Abstract
Purpose
Diabetes and kidney diseases pose escalating threats to women’s health, yet their combined burden among women aged 15–49 years remains underexplored. This study used data from the Global Burden of Disease 2021 study to quantify trends in type 2 diabetes (T2DM), chronic kidney disease (CKD), and CKD subtypes attributable to hypertension and T2DM across 204 countries and territories from 1990 to 2021, addressing important gaps in metabolic and kidney health research.
Patients and Methods
Using GBD 2021 methodology, we calculated age-standardized incidence rates (ASIRs), Disability-Adjusted Life Years (DALYs), and Estimated Annual Percentage Changes (EAPCs). Analyses were stratified by Socio-Demographic Index (SDI) to dissect socioeconomic disparities.
Results
Global age-standardized DALYs and ASIR for diabetes and kidney diseases increased from 1990 to 2021. T2DM accounted for the greatest burden and showed the most rapid increase. Low-SDI regions experienced the highest disease burden, whereas high-SDI regions exhibited the fastest growth in T2DM. Marked regional disparities were observed, with Oceania and North Africa/Middle East showing particularly high incidence and burden.
Conclusion
The global burden of diabetes and kidney disease among women aged 15–49 years is rapidly increasing, driven predominantly by T2DM. Low-SDI regions face disproportionate burdens, whereas high-SDI areas experience the most rapid growth in T2DM. These findings highlight important socioeconomic inequalities and support the need for context-specific public health strategies, including early screening, metabolic risk reduction, and improved access to diabetes and kidney disease prevention and management services.
Keywords: diabetes and kidney diseases, type 2 diabetes, T2DM, chronic kidney disease, global burden of disease, reproductive age, DALYs
Introduction
Diabetes and kidney disease is a type of kidney disease caused by diabetes. As the most common chronic complication of type 2 diabetes mellitus (T2DM), affect an estimated 20–40% of diabetic patients worldwide.1 Data from the International Diabetes Federation (IDF) shows that the global diabetic population will grow from 537 million in 2021 to 783 million by 2045, with approximately one-third at risk of developing chronic kidney disease (CKD).2 Over the past three decades, the global incidence of CKD has risen significantly, particularly in low- and middle-income countries.3 For example, the CKD prevalence among 15–49-year-old females has reached 3–5% in some regions and continues to increase.4 These trends pose multiple health threats: besides renal failure progression (from microproteinuria to end-stage renal disease),2 affected women face higher risks of cardiovascular disease, anemia, osteoporosis, and a 40–60% elevated likelihood of pregnancy complications.5
In addition, emerging evidence suggests that women experience unique metabolic risk factors across the reproductive life course, including gestational diabetes, polycystic ovary syndrome, adverse pregnancy outcomes, and reproductive hormonal changes, all of which may contribute to a higher long-term risk of T2DM and related kidney complications.6,7 Pregnancy itself imposes a physiological “stress test” on renal function—glomerular hyperfiltration and increased proteinuria may unmask or accelerate subclinical kidney disease, making the reproductive years a critical window for early detection.8 These sex-specific metabolic characteristics highlight the importance of evaluating diabetes and kidney disease burden specifically among women of reproductive age. Conversely, these diseases can also adversely affect women’s preconception health, pregnancy outcomes, and fertility. First, CKD and T2DM impair fertility through multiple pathways: chronic inflammation, hypothalamic-pituitary-ovarian axis dysregulation, contributing to the reproductive disorders.9 Second, among women who conceive, pre-existing T2DM and CKD independently increases the risk of preeclampsia, preterm birth, Caesarean section, small for gestational age infant and infant admission to neonatal intensive care unit.10 Third, and most importantly from a public health perspective, adverse in-utero exposures (maternal hyperglycemia, hypertension, and uremic toxins) can program offspring susceptibility to obesity, insulin resistance, and hypertension via epigenetic mechanisms, perpetuating an intergenerational cycle of cardiometabolic disease.11 These reproductive and transgenerational risks, combined with the scarcity of burden estimates specifically tailored to this demographic, justify our sex- and age-stratified approach.
The dual burden of the disease is notably severe: from a health perspective, women aged 15–49 years constitute a significant portion of the over one million annual deaths worldwide attributed to diabetes-related nephropathy;12 economically, patients in low- and middle-income countries bear an average annual healthcare cost ranging from 30% to 50% of their household incomes, with reproductive-aged women—who in many settings serve as central caregivers and household health decision-makers, facing heightened economic risk.13 While Global Burden of Disease (GBD) studies have highlighted global shifts in CKD burden, they exhibit three primary shortcomings: (1) an absence of systematic analysis for women of reproductive age; (2) overlooking the impact of socioeconomic determinants (such as income and education) on disease distribution; and (3) insufficient differentiation among the epidemiological traits of diabetes-associated nephropathy subtypes.14,15 Moreover, comprehensive burden assessments are essential for identifying high-risk populations, informing healthcare resource allocation, and supporting evidence-based prevention and management strategies across different socioeconomic settings.
We deliberately included both CKD due to T2DM, and CKD due to hypertension—rather than focusing solely on diabetic and kidney disease—for three pragmatic and epidemiological reasons. First, T2DM and hypertension are consistently identified as leading causes of CKD worldwide, yet these two etiologies are frequently siloed in clinical guidelines and surveillance systems. Second, although their pathophysiological mechanisms differ (metabolic vs vascular), they share common upstream modifiable risk factors—including obesity, physical inactivity, and metabolic syndrome—which enables integrated screening strategies during routine preconception or antenatal care visits. Third, the first-line preventive and disease-modifying interventions for both conditions (strict blood pressure control, glycemic management, and renin-angiotensin-aldosterone system blockade) are synergistic rather than mutually exclusive, supporting a unified policy framework for low- and middle-income countries (LMICs) where resource constraints preclude disease-specific vertical programs.
This study utilizes data from GBD 2021 to address existing gaps in the comprehensive analysis of disease burden related to diabetes and kidney diseases among females aged 15–49 years from 1990 to 2021. Unlike previous GBD analyses that primarily focused on the overall population or single disease outcomes, this study specifically examines reproductive-age women, simultaneously evaluates T2DM, CKD, CKD due to T2DM, and CKD due to hypertension, and investigates their burden across 204 countries and territories over a 31-year period. Furthermore, by integrating SDI-stratified analyses, this study provides a more comprehensive understanding of socioeconomic disparities in metabolic and kidney disease burden among women of reproductive age. These novel perspectives may help inform more targeted prevention strategies and health policy development worldwide.
Methods
Data Sources
This study examines trends in the disease burden of diabetes and kidney diseases, including their four primary types, among women aged 15–49 years from 1990 to 2021. Women aged 15–49 years are generally defined as women of reproductive age, and diabetes and kidney diseases during these years may affect both long-term health and pregnancy-related outcomes. Using data from the GBD study, which encompasses 204 countries and territories, 369 diseases and injuries, and 88 risk factors, drawing from published scientific reports, registry and cohort studies, administrative health data, and population surveys;16 the GBD 2021 framework integrates data from multiple sources—including vital registration systems, published scientific literature, registry and cohort studies, administrative health data, and population-based surveys—using standardized modeling approaches to ensure cross-country comparability.
All data were extracted from the publicly accessible GBD Results Tool via the Global Health Data Exchange (GHDx) repository (https://vizhub.healthdata.org/gbd-results/) on 15 February 2026. The following extraction parameters were applied: Location: all 204 countries and territories, grouped into 21 GBD regions and 7 super-regions; Year: 1990–2021 (annual intervals); Age: females aged 15–49 years, stratified into seven 5-year age groups (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49 years); Sex: female; Measures: number of incident cases, number of disability-adjusted life years (DALYs), incidence rate per 100,000 population, and DALY rate per 100,000 population; Metrics: number and rate. All extracted estimates were accompanied by 95% uncertainty intervals (UIs), derived from the 25th and 75th percentiles of the GBD model’s posterior distribution across 1000 draws, representing the range within which the true value lies with 95% probability given the modeling assumptions.
The study was conducted in accordance with the GBD 2021 Guidelines and adhered to the University of Washington’s ethical standards. This analysis used publicly available, de-identified secondary data; therefore, additional ethical approval and informed consent were not required.
Study Population
The study population comprised women aged 15–49 years across all 204 countries and territories included in the GBD 2021 study. This age range corresponds to the conventional reproductive-age window defined by the World Health Organization (WHO) and is widely used in reproductive and maternal health research.
Countries were categorized according to the Socio-demographic Index (SDI), a composite measure of development based on lag-distributed income per capita, average years of education, and total fertility rate among individuals under 25 years of age. The SDI ranges from 0 (lowest development) to 1 (highest development) and was classified into five quintiles for stratified analyses: low, low-middle, middle, high-middle, and high SDI. This stratification enables assessment of socioeconomic disparities in disease burden across regions with varying levels of healthcare infrastructure, screening capacity, and chronic disease management resources.
Disease Case Definitions and GBD Cause Categories
This study focused on four prespecified disease categories within the GBD 2021 cause hierarchy: (1) type 2 diabetes mellitus (T2DM) without kidney involvement, (2) chronic kidney disease (CKD) from all causes, (3) CKD specifically attributable to T2DM, and (4) CKD specifically attributable to hypertensive disorders. These categories were selected to capture both the metabolic (T2DM) and vascular (hypertension) drivers of kidney burden in reproductive-age women, while maintaining etiological specificity.
All disease-specific estimates were extracted using the GBD Results Tool with the following exact cause identifiers. Type 2 diabetes mellitus corresponds to GBD cause ID 647, with ICD-10 codes E11.0–E11.9. Chronic kidney disease (all causes) corresponds to GBD cause ID 592, with ICD-10 codes N18.1–N18.9. CKD due to type 2 diabetes mellitus corresponds to GBD cause ID 649, with ICD-10 codes E11.2 combined with N18.3–N18.5. CKD due to hypertension corresponds to GBD cause ID 650, with ICD-10 codes I12.0, I13.1, and I13.2 combined with N18.3–N18.5. We acknowledge that the GBD framework uses a hierarchical Bayesian meta-regression modeling approach for cause-specific estimation rather than raw ICD code abstraction.
Chronic kidney disease (CKD) is defined by persistent impairment of renal function, clinically evaluated through the estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (ACR). For adults aged 18 years and older, the CKD-EPI 2021 creatinine-based equation is the reference standard for eGFR estimation. For the 15- to 17-year-old subgroup, the Schwartz bedside equation (using height and serum creatinine) is typically recommended in pediatric nephrology guidelines, and the CKD-EPI equation has not been formally validated in this age group. However, for the purpose of this GBD-based analysis, we rely on the GBD 2021 standardized case definitions, which apply a unified modeling framework across all age groups to ensure cross-country comparability, rather than primary clinical eGFR calculations.
Type 2 diabetes mellitus (T2DM) is defined as a metabolic disorder characterized by insulin resistance and relative insulin deficiency, leading to sustained elevations in blood glucose levels. Prolonged hyperglycemia can progressively cause substantial damage to multiple organs and systems, including the kidneys, cardiovascular system, eyes, and nerves.17
Outcome Measures
The following indicators were used to evaluate disease burden in women aged 15–49 years: Incident cases: the number of new cases of each disease occurring during the specified year. Disability-adjusted life years (DALYs): the total health loss caused by a disease, calculated as the sum of years of life lost (YLL) due to premature mortality and years lived with disability (YLD). One DALY represents the loss of one year of healthy life. Crude rate: the number of incident cases or DALYs per 100,000 population in a given year, without age standardization. Age-standardized incidence rate (ASIR): the incidence rate per 100,000 population standardized to the GBD 2021 global standard population age structure, to eliminate the confounding effect of age composition differences across regions and over time. Age-standardized DALY rate: the DALY rate per 100,000 population standardized to the same global standard population. All rates and counts were reported with their corresponding 95% uncertainty intervals (UIs).
Statistical Analysis
To ensure consistent disease estimates, DisMod-MR 2.1 and MR-BRT software were employed, with uncertainty intervals (UIs) derived from the model’s posterior distribution, and variables such as incidence, prevalence, and mortality were expressed as estimates per 100,000 population, along with their 95% UIs, across global, regional, and national scales, while the data were segmented by age (females aged 15–49 years), time (1990–2021), disease (diabetes and kidney disease, CKD and its subtypes), region (7 super-regions), and country (204 countries or territories).
To quantify trends in disease burden from 1990 to 2021, we calculated the estimated annual percentage change (EAPC) for age-standardized rates (ASIR and age-standardized DALY rate). The EAPC was calculated using the following linear regression model: ln(ASR)=α+β×X+ε. where X represents the calendar year and ln(ASR) is the natural logarithm of the age-standardized rate. The EAPC was derived as: EAPC=100×(exp(β)−1). An EAPC value greater than 0 indicates an upward trend in the standardized rate over the study period, whereas a value less than 0 signifies a downward trend. The 95% confidence intervals (CIs) for the EAPC were calculated from the standard error of the regression coefficient β.
All statistical analyses and data visualizations were performed using R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria). The following R packages were used: tidyverse (v2.0.0) for data manipulation and processing. ggplot2 (v3.4.4) for figure generation. gridExtra (v2.3) for multi-panel figure arrangement. boot (v1.3–28.1) for uncertainty interval calculations.
The GBD study was approved by the University of Washington. This study employed publicly available data that did not include confidential or personally identifiable patient information. Because this study was based solely on secondary analyses of publicly available, de-identified data and did not involve direct participation of human subjects, ethical approval and informed consent were not required in accordance with the Measures for Ethical Review of Life Science and Medical Research Involving Human Subjects (2023, China), Article 32.
Results
Overall, the burden of diabetes and kidney diseases among women aged 15–49 years increased substantially from 1990 to 2021, with T2DM emerging as the primary contributor to this increase. Clear disparities were observed across SDI regions, with low-SDI regions bearing the highest burden, whereas high-SDI regions experienced the most rapid growth. At the regional level, Oceania consistently exhibited the highest disease burden, while North Africa and the Middle East showed the fastest increases in incidence. These findings reveal substantial geographic and socioeconomic inequalities and identify important hotspot regions that warrant targeted prevention and intervention strategies.
Trends in Global Distribution
From 1990 to 2021, the global disease burden of diabetes and kidney disease among women aged 15–49 years increased substantially: during this period, the total number of DALYs escalated by 2.16 times, from 6.33 million (95% UI: 5.597 million - 7.177 million) to 12.710 million (95% UI: 10.895 million - 14.995 million), while the age-standardized DALY rate rose from 492.16 per 100,000 (95% UI: 491.76–492.56) to 618.41 per 100,000 (95% UI: 618.06–618.75), with an EAPC of 0.66 (95% CI: 0.57–0.75); furthermore, the total number of incident cases increased 1.80-fold, from 196.70 million (95% UI: 179.55–217.78 million) to 353.39 million (95% UI: 321.97–390.03 million), and the ASIR also saw a significant jump from 212.64/100,000 to 345.89/100,000 with an EAPC of 1.50 (95% CI: 1.45–1.54). In conclusion, the burden of diabetes and kidney disease, as measured by both DALYs and incidence rate, continues to rise, particularly among the young female demographic.
In 2021, among disease subtypes, T2DM contributed the highest age-standardized DALY rate at 329.93 per 100,000 (95% UI: 329.68–330.18), while CKD due to T2DM showed the lowest rate at 16.61 per 100,000 (95% UI: 16.56–16.67). The fastest growth in DALYs was observed in T2DM ([EAPC] = 1.83), whereas CKD (EAPC = −0.14) and CKD due to hypertension (EAPC = −0.10) exhibited decreasing trends. In terms of incidence, T2DM had the highest incidence rate (267.78 per 100,000) and growth rate (EAPC = 1.95), with no observed decline in incidence for any subtype. Notably, the burden of T2DM increased significantly in both age-standardized DALYs and ASIR, while certain CKD subtypes showed decreasing or slow-growing trends (Tables 1, 2 and Figure 1, SFigure 1).
Table 1.
Global Age-Standardized DALYs Rate and Case Number of Diabetes and Kidney Diseases of Female Aged 15–49 Years, and Their Trends from 1990 to 2021
| Location | DALYs Case | DALYs Rates | |||||
|---|---|---|---|---|---|---|---|
| 1990_Millions (95% UI) | 2021_Millions (95% UI) | EAPC No. (95% CI) | 1990_per 100,000 (95% UI) | 2021_per 100,000 (95% UI) | EAPC No. (95% CI) | ||
| Diabetes and kidney diseases | Global | 6,332,886.58 (5,597,121.83, 7,176,631.88) | 12,709,881.17 (10,895,232.42, 14,995,053.54) | 2.16 (2.08, 2.25) | 492.16 (491.76, 492.56) | 618.41 (618.06, 618.75) | 0.66 (0.57, 0.75) |
| Diabetes and kidney diseases | High-middle SDI | 1,016,456.57 (864,941.00, 1,194,669.63) | 1,487,711.40 (1,165,969.50, 1,881,609.05) | 1.02 (0.80, 1.24) | 376.32 (375.58, 377.07) | 453.88 (453.14, 454.63) | 0.52 (0.37, 0.67) |
| Diabetes and kidney diseases | High SDI | 650,246.79 (553,142.61, 765,766.19) | 1,187,112.90 (947,590.62, 1,477,223.07) | 1.90 (1.81, 1.99) | 278.03 (277.35, 278.71) | 433.43 (432.63, 434.22) | 1.51 (1.39, 1.63) |
| Diabetes and kidney diseases | Low-middle SDI | 1,521,136.14 (1,329,298.80, 1,728,623.61) | 3,742,684.16 (3,211,305.38, 4,391,278.53) | 2.89 (2.85, 2.94) | 604.83 (603.81, 605.85) | 727.50 (726.74, 728.26) | 0.62 (0.50, 0.73) |
| Diabetes and kidney diseases | Low SDI | 684,096.56 (592,546.25, 779,422.51) | 1,814,169.26 (1,567,113.74, 2,136,588.79) | 3.02 (2.91, 3.13) | 695.58 (693.84, 697.31) | 742.17 (741.04, 743.30) | 0.13 (0.04, 0.21) |
| Diabetes and kidney diseases | Middle SDI | 2,453,731.78 (2,162,117.81, 2,790,085.27) | 4,464,847.10 (3,827,661.51, 5,234,276.47) | 1.86 (1.73, 1.98) | 598.29 (597.49, 599.10) | 677.72 (677.09, 678.35) | 0.27 (0.17, 0.37) |
| Diabetes mellitus type 2 | Global | 2,457,514.22 (2,001,624.89, 3,015,711.23) | 6,895,581.46 (5,283,657.23, 8,797,870.44) | 3.30 (3.21, 3.38) | 191.78 (191.53, 192.02) | 329.93 (329.68, 330.18) | 1.83 (1.68, 1.98) |
| Diabetes mellitus type 2 | High-middle SDI | 382,333.80 (286,140.45, 503,476.53) | 946,446.10 (666,664.64, 1,281,984.50) | 2.91 (2.64, 3.17) | 143.57 (143.11, 144.04) | 285.31 (284.73, 285.90) | 2.41 (2.23, 2.59) |
| Diabetes mellitus type 2 | High SDI | 290,550.09 (226,140.99, 364,623.04) | 698,708.96 (498,898.65, 931,651.51) | 2.72 (2.62, 2.82) | 123.31 (122.86, 123.77) | 249.70 (249.11, 250.30) | 2.32 (2.19, 2.44) |
| Diabetes mellitus type 2 | Low-middle SDI | 596,086.91 (500,051.37, 708,852.06) | 1,955,083.60 (1,552,984.49, 2,463,337.49) | 3.86 (3.82, 3.90) | 234.20 (233.57, 234.83) | 372.62 (372.08, 373.16) | 1.68 (1.44, 1.91) |
| Diabetes mellitus type 2 | Low SDI | 267,994.36 (224,917.26, 316,459.63) | 887,866.17 (718,351.16, 1,105,441.03) | 3.77 (3.67, 3.87) | 280.50 (279.40, 281.61) | 365.03 (364.24, 365.83) | 1.13 (0.92, 1.34) |
| Diabetes mellitus type 2 | Middle SDI | 917,167.16 (738,434.68, 1,130,079.77) | 2,399,669.33 (1,849,246.06, 3,043,255.22) | 3.07 (2.94, 3.20) | 214.41 (213.94, 214.88) | 358.79 (358.33, 359.24) | 1.56 (1.40, 1.72) |
| Chronic kidney disease | Global | 3,083,465.18 (2,744,949.67, 3,471,768.28) | 4,850,378.19 (4,331,667.25, 5,445,352.20) | 1.36 (1.25, 1.47) | 239.83 (239.55, 240.11) | 239.63 (239.42, 239.85) | −0.14 (−0.20, −0.08) |
| Chronic kidney disease | High-middle SDI | 483,393.15 (422,658.93, 553,627.33) | 430,778.14 (366,997.38, 503,334.39) | −0.62 (−0.91, −0.33) | 177.92 (177.41, 178.43) | 132.53 (132.13, 132.94) | −1.11 (−1.23, −0.98) |
| Chronic kidney disease | High SDI | 238,281.44 (202,315.79, 279,911.51) | 364,015.33 (312,836.00, 422,762.38) | 1.51 (1.40, 1.62) | 102.39 (101.97, 102.80) | 134.22 (133.77, 134.66) | 1.13 (1.00, 1.26) |
| Chronic kidney disease | Low-middle SDI | 737,025.60 (646,716.09, 868,346.77) | 1,481,074.44 (1,303,839.83, 1,687,086.42) | 2.23 (2.14, 2.33) | 295.54 (294.83, 296.26) | 293.70 (293.21, 294.19) | −0.07 (−0.14, 0.00) |
| Chronic kidney disease | Low SDI | 348,037.39 (302,829.49, 399,789.25) | 789,799.89 (673,263.77, 920,332.36) | 2.49 (2.36, 2.61) | 348.13 (346.91, 349.35) | 320.98 (320.24, 321.71) | −0.55 (−0.67, −0.43) |
| Chronic kidney disease | Middle SDI | 1,273,857.38 (1,122,960.01, 1,426,787.93) | 1,780,215.79 (1,574,393.00, 1,988,015.08) | 0.98 (0.82, 1.13) | 317.16 (316.56, 317.75) | 273.68 (273.27, 274.08) | −0.61 (−0.69, −0.53) |
| Chronic kidney disease due to diabetes mellitus type 2 | Global | 218,013.65 (154,584.62, 300,662.08) | 366,969.35 (257,400.71, 511,942.05) | 1.66 (1.54, 1.79) | 16.88 (16.81, 16.96) | 16.61 (16.56, 16.67) | 0.18 (−0.08, 0.44) |
| Chronic kidney disease due to diabetes mellitus type 2 | High-middle SDI | 38,346.77 (26,700.72, 53,269.65) | 43,680.55 (30,608.59, 61,102.66) | 0.22 (−0.18, 0.62) | 14.26 (14.11, 14.40) | 12.26 (12.14, 12.37) | −0.40 (−0.58, −0.21) |
| Chronic kidney disease due to diabetes mellitus type 2 | High SDI | 22,429.78 (16,676.88, 29,398.77) | 43,420.70 (32,749.30, 56,081.96) | 2.53 (2.37, 2.70) | 9.31 (9.19, 9.44) | 14.21 (14.07, 14.34) | 1.99 (1.78, 2.21) |
| Chronic kidney disease due to diabetes mellitus type 2 | Low-middle SDI | 41,301.83 (28,022.47, 57,374.95) | 91,162.64 (61,057.39, 130,685.98) | 2.67 (2.60, 2.73) | 15.73 (15.57, 15.89) | 16.29 (16.19, 16.40) | 0.70 (0.23, 1.17) |
| Chronic kidney disease due to diabetes mellitus type 2 | Low SDI | 16,559.34 (11,301.85, 23,232.26) | 35,121.78 (23,411.53, 51,689.34) | 2.21 (2.00, 2.42) | 17.93 (17.65, 18.21) | 15.29 (15.13, 15.46) | 0.28 (−0.41, 0.98) |
| Chronic kidney disease due to diabetes mellitus type 2 | Middle SDI | 99,204.88 (68,397.67, 139,629.84) | 153,226.11 (106,786.10, 218,270.49) | 1.37 (1.24, 1.51) | 22.59 (22.44, 22.74) | 21.84 (21.74, 21.96) | −0.08 (−0.38, 0.23) |
| Chronic kidney disease due to hypertension | Global | 568,906.46 (455,182.33, 716,391.55) | 912,371.86 (707,119.43, 1,178,875.53) | 1.40 (1.27, 1.54) | 43.71 (43.59, 43.83) | 44.36 (44.27, 44.45) | −0.10 (−0.17, −0.02) |
| Chronic kidney disease due to hypertension | High-middle SDI | 75,423.47 (59,759.61, 96,450.05) | 75,762.13 (57,733.97, 96,890.78) | −0.16 (−0.44, 0.12) | 27.78 (27.58, 27.98) | 23.10 (22.93, 23.27) | −0.67 (−0.77, −0.56) |
| Chronic kidney disease due to hypertension | High SDI | 32,615.60 (25,665.45, 40,832.14) | 62,586.99 (50,681.57, 77,073.21) | 2.32 (2.19, 2.46) | 13.93 (13.78, 14.09) | 22.44 (22.27, 22.62) | 1.90 (1.73, 2.06) |
| Chronic kidney disease due to hypertension | Low-middle SDI | 145,433.98 (112,318.27, 190,390.11) | 270,498.03 (207,868.58, 357,315.61) | 1.89 (1.76, 2.02) | 57.88 (57.56, 58.19) | 52.78 (52.58, 52.99) | −0.38 (−0.49, −0.27) |
| Chronic kidney disease due to hypertension | Low SDI | 45,744.13 (34,054.10, 60,393.32) | 106,102.85 (78,179.37, 141,094.68) | 2.59 (2.46, 2.73) | 45.02 (44.59, 45.45) | 42.70 (42.43, 42.97) | 0.03 (−0.13, 0.19) |
| Chronic kidney disease due to hypertension | Middle SDI | 269,181.43 (215,173.58, 339,362.47) | 396,565.17 (308,297.68, 508,145.04) | 1.12 (0.95, 1.28) | 65.85 (65.58, 66.12) | 60.55 (60.36, 60.74) | −0.47 (−0.57, −0.37) |
Table 2.
Global Age-Standardized Incidence Rate and Case Number of Diabetes and Kidney Diseases of Female Aged 15–49 Years, and Their Trends from 1990 to 2021
| Location | Incidence Case | Incidence Rates | |||||
|---|---|---|---|---|---|---|---|
| 1990_Millions (95% UI) | 2021_Millions (95% UI) | EAPC No. (95% CI) | 1990_per 100,000 (95% UI) | 2021_per 100,000 (95% UI) | EAPC No. (95% CI) | ||
| Diabetes and kidney diseases | Global | 196.70 (179.55, 217.78) | 353.39 (321.97, 390.03) | 1.83 (1.79, 1.86) | 212.64 (212.38, 212.90) | 345.89 (345.63, 346.15) | 1.50 (1.45, 1.54) |
| Diabetes and kidney diseases | High-middle SDI | 195.58 (177.03, 217.32) | 344.13 (311.90, 383.69) | 1.78 (1.70, 1.86) | 206.18 (205.63, 206.73) | 320.61 (319.97, 321.24) | 1.37 (1.29, 1.44) |
| Diabetes and kidney diseases | High SDI | 184.14 (167.17, 204.79) | 370.32 (338.52, 407.97) | 2.16 (2.08, 2.24) | 179.38 (178.84, 179.93) | 335.47 (334.77, 336.18) | 2.00 (1.92, 2.08) |
| Diabetes and kidney diseases | Low-middle SDI | 190.12 (173.81, 208.74) | 364.89 (333.43, 400.69) | 2.06 (2.01, 2.11) | 212.99 (212.40, 213.58) | 379.09 (378.54, 379.63) | 1.79 (1.75, 1.83) |
| Diabetes and kidney diseases | Low SDI | 150.57 (136.85, 165.77) | 257.81 (235.41, 283.81) | 1.71 (1.63, 1.78) | 170.31 (169.47, 171.15) | 284.76 (284.08, 285.45) | 1.62 (1.56, 1.67) |
| Diabetes and kidney diseases | Middle SDI | 219.26 (199.21, 241.87) | 384.04 (349.81, 426.38) | 1.73 (1.69, 1.77) | 245.48 (244.98, 245.98) | 367.54 (367.06, 368.01) | 1.17 (1.11, 1.22) |
| Diabetes mellitus type 2 | Global | 132.17 (115.39, 149.92) | 272.87 (242.83, 307.43) | 2.26 (2.21, 2.31) | 142.09 (141.88, 142.30) | 267.78 (267.55, 268.01) | 1.95 (1.91, 1.99) |
| Diabetes mellitus type 2 | High-middle SDI | 129.60 (111.55, 149.94) | 261.94 (228.60, 300.14) | 2.22 (2.11, 2.33) | 136.33 (135.88, 136.78) | 249.81 (249.24, 250.37) | 1.88 (1.81, 1.96) |
| Diabetes mellitus type 2 | High SDI | 123.71 (108.38, 140.55) | 294.61 (262.51, 329.87) | 2.75 (2.68, 2.82) | 120.50 (120.05, 120.95) | 269.37 (268.73, 270.01) | 2.62 (2.55, 2.69) |
| Diabetes mellitus type 2 | Low-middle SDI | 125.88 (110.10, 142.66) | 285.16 (251.83, 321.09) | 2.60 (2.56, 2.63) | 139.64 (139.17, 140.12) | 295.27 (294.79, 295.76) | 2.35 (2.32, 2.39) |
| Diabetes mellitus type 2 | Low SDI | 100.60 (87.62, 114.03) | 200.58 (177.12, 225.50) | 2.20 (2.16, 2.24) | 112.74 (112.07, 113.42) | 219.10 (218.50, 219.69) | 2.10 (2.07, 2.13) |
| Diabetes mellitus type 2 | Middle SDI | 149.71 (130.58, 170.33) | 291.49 (258.98, 330.85) | 2.01 (1.94, 2.07) | 166.50 (166.09, 166.90) | 280.70 (280.29, 281.11) | 1.48 (1.42, 1.54) |
| Chronic kidney disease | Global | 48.96 (40.05, 58.45) | 69.51 (58.67, 81.32) | 1.12 (1.09, 1.15) | 55.05 (54.92, 55.19) | 67.01 (66.89, 67.12) | 0.63 (0.59, 0.66) |
| Chronic kidney disease | High-middle SDI | 44.46 (35.72, 53.64) | 70.94 (58.81, 83.84) | 1.48 (1.40, 1.56) | 48.13 (47.86, 48.40) | 59.08 (58.82, 59.33) | 0.67 (0.62, 0.73) |
| Chronic kidney disease | High SDI | 48.74 (38.52, 61.40) | 62.56 (51.68, 74.53) | 0.58 (0.41, 0.76) | 46.95 (46.67, 47.23) | 52.46 (52.19, 52.73) | 0.25 (0.13, 0.37) |
| Chronic kidney disease | Low-middle SDI | 53.93 (44.39, 63.63) | 69.12 (57.60, 80.39) | 0.75 (0.65, 0.85) | 63.44 (63.11, 63.77) | 73.32 (73.07, 73.56) | 0.39 (0.30, 0.48) |
| Chronic kidney disease | Low SDI | 40.36 (33.92, 47.83) | 47.27 (39.66, 54.76) | 0.48 (0.34, 0.62) | 48.26 (47.80, 48.72) | 56.01 (55.70, 56.32) | 0.44 (0.32, 0.56) |
| Chronic kidney disease | Middle SDI | 50.99 (41.88, 60.81) | 81.67 (69.91, 94.90) | 1.61 (1.57, 1.65) | 60.48 (60.23, 60.74) | 75.71 (75.50, 75.92) | 0.78 (0.74, 0.83) |
| Chronic kidney disease due to diabetes mellitus type 2 | Global | 2.38 (1.88, 3.01) | 3.31 (2.69, 3.94) | 1.01 (0.97, 1.06) | 2.83 (2.80, 2.87) | 3.13 (3.11, 3.16) | 0.27 (0.21, 0.34) |
| Chronic kidney disease due to diabetes mellitus type 2 | High-middle SDI | 2.20 (1.72, 2.76) | 3.57 (2.82, 4.36) | 1.48 (1.39, 1.57) | 2.49 (2.43, 2.55) | 2.76 (2.71, 2.81) | 0.29 (0.22, 0.35) |
| Chronic kidney disease due to diabetes mellitus type 2 | High SDI | 2.49 (1.89, 3.23) | 3.20 (2.52, 3.93) | 0.54 (0.35, 0.73) | 2.40 (2.33, 2.46) | 2.55 (2.49, 2.61) | 0.06 (−0.08, 0.19) |
| Chronic kidney disease due to diabetes mellitus type 2 | Low-middle SDI | 2.57 (2.02, 3.18) | 3.12 (2.48, 3.79) | 0.54 (0.40, 0.68) | 3.26 (3.19, 3.34) | 3.41 (3.36, 3.47) | 0.01 (−0.11, 0.13) |
| Chronic kidney disease due to diabetes mellitus type 2 | Low SDI | 1.84 (1.44, 2.31) | 1.96 (1.55, 2.37) | 0.14 (−0.06, 0.34) | 2.43 (2.32, 2.54) | 2.57 (2.50, 2.64) | 0.11 (−0.05, 0.27) |
| Chronic kidney disease due to diabetes mellitus type 2 | Middle SDI | 2.47 (1.95, 3.08) | 3.97 (3.24, 4.74) | 1.62 (1.55, 1.69) | 3.18 (3.12, 3.24) | 3.55 (3.51, 3.59) | 0.36 (0.28, 0.45) |
| Chronic kidney disease due to hypertension | Global | 1.70 (1.38, 2.06) | 2.50 (2.12, 2.93) | 1.23 (1.20, 1.26) | 1.96 (1.93, 1.99) | 2.39 (2.37, 2.41) | 0.64 (0.60, 0.67) |
| Chronic kidney disease due to hypertension | High-middle SDI | 1.55 (1.24, 1.88) | 2.62 (2.13, 3.18) | 1.65 (1.56, 1.74) | 1.71 (1.66, 1.76) | 2.12 (2.07, 2.17) | 0.70 (0.64, 0.76) |
| Chronic kidney disease due to hypertension | High SDI | 1.80 (1.38, 2.34) | 2.34 (1.90, 2.85) | 0.62 (0.43, 0.80) | 1.73 (1.68, 1.78) | 1.93 (1.88, 1.98) | 0.24 (0.11, 0.36) |
| Chronic kidney disease due to hypertension | Low-middle SDI | 1.85 (1.52, 2.24) | 2.43 (2.03, 2.90) | 0.84 (0.74, 0.93) | 2.24 (2.18, 2.31) | 2.61 (2.56, 2.65) | 0.40 (0.31, 0.49) |
| Chronic kidney disease due to hypertension | Low SDI | 1.36 (1.09, 1.68) | 1.61 (1.31, 1.94) | 0.53 (0.38, 0.68) | 1.69 (1.61, 1.78) | 1.98 (1.92, 2.04) | 0.47 (0.35, 0.59) |
| Chronic kidney disease due to hypertension | Middle SDI | 1.75 (1.41, 2.09) | 2.95 (2.51, 3.44) | 1.80 (1.76, 1.84) | 2.14 (2.10, 2.19) | 2.70 (2.66, 2.74) | 0.80 (0.75, 0.85) |
Figure 1.
Global age-standardized DALYs rate and case number of diabetes and kidney diseases and their subtypes of female aged 15–49 years in 2021. (A) Diabetes and kidney diseases. (B) Diabetes mellitus type 2. (C) Chronic kidney disease. (D) Chronic kidney disease due to diabetes mellitus type 2. (E) Chronic kidney disease due to hypertension.
SDI Regional Differences and Differentiation
The disease burden differed markedly across SDI regions: in 2021, the age-standardized DALY rate was highest in low SDI regions at 742.17 per 100,000 (95% UI: 741.04–743.30) but grew fastest in high SDI regions (EAPC = 1.51, 95% CI: 1.39–1.63); the ASIR was highest in low-middle SDI regions at 379.09 per 100,000 (95% UI: 378.54–379.63) yet saw the highest growth rate in high SDI regions (EAPC = 2.00, 95% CI: 1.92–2.08). Subtype analysis indicated that low SDI regions had the greatest age-standardized DALY rates for T2DM at 365.03 per 100,000 (95% UI: 364.24–365.83) and CKD at 320.98 per 100,000 (95% UI: 320.24–321.71), whereas intermediate SDI regions had the highest DALYs rates for diabetic nephropathy (21.84/100,000) and CKD due to hypertension (60.55/100,000). Growth patterns varied: T2DM increased most rapidly in high intermediate SDI areas (EAPC = 2.41), diabetic nephropathy and CKD due to hypertension grew significantly in high SDI areas (EAPC = 1.99 and 1.90, respectively), while the CKD burden decreased in high intermediate SDI areas (EAPC = −1.11), suggesting potential medical intervention impacts. For ASIR, medium SDI regions had the highest rates for all four diseases, notably T2DM (280.70/100,000) and CKD (73.32/100,000), while T2DM ASIR in high SDI regions increased at a rate of 2.62, highlighting the ongoing challenge of metabolic disease management (Tables 1, 2 and Figure 1, SFigure 1).
Trends in Country Distribution
In the 204-country cohort of females aged 15–49 years assessed in 2021, notable regional disparities were observed in indicators related to diabetes and kidney disease. The Marshall Islands was identified as having the highest rates of diabetes and kidney disease, with age-standardized DALYs rate and ASIRs for type 2 diabetes (T2DM) reaching 3920.99, 2824.16, 1523.75, and 1373.74, respectively. In the context of CKD and its subcategories, American Samoa and Mauritius stood out, ranking first in CKD DALYs (1096.94), CKD attributable to T2DM DALY (194.64), and CKD due to hypertension DALY (324.49), respectively.
Upon examining the EAPC trends from 1990 to 2021, it is evident that the most pronounced increases are predominantly in developing nations and regions, with Lesotho notably exhibiting the highest growth rates in diabetes and kidney disease DALY (4.33), T2DM DALY (4.37), and CKD due to hypertension DALY (4.61), while Zimbabwe and Ukraine also demonstrate considerable growth across various categories; in contrast, the countries with the most marked reductions were primarily in Sub-Saharan Africa and Asia, such as Rwanda and Ethiopia, which both recorded negative growth in numerous indicators, especially in Diabetes and Kidney Disease DALY (−2.96) and CKD DALY (−3.36). In general, there is an upward trajectory of disease burden in high-income nations and certain developing regions, whereas Sub-Saharan Africa and some Asian countries indicate a downward trend, underscoring the intricate and diverse nature of global disease patterns (Tables S1–S11 and Figure 2, SFigures 2, 3).
Figure 2.
Age-standardized DALYs rate of diabetes and kidney diseases and their subtypes of female aged 15–49 years, and their trends from 1990 to 2021. (A) Diabetes and kidney diseases. (B) Diabetes mellitus type 2. (C) Chronic kidney disease. (D) Chronic kidney disease due to diabetes mellitus type 2. (E) Chronic kidney disease due to hypertension.
Trends in Regional Distribution
The tabular data from 2021 indicates significant regional variations in the performance of the 15–49-year-old female cohort across 21 districts on indicators related to diabetes, kidney disease, and nephropathy: Oceania recorded the highest age-standardized DALYs rate and ASIR for diabetes and kidney disease, T2DM, and CKD due to T2DM, with respective figures of 1522.48, 1120.62, 743.04, and 659.01; in relation to CKD and its subcategories, Central Latin America and Southeast Asia performed exceptionally well, occupying the top positions in CKD DALY (443.68) and CKD due to hypertension DALY (157.99), respectively; and with regards to ASIR, North Africa and the Middle East were ranked highest in several categories.
Upon examining the changes in EAPC between 1990 and 2021, we observe that the regions experiencing the most rapid increases are predominantly high-income and developing areas: specifically, High-income North America exhibits the most pronounced growth rates in diabetes and kidney disease, T2DM, and CKD DALY, with values of 1.70, 2.83, and 2.12, respectively; in contrast, North Africa and the Middle East are notable for their significant increase in incidence, particularly for T2DM (3.41); conversely, the regions witnessing the most substantial declines are primarily in Sub-Saharan Africa and East Asia, with Eastern Sub-Saharan Africa and East Asia, for instance, demonstrating negative growth in various indicators such as diabetes and kidney disease DALY (−1.01) and CKD DALY (−2.13). In general, while high-income regions display an upward trajectory, certain developing regions indicate a downward trend, highlighting marked disparities in the evolving disease burden across different regions (Table S12 and Figure 3).
Figure 3.
Age-standardized rate of diabetes and kidney diseases and their subtypes of female aged 15–49 years, and their trends from 1990 to 2021. (A) Age-standardized DALYs rate. (B) Age-standardized incidence rate.
Discussion
From 1990 to 2021, there was a marked rise in the GBD for diabetes and kidney disease among females aged 15–49 years, characterized by a consistent increase in both DALYs and incidence rate; as per the 2021 data, T2DM recorded the highest age-standardized DALYs rate and ASIR at 329.93 and 267.78 respectively (per 100,000 population), while CKD resulting from T2DM interestingly had the least burden. An examination of different SDI regions revealed that those with a low SDI had the most significant burden in terms of DALYs rates and incidence, whereas high SDI regions exhibited the most rapid increases despite their currently lower burdens, underscoring the growing disease burden in high-income countries. Within the 21 analyzed regions, Oceania had the most pronounced rates and incidence of DALYs related to diabetes and kidney disease, in contrast to Central Latin America and Southeast Asia, which bore a higher burden of CKD and its subcategories. At the country level, developing nations such as the Marshall Islands, American Samoa, and Mauritius ranked highest in several disease indicators, while sub-Saharan Africa and some East Asian countries like Rwanda and Ethiopia demonstrated a notable decline. In summary, while high-income regions see an escalating disease burden, low-income areas continue to grapple with a severe burden due to limited healthcare resources. This depiction of global disease patterns emphasizes the need to: (i) integrate diabetes and CKD screening into existing maternal and reproductive health platforms, particularly antenatal and postpartum care; (ii) deploy point-of-care HbA1c and urine ACR testing to overcome laboratory infrastructure barriers in low-resource settings; and (iii) prioritize risk-stratified screening targeting women with obesity, prior GDM, or family history of diabetes.
The increasing incidence rate of T2DM among women aged 15–49 years between 1990 and 2021 may be partially explained by the concurrent rise in obesity and metabolic syndrome prevalence, although this ecological association does not establish causality at the individual level, with factors such as high-calorie diets, sedentary lifestyles, and delayed childbearing contributing to heightened insulin resistance,18 polycystic ovary syndrome has been associated with elevated risk in prior clinical studies, although our ecological data cannot confirm a causal link;19 In addition, gestational diabetes mellitus (GDM), a common pregnancy-related metabolic disorder, has been associated with a substantially increased risk of subsequent T2DM and CKD later in life. Given the reproductive-age focus of this study, the growing prevalence of GDM worldwide may represent an important contributor to the rising burden of diabetes and kidney diseases among women. On the other hand, the relatively lower burden of diabetic nephropathy could reflect its typically slow progression and possibly improvements in early detection, but alternative explanations—including underdiagnosis in LMICs and GBD modeling assumptions—cannot be excluded.20 Although advancements in healthcare have led to higher diabetes detection rates,21 the limited screening and management of gestational diabetes in underprivileged regions continue to fuel disease advancement,22 while the burden of CKD remains relatively low—possibly due to the use of renin-angiotensin system inhibitors and better glycemic control23—the absolute number of cases is still rising due to population growth and aging. To counter these trends, we recommend: (i) universal screening for GDM between 24–28 weeks of gestation using oral glucose tolerance testing, with postpartum re-assessment at 6–12 weeks to identify women at risk of progressing to T2DM; (ii) early initiation of RAAS blockers (eg, enalapril, losartan) in women with persistent microalbuminuria to delay CKD progression; and (iii) lifestyle intervention programs—dietary modification and structured physical activity—delivered through community health workers for women with prior GDM or obesity.
The disease burden exhibits considerable regional heterogeneity: for instance, in low SDI regions such as sub-Saharan Africa, high rates of diabetes and kidney disease are prevalent due to rapid urbanization, widespread unhealthy diets, and the concurrent existence of malnutrition and obesity,24 with this burden further amplified by the scarcity of healthcare resources and insufficient capacity for chronic disease screening; conversely, in high SDI regions, the current burden may be low, yet increasing obesity rates, aging populations, and sedentary lifestyles are leading to a swift escalation of diseases;25 when examining specific regions, Oceania, particularly Pacific Island countries, has the highest global burden due to genetic predispositions, adoption of westernized diets, and elevated obesity rates,26 Central Latin America and Southeast Asia showed particularly high CKD burden, with age-standardized DALY rates of 443.68 per 100,000 and 431.44 per 100,000, respectively, in 2021 (Table S12 and Figure 3C) — substantially exceeding the global CKD average of 239.63 per 100,000 (Table 1). This elevated burden may reflect a combination of uncontrolled hypertension, exposure to environmental toxins such as pesticide contamination, and infection-related nephropathies, although these hypothesized drivers cannot be confirmed in our ecological analysis and require individual-level investigation.27 These disparities underscore the profound influence of socioeconomic factors, lifestyle choices, and healthcare equity on disease prevention and management. Based on these regional patterns, we propose differentiated strategies: (i) in Oceania and Pacific Islands, community-based lifestyle interventions leveraging local food systems and cultural practices should be prioritized to address dietary transitions and obesity; (ii) in North Africa and the Middle East, where incidence is rising most rapidly, health systems should focus on early diagnosis and treatment to prevent progression from diabetes to CKD, alongside public awareness campaigns; (iii) in Central Latin America and Southeast Asia, where hypertension-related CKD burden is particularly high, blood pressure screening and control should be the primary focus, with emphasis on affordable antihypertensive medications and salt-reduction strategies.
In terms of the changes in EAPC from 1990 to 2021, developing countries (eg, Lesotho, Zimbabwe, and Ukraine) have the highest increasing trend, mainly related to rapid urbanization, unhealthy lifestyles, and aging populations in these countries; for instance, many low-income countries in sub-Saharan Africa are experiencing a fast socioeconomic transition characterized by a diet high in processed foods, declining physical activity, and increasing obesity—all of which contribute to the increase in diabetes and kidney disease28,29—and moreover, the shortage of medical resources and weak health systems hinder their ability to respond to the surge in chronic diseases. On the other hand, China demonstrated a substantial decline in age-standardized CKD DALY rate from 260.29 per 100,000 (95% UI: 259.67–260.91) in 1990 to 135.28 per 100,000 (95% UI: 134.87–135.68) in 2021, corresponding to an EAPC of −2.38 (95% CI: −2.55 to −2.21) (Table S3 and Figure 2C). Similarly, Rwanda and Ethiopia showed declining trends for diabetes and kidney disease DALY rates, with EAPCs of −2.96 (95% CI: −3.36 to −2.55) and −2.86 (95% CI: −3.13 to −2.59), respectively (Table S1). These downward trends may reflect the impact of sustained public health investments, although this descriptive analysis cannot establish a causal link between specific interventions and the observed changes, such as infectious disease control, nutrition improvement programs, and health education promotion,30,31 though this decreasing trend could also be due to problems in data collection or surveillance systems for chronic diseases. In general, high-income regions showed the most rapid growth in T2DM ASIR, with an EAPC of 2.62 (95% CI: 2.55–2.69) (Table 2), despite their currently lower age-standardized rates compared with low-SDI regions. This paradoxical pattern—low current burden but rapid growth—suggests that rising obesity rates, sedentary lifestyles, and population aging may be driving new incident cases (Table 2 and Figure 1B), although the ecological design of our study precludes causal attribution of these associations, while the downward trend in sub-Saharan Africa and some Asian countries could reflect the effectiveness of public health interventions—with awareness of potential deficiencies in data quality32—and the diversity and complexity of global disease patterns indicate the necessity of tailoring chronic disease prevention and control strategies to local needs and situations, as well as reinforcing global chronic disease surveillance and research. Specifically, we recommend: (i) adopting population-level measures in high-income regions—including sugar-sweetened beverage taxation, front-of-package nutritional labeling, and workplace wellness programs—to halt obesity-driven T2DM growth; (ii) studying and adapting successful exemplars from China and Rwanda to inform policy transfer to other LMICs through South-South technical cooperation; and (iii) strengthening chronic disease surveillance systems in LMICs to improve data quality and enable accurate monitoring of intervention impact.
All interpretations presented in this discussion are hypothesis-generating rather than confirmatory. Given the ecological and descriptive nature of GBD-based analyses, the associations we observe between risk factors and disease burden trends should be interpreted as potential explanations warranting further investigation, rather than as established causal relationships.
Based on data from the GBD, this study offers an exhaustive analysis spanning 204 countries and territories, employing multidimensional indicators to elucidate long-term trends and disparities in diabetes and kidney disease across various regions and socio-economic strata, which provides a crucial perspective for understanding the global burden of chronic disease; nevertheless, the study has substantial limitations: first, changes in GBD estimates over the 1990–2021 period may reflect not only true epidemiological changes in disease occurrence but also temporal and cross-country variations in data availability, data quality, disease coding practices, surveillance system coverage, and the underlying GBD modeling methodology (eg, updates in DisMod-MR 2.1 and MR-BRT algorithms across GBD rounds). These factors are particularly consequential in low-income countries, where vital registration systems are often incomplete and where improvements in surveillance over time may artifactually inflate the apparent disease burden independent of true epidemiological trends. Therefore, our reported EAPCs should be interpreted as composite metrics reflecting a combination of true disease dynamics and measurement system evolution, rather than as pure estimates of epidemiological change; secondly, the studies relied on population-level ecological designs and did not conduct individual-level causality analyses, hindering the identification of specific drivers;4,33,34 furthermore, the studies did not sufficiently account for potential confounders such as genetic susceptibility, exposure to environmental pollutants, and healthcare resource allocation, which could impact the accuracy of the results; the heterogeneity of chronic disease surveillance systems across different countries (eg, variations in diagnostic criteria and screening frequency) also constrains the reliability of cross-national and cross-regional comparisons;35 moreover, the lack of age-stratified eGFR equations in the GBD estimation process may introduce bias, particularly for the 15–17-year subgroup, and should be considered when interpreting burden estimates for younger adolescents; lastly, the studies lack predictive models for future trends and fail to address the emerging challenges presented by globalization, climate change, and demographic shifts. Future studies should integrate GBD data with forecasting approaches, such as age-period-cohort analyses, Bayesian prediction models, or other burden forecasting frameworks, to estimate future trends in diabetes and kidney diseases among reproductive-age women. Such predictive modelling could provide valuable evidence for long-term healthcare planning, resource allocation, and targeted prevention strategies. In conclusion, while the study’s extensive coverage and multidimensional analysis are strengths, deficiencies in data quality, control of confounders, and prospective analyses significantly limit its depth and usefulness.
Conclusion
From 1990 to 2021, the global burden of diabetes and kidney disease among women aged 15–49 saw a substantial increase, driven predominantly by T2DM which exhibited the fastest-rising DALY rate and incidence. This burden was highest in low-SDI regions with marked regional disparities—Oceania emerged as a “hot spot,” North America showed high-income trends, and the Middle East-North Africa region (especially North Africa) saw significant growth. Notably, CKD and its hypertension-related subtypes declined in high-SDI regions, may suggest improvements in blood pressure control and diabetes management, but this observational trend alone does not confirm causality, while public health investments improved indicators in some sub-Saharan African and East Asian countries, may reflect the impact of preventive measures, although causal attribution requires further analytical studies. These findings highlight a global dichotomy: low-income regions struggle with primary healthcare access, while high-income regions face rising metabolic diseases and aging populations. To address these challenges, healthcare systems and policymakers should strengthen primary healthcare capacity in low-SDI regions through improved screening, early diagnosis, and long-term management of diabetes and kidney diseases. In high-SDI regions, greater emphasis should be placed on obesity prevention, healthy lifestyle promotion, and metabolic risk reduction among women of reproductive age. Tailored interventions based on regional socioeconomic conditions, together with international collaboration, may help reduce the disease burden and improve global health equity. Addressing this requires prioritizing metabolic diseases in middle-SDI regions, enhancing primary care in low-SDI regions, and tackling obesity-driven ASIR surges in high-SDI regions; layered interventions and international collaboration can reduce GBD and advance health equity.
Funding Statement
This study was supported by Zhejiang Province Medical and Health Science and Technology Planning Project (2023KY1031).
Data Sharing Statement
The datasets used and analyzed during the current study are available from publicly available GBD 2021 (https://vizhub.healthdata.org/gbdresults).
Ethics Approval
The GBD study was approved by the University of Washington. This study employed publicly available data that did not include confidential or personally identifiable patient information. Because this study was based solely on secondary analyses of publicly available, de-identified data and did not involve direct participation of human subjects, ethical approval and informed consent were not required in accordance with the Measures for Ethical Review of Life Science and Medical Research Involving Human Subjects (2023, China), Article 32.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; Lisha Li, Xiaoqing Jiang, Peimin Yu and Junyuan Li took part in drafting, Ming Tang, Peimin Yu and Junyuan Li took part in revising or critically reviewing the article; All authors gave final approval of the version to be published; All authors have agreed on the journal to which the article has been submitted; All authors agree to be accountable for all aspects of the work.
Disclosure
The author(s) of this work have nothing to disclose.
References
- 1.Fotheringham AK, Gallo LA, Borg DJ, Forbes JM. Advanced Glycation End Products (AGEs) and chronic kidney disease: does the modern diet AGE the kidney? Nutrients. 2022;14(13). doi: 10.3390/nu14132675 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Jain V, Al Rifai M, Khan SU, et al. Association between social vulnerability index and cardiovascular disease: a behavioral risk factor surveillance system study. J Am Heart Assoc. 2022;11(15):e024414. doi: 10.1161/JAHA.121.024414 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Li JW, Arnott C, Heerspink HJL, et al. Effect of Canagliflozin on total cardiovascular burden in patients with diabetes and chronic kidney disease: a post hoc analysis from the CREDENCE Trial. J Am Heart Assoc. 2022;11(16):e025045. doi: 10.1161/JAHA.121.025045 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Liu Y, Wen H, Bai J, Shi F, Bi R, Yu C. Burden of diabetes and kidney disease attributable to non-optimal temperature from 1990 to 2019: a systematic analysis from the Global Burden of Disease Study 2019. Sci Total Environ. 2022;838(Pt 3):156495. doi: 10.1016/j.scitotenv.2022.156495 [DOI] [PubMed] [Google Scholar]
- 5.Gupta S, Dominguez M, Golestaneh L. Diabetic kidney disease: an update. Med Clin North Am. 2023;107(4):689–16. doi: 10.1016/j.mcna.2023.03.004 [DOI] [PubMed] [Google Scholar]
- 6.Kautzky-Willer A, Leutner M, Harreiter J. Sex differences in type 2 diabetes. Diabetologia. 2023;66(6):986–1002. doi: 10.1007/s00125-023-05891-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Nichols AR, Chavarro JE, Oken E. Reproductive risk factors across the female lifecourse and later metabolic health. Cell Metab. 2024;36(2):240–262. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Smith PA, Sarris I, Clark K, Wiles K, Bramham K. Kidney disease and reproductive health. Nat Rev Nephrol. 2025;21(2):127–143. [DOI] [PubMed] [Google Scholar]
- 9.Zaimi M, Michalopoulou O, Stefanaki K, et al. Gonadal dysfunction in women with diabetes mellitus. Endocrine. 2024;85(2):461–472. doi: 10.1007/s12020-024-03729-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Banerjee A, Brackenridge A. Managing diabetic chronic kidney disease in pregnancy: current clinical practice and uncertainties. Diabetic Med. 2025;42(2):e15460. doi: 10.1111/dme.15460 [DOI] [PubMed] [Google Scholar]
- 11.Wicklow B, Retnakaran R. Gestational diabetes mellitus and its implications across the life span. Diabet Metabol J. 2023;47(3):333–344. doi: 10.4093/dmj.2022.0348 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Jefferson Antonio B, Patiño DG, Lopez Moreno M. Cost-utility analysis and budget impact of benralizumab as add-on therapy to standard care for severe eosinophilic asthma in Colombia. Expert Rev Pharmacoecon Outcomes Res. 2022;22(2):299–305. doi: 10.1080/14737167.2021.1945445 [DOI] [PubMed] [Google Scholar]
- 13.Khandpur S, Awasthi A, Behera MR, Purty AJ, Singh NP, Tiwari S. Kidney disease burden in an Asian Indian population: effect of the new 2021 serum creatinine CKD-EPI equation. Diabet Res Clin Pract. 2022;193:110120. doi: 10.1016/j.diabres.2022.110120 [DOI] [PubMed] [Google Scholar]
- 14.Chung H, Crowe CL, Kong SX, et al. Descriptive study of the economic burden among patients with type 2 diabetes mellitus, chronic kidney disease, and chronic kidney disease and type 2 diabetes mellitus in a large US commercially insured population. J Manag Care Spec Pharm. 2023;29(1):80–89. doi: 10.18553/jmcp.2023.29.1.80 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Bosomprah S, Bjonstad EC, Musuku J, et al. Burden of chronic kidney diseases and underlying causes in Zambia: evidence from the global burden of disease study 2019. BMC Nephrol. 2023;24(1):39. doi: 10.1186/s12882-023-03078-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Cook S, Schmedt N, Broughton J, Kalra PA, Tomlinson LA, Quint JK. Characterising the burden of chronic kidney disease among people with type 2 diabetes in England: a cohort study using the Clinical Practice Research Datalink. BMJ Open. 2023;13(3):e065927. doi: 10.1136/bmjopen-2022-065927 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ikem RT, Lawal-Bello AT, Soyoye DO, Fasanu AN, Kolawole BA. Assessing the burden of kidney disease among type 2 diabetic subjects attending a semi-urban tertiary health facility in South-West Nigeria. West Afr J Med. 2023;40(6):640–645. [PubMed] [Google Scholar]
- 18.Rangaswami J, Mathew RO. Reducing kidney disease burden in type 2 diabetes with SGLT2 inhibitors: shifting the goalposts upstream. Clin J Am Soc Nephrol. 2023;18(9):1119–1121. doi: 10.2215/CJN.0000000000000258 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Wearne N, Davidson B, Blockman M, Jones J, Ross IL, Dave JA. Management of type 2 diabetes mellitus and kidney failure in people with HIV-infection in Africa: current status and a call to action. HIV AIDS. 2023;15:519–535. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Kibirige D, Sekitoleko I, Lumu W. Burden and predictors of diabetic kidney disease in an adult Ugandan population with new-onset diabetes. BMC Res Notes. 2023;16(1):234. doi: 10.1186/s13104-023-06500-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Kewcharoen J, Trongtorsak A, Kanitsoraphan C, et al. Cognitive impairment and 30-day rehospitalization rate in patients with acute heart failure: a systematic review and meta-analysis. Indian Heart J. 2019;71(1):52–59. doi: 10.1016/j.ihj.2018.12.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Hlyan NP, Arif T, Jaufar SS, et al. From sugar spikes to pressure peaks: navigating the world of diabetes, hypertension, obesity, and kidney health. Cureus. 2024;16(3):e57241. doi: 10.7759/cureus.57241 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Ding X, Li X, Ye Y, Jiang J, Lu M, Shao L. Epidemiological patterns of chronic kidney disease attributed to type 2 diabetes from 1990–2019. Front Endocrinol. 2024;15:1383777. doi: 10.3389/fendo.2024.1383777 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Dennis JM, Shields BM, Henley WE, Jones AG, Hattersley AT. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol. 2019;7(6):442–451. doi: 10.1016/S2213-8587(19)30087-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Palomo-Piñón S, Aguilar-Alonso JA, Chávez-Iñiguez JS, et al. Strategies to address diabetic kidney disease burden in Mexico: a narrative review by the Mexican College of Nephrologists. Front Med. 2024;11:1376115. doi: 10.3389/fmed.2024.1376115 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Dettori R, Milzi A, Lubberich RK, et al. Chronic kidney disease is related to impaired left ventricular strain as assessed by cardiac magnetic resonance imaging in patients with ischemic cardiomyopathy. Clin Res Cardiol. 2024;113(11):1544–1554. doi: 10.1007/s00392-023-02346-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Sun J, Hu W, Ye S, Xu M, Deng D, Chen M. Global, regional and country-specific burden of chronic kidney disease due to type 1 diabetes mellitus: a systematic analysis of the 2021 global disease burden study. Diabetes Obes Metab. 2025;27(6):3397–3409. doi: 10.1111/dom.16358 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Ma X, Liu R, Xi X, Zhuo H, Gu Y. Global burden of chronic kidney disease due to diabetes mellitus, 1990–2021, and projections to 2050. Front Endocrinol. 2025;16:1513008. doi: 10.3389/fendo.2025.1513008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Chen J, Deng M, Zheng R, et al. Global, regional, and national trends in chronic kidney disease burden (1990–2021): a systematic analysis of the global burden of disease in 2021. Trop Med Health. 2025;53(1):26. doi: 10.1186/s41182-025-00703-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Hu R, Zhao Z, Xie L, Ma Z, Wu W, Li S. Global, regional, and national burden of chronic kidney disease due to diabetes mellitus type 2 from 1990 to 2021, with projections to 2036: a systematic analysis for the Global Burden of Disease Study 2021. Front Med. 2025;12:1531811. doi: 10.3389/fmed.2025.1531811 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Wang Y, Lin T, Lu J, et al. Trends and analysis of risk factor differences in the global burden of chronic kidney disease due to type 2 diabetes from 1990 to 2021: a population-based study. Diabetes Obes Metab. 2025;27(4):1902–1919. doi: 10.1111/dom.16183 [DOI] [PubMed] [Google Scholar]
- 32.Yang Q, Jin L, Luo M, Xie S. Current status, trend changes, and future predictions of the disease burden of type 1 diabetes kidney disease in global and China. Front Endocrinol. 2025;16:1559363. doi: 10.3389/fendo.2025.1559363 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Ghafoury R, Malek M, Ismail-Beigi F, Khamseh ME. Role of residual inflammation as a risk factor across Cardiovascular-Kidney-Metabolic (CKM) syndrome: unpacking the burden in people with type 2 diabetes. Diabetes Ther. 2025;16(7):1341–1365. doi: 10.1007/s13300-025-01743-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Wang Y, Gu S, Xie Z, et al. Trends and disparities in the burden of chronic kidney disease due to type 2 diabetes in China from 1990 to 2021: a population-based study. J Diabetes. 2025;17(4):e70084. doi: 10.1111/1753-0407.70084 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Jain L, Khatib MN, Roopashree R, et al. Regional burden, trends, and future projections of chronic kidney disease due to type 2 diabetes mellitus in South Asia: insights from the global burden of disease study (1990–2021) and ARIMA forecasting. Expert Rev Endocrinol Metab. 2025;20(4):335–343. doi: 10.1080/17446651.2025.2502620 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and analyzed during the current study are available from publicly available GBD 2021 (https://vizhub.healthdata.org/gbdresults).