by the implementation of national noncommunicable disease prevention policies [41]. These projections should, therefore, be used to inform health‐system planning and hypothesis generation, while recognizing that future prevention and treatment strategies may alter the estimated burden.

Our analysis highlights the distinct risk patterns among WRA and underscores shifting epidemiological patterns that demand targeted intervention. These findings have major implications for global health policy and practice. Control of high BMI and DM should be the central focus, especially in younger women. Regional strategies should differ by SDI. In high‐SDI regions, early screening and prevention of CKD and LEAD are needed to avoid future crises. In low–middle SDI regions, reducing obesity and IHD burden and strengthening primary health care are priorities [42]. Age‐ and sex‐specific strategies are also essential. For women aged 15–34 years, prevention of obesity and metabolic risks is critical. For women aged 35–49 years, stronger secondary prevention of diabetes and cardiovascular disease is required.

Although most previous studies have focused on general populations or on men, systematic evaluations specifically targeting WRA have been limited. These findings must be interpreted considering several limitations. First, GBD estimates rely on modeled data, which may be less precise in regions with limited vital registration and health information systems, which may lead to underdiagnosis, misclassification, and country‐level differences in data quality, especially in low‐resource settings [43]. Second, we focused only on women aged 15–49 years, excluding postmenopausal women [44], who also face high CKM risk. Third, environmental, genetic, and psychosocial factors were not examined in detail. Fourth, we assessed key CKM‐related disease outcomes and high‐BMI‐attributable burden, rather than individually defined CKM syndrome or AHA‐defined CKM stages. The reported estimates should, therefore, be interpreted as component‐specific burden estimates rather than the total burden of an integrated CKM syndrome. Because GBD provides aggregate‐level estimates, individual‐level multimorbidity, disease co‐occurrence, synergistic effects among CKM components, and progression across the CKM continuum could not be evaluated. Moreover, high BMI is a GBD risk factor, and its estimates represent attributable health loss rather than a distinct disease burden. Fifth, pregnancy‐specific factors, including gestational diabetes, hypertensive disorders of pregnancy, preeclampsia, parity, and pregnancy outcomes, were unavailable in GBD, although they are highly relevant to CKM risk among WRA. Finally, changes in diagnostic criteria, screening practices, disease awareness, and country‐level data quality may influence temporal trend estimates. Future studies using individual‐level longitudinal data are needed to clarify female‐specific CKM trajectories and multimorbidity patterns.

Overall, WRA experienced a substantial and heterogeneous burden of CKM‐related conditions, with marked regional and SDI disparities. Under historical‐trend assumptions, this burden may persist through 2050. Prevention should prioritize obesity control, early diabetes detection, reproductive‐age cardiometabolic risk screening, and strengthened primary care in low‐ and low–middle‐SDI settings. Future longitudinal studies are needed to clarify female‐specific CKM trajectories and multimorbidity patterns.

Funding

This study was supported by the Noncommunicable Chronic Diseases–National Science and Technology Major Project (2026ZD0556900, 2026ZD0556901, 2023ZD0507100, and 2023ZD0507103); China International Medical Foundation (CIMF; Z‐2017‐26‐2302); Fundamental Research Funds for the Central Universities (YG2025QNA44 and YG2025QNB51); Shanghai Municipal Health Commission Research Program (2025BJPT003); National Natural Science Foundation of China (82170836, 82501389, 82171125, 32270704, and 82371131); Hospital‐level Scientific Research Fund Program of Shanghai Sixth People's Hospital (ynts202404 and hlyjkt202325); Natural Science Foundation of Shanghai Fundamental Research Program (25ZR1402418); Shanghai Hospital Development Center Foundation (SHDC12025112); Shanghai Oriental Talent Program Youth Project (QNZH2024002).

Conflicts of Interest

All authors declare no conflicts of interest.

Supporting information

Supporting Figure 1: Age‐standardized DALYs (A), YLDs (B), and YLLs (C) rates of key CKM components across the 21 GBD regions in 2021.

Supporting Figure 2: Temporal trends in age‐standardized DALYs, YLDs, and YLLs rates of key CKM components from 1990 to 2021 at the global and 5 SDI regions.

Supporting Figure 3: Temporal trends in age‐standardized incidence, death, prevalence rates of key CKM components from 1990 to 2021 at the global and 5 SDI regions.

Supporting Figure 4: Age‐specific (5‐year groups) rates of DALYs (A), YLDs (B), and YLLs (C) for key CKM components among WRA globally in 2021.

Supporting Figure 5: Decomposition analysis of changes in DALYs (A), YLDs (B), and YLLs (C) number for key CKM components globally from 1990 to 2021.

Supporting Table 1: Global burden and trends of cardiovascular‐kidney‐metabolic among women of reproductive age by age group in 2021.

Supporting Table 2: Projected global burden of cardiovascular‐kidney‐metabolic among women of reproductive age from 2022 to 2050.

Supporting Table 3: Retrospective validation of BAPC projections.

JEBM-19-0-s001.docx^{(1.1MB, docx)}

Acknowledgments

We sincerely appreciate the exceptional cooperation of our study team members and gratefully acknowledge the significant contributions of the Global Burden of Disease Study.

Contributor Information

Weijun Huang, Email: hellohuangwj@126.com.

Tianjiao Zhou, Email: g063048@sjtu.edu.cn.

Ji Hu, Email: huji@suda.edu.cn.

Data Availability Statement

The data supporting the findings of this study are available upon reasonable request from the corresponding author.

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