Tracking three decades of type 1 diabetes‐related chronic kidney disease in East Asia: Burden, age–sex patterns, and quality of care index

ABSTRACT

Background

The escalating burden of type 1 diabetes‐related chronic kidney disease (CKD‐T1DM) presents a critical public health challenge worldwide. This study aims to comprehensively evaluate the longitudinal trends in disease burden and healthcare quality for CKD‐T1DM in East Asia compared with global patterns from 1990 to 2023.

Methods

Utilizing data from the Global Burden of Disease (GBD) Study 2023, we analyzed the age‐standardized incidence (ASIR), mortality (ASMR), and disability‐adjusted life years (ASDR) rates of CKD‐T1DM. The Estimated Annual Percentage Change (EAPC) was calculated to quantify temporal trends. Additionally, a multidimensional quality of care index (QCI) was constructed using principal component analysis (PCA) based on four proxy indicators to assess and benchmark healthcare performance across East Asian countries.

Results

Contrary to the global trend of rising mortality and disability burdens, East Asia exhibited a significant concurrent decline in ASIR, ASMR, and ASDR from 1990 to 2023. However, substantial regional heterogeneity was observed: Japan and South Korea maintained consistently high QCI scores (63.8–75.0), while China, Mongolia, and North Korea lagged significantly behind. The study also confirmed a bimodal age‐specific incidence pattern peaking in adolescence (10–14 years) and older adulthood (55–69 years), alongside notable gender disparities in older populations.

Conclusion

East Asia has achieved remarkable progress in mitigating the CKD‐T1DM burden, diverging significantly from the global trajectory; however, deep structural inequities in care quality persist between high‐income and middle‐to‐low‐income nations. Integrating QCI into national monitoring systems and prioritizing comprehensive, lifespan‐oriented management strategies are essential to addressing these disparities and sustaining regional improvements.

Between 1990 and 2023, East Asia achieved a significant reduction in CKD‐T1DM burden, diverging from rising global burden trends. Despite this progress, persistent regional inequalities in healthcare quality (QCI) highlight the urgent need for targeted strategies to bridge care gaps in lower‐performing nations.

Abbreviations

ASDR

Age‐standardized DALY rate

ASIR

Age‐standardized incidence rate

ASMR

Age‐standardized mortality rate

CI

Confidence interval

CKD

Chronic kidney disease

CKD‐T1DM

Type 1 diabetes‐related chronic kidney disease

CODEm

Cause of Death Ensemble modeling

DALYs

Disability‐adjusted life years

DisMod‐MR

Bayesian Multivariate Disease Modeling Tool

DPR

DALY‐to‐prevalence ratio

ESRD

End‐stage renal disease

ICD

International Classification of Diseases

IDF

International Diabetes Federation

MIR

Mortality‐to‐incidence ratio

NCD

Noncommunicable disease

PC1

First principal component

PCA

Principal Component Analysis

PIR

Prevalence‐to‐incidence ratio

QCI

Quality of Care Index

ST‐GPR

Spatiotemporal Gaussian process regression

T1DM

Type 1 diabetes / Type 1 diabetes mellitus

T2DM

Type 2 diabetes/Type 2 diabetes mellitus

UI

Uncertainty interval

YLDs

Years lived with disability

YLLs

Years of life lost

YLR

Years of life lost to years lived with disability ratio

INTRODUCTION

Chronic kidney disease (CKD) has emerged as a major global health threat, affecting ~ 788 million people in 2023—more than double the 1990 figure—and ranking as the ninth leading cause of death. Diabetes remains a primary driver of this burden ¹ ; the International Diabetes Federation (IDF) reported 537 million adults living with diabetes in 2021, with up to 40% at risk of developing diabetic kidney disease (DKD) ² , ³ . Among these, patients with type 1 diabetes face a unique and severe trajectory. Unlike type 2 diabetes, type 1 diabetes typically has an earlier onset, exposing patients to a longer lifetime of hyperglycemia that drives renal tubular and glomerular damage through oxidative stress and inflammation ⁴ , ⁵ . This prolonged metabolic strain significantly accelerates the progression to end‐stage renal disease (ESRD), leading to disproportionately high mortality and disability rates ⁵ . In East Asia, where diabetes prevalence is rising alongside rapid population aging, addressing this distinct burden of type 1 diabetes‐related CKD (CKD‐T1DM) has become an urgent public health priority, particularly in China, Japan, and South Korea ⁶ , ⁷ , ⁸ .

Recent studies have consistently shown a global increase in the incidence, mortality, and disability‐adjusted life years (DALYs) associated with CKD‐type 1 diabetes, with significant disparities across various demographic indices and regions ⁹ . Age‐stratified analyses further reveal that, despite improvements in diabetes management, the burden of CKD‐type 1 diabetes in terms of both incidence and DALYs continues to rise among young populations aged 10–35 years or 15–39 years ¹⁰ , ¹¹ . While most existing research has focused on the quantitative assessment of disease burden, there has been growing attention in recent years to the quality of care index (QCI) as a comprehensive measure of healthcare service quality ¹² , ¹³ . However, current literature on CKD‐type 1 diabetes, particularly in East Asia, has predominantly prioritized isolated metrics—such as incidence or mortality rates—offering only a fragmented view of the disease burden. The systematic application of QCI in CKD‐type 1 diabetes, particularly in East Asia, remains limited.

This study presents a comprehensive, updated evaluation of the global and East Asian disease burden of CKD‐type 1 diabetes, using GBD 2023 data for the period from 1990 to 2023. We analyzed the long‐term trends in age‐standardized rates (ASRs) for CKD‐type 1 diabetes, both globally and across East Asian countries. The study reports absolute changes in incidence, mortality, and DALYs, stratified by gender and age. Additionally, we examined age‐ and sex‐specific incidence patterns worldwide and in East Asia, revealing a bimodal distribution in both pediatric/adolescent and elderly populations. Lastly, we constructed a CKD‐type 1 diabetes‐specific QCI based on four GBD‐derived ratios (e.g., death/incidence ratio and DALY/prevalence ratio), and calculated the composite QCI using principal component analysis (PCA). We then compared QCI levels and trends across East Asian countries and countries with varying development levels, offering a cross‐national benchmarking evaluation of CKD‐type 1 diabetes healthcare quality.

METHODS

Study population and data collection

This study utilizes the Global Burden of Disease (GBD) 2023 database. GBD is the most systematic and comprehensive global epidemiological research framework, encompassing disease burden estimates from 1990 to 2023 for 204 countries and regions, 375 diseases and injuries, and 88 risk factors ¹⁴ . The GBD 2023 data are publicly accessible via the Global Health Data Exchange (GHDx) platform (https://ghdx.healthdata.org/). The database provides extensive health metrics, including incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and DALYs, along with 95% uncertainty intervals (UIs) ¹⁴ , ¹⁵ .

The estimation methods employed in the GBD study have been extensively detailed in the relevant literature ¹ , ¹⁰ , ¹¹ , ¹⁴ , ¹⁶ . The GBD disease burden estimates are based on a multi‐module model framework. Mortality data are estimated using the ‘Cause of Death Ensemble modelling’ (CODEm), while nonfatal burden (including prevalence, incidence, and YLDs) is derived from the ‘Bayesian Multivariate Disease Modelling Tool’ (DisMod‐MR 2.1). Risk factor estimates are obtained through spatiotemporal Gaussian process regression (ST‐GPR) ¹⁴ . In the CKD‐type 1 diabetes model, the disease remission rate is set to 0, consistent with the established consensus that diabetic nephropathy does not resolve naturally ¹⁷ . The GBD study calculates YLLs using a standardized life table, computes YLDs based on disability weights, and combines these to yield DALYs. All estimates include 95% UIs to account for statistical uncertainty.

Case definition and study metrics

This study primarily examines the changes in the burden of CKD‐type 1 diabetes (chronic kidney disease resulting from type 1 diabetes) in East Asia (China, Japan, South Korea, North Korea, and Mongolia) and globally. The GBD framework categorizes CKD by its etiology, including type 1 diabetes, type 2 diabetes, hypertension, glomerular diseases, and other causes. type 1 diabetes‐CKD was identified according to the International Classification of Diseases codes version 9 (ICD‐9) and version 10 (ICD‐9: 250.41, 250.43; ICD‐10: E10.2, E10.21, E10.22, E10.29) ¹¹ , ¹⁴ .

This study conducts a secondary analysis based on GBD estimate results, extracting annual data on the incidence, incidence rate, mortality, mortality rate, prevalence, DALYs, YLLs, and YLDs for CKD‐type 1 diabetes from 1990 to 2023 using the GHDx query tool (http://ghdx.healthdata.org/gbd‐results‐tool). All indicators were stratified by gender, country, and 5‐year age groups. In addition, age‐standardized incidence rate (ASIR), mortality rate (ASMR), and DALY rate (ASDR) were calculated to facilitate cross‐regional and cross‐temporal comparisons ¹⁸ , ¹⁹ .

Estimated annual percentage change

We evaluated temporal trends in the ASRs of CKD‐type 1 diabetes from 1990 to 2023 using the Estimated Annual Percentage Change (EAPC). A log‐linear regression model was employed to describe the natural logarithm of the ASR, fitting the equation: $\ln \left(\mathrm{ASR}\right)=\mathrm{\alpha }+\mathrm{\beta x}+\mathrm{\epsilon }$, where $x$ represents the calendar year and $\mathrm{\epsilon }$ denotes the error term. The EAPC was then derived from the regression coefficient $\mathrm{\beta }$ using the formula: $\text{EAPC}=100\times \left(\exp \left(\mathrm{\beta }\right)-1\right)$, along with its 95% confidence intervals (CIs). Trends were considered significantly increasing or declining if the 95% CI of the EAPC was entirely positive or negative, respectively; otherwise, they were deemed stable. Additionally, absolute burden metrics (Incedence, Deaths, DALYs, ASIR, ASMR, and ASDR) are presented with 95% uncertainty intervals (UIs) derived from the 2.5th and 97.5th percentiles of 1,000 bootstrap simulations ²⁰ , ²¹ .

Quality of care index (QCI)

To evaluate the quality of care for CKD‐type 1 diabetes, we adapted methodologies from previous GBD‐based QCI studies and selected four proxy indicators that reflect differences in mortality, disability, quality of life, and age structure for CKD‐type 1 diabetes (22, 23, 24, 25). These indicators include the mortality‐to‐incidence ratio (MIR), DALY‐to‐prevalence ratio (DPR), prevalence‐to‐incidence ratio (PIR), and years of life lost to years lived with disability ratio (YLR). Specifically, MIR reflects mortality control, DPR indicates disease severity and disability burden, PIR assesses the quality of chronic disease management, and YLR captures the influence of age structure on disease outcomes ¹³ , ²² , ²³ .

We conducted PCA on the four standardized indicators to derive the first principal component (PC1), which maximizes the explained variance, serving as the core score for overall quality of care ²⁴ . To facilitate direct comparison of the QCI across countries and years, the original PC1 scores were further standardized to a uniform range of 0–100 using a min–max linear transformation. In this context, 0 indicates the lowest quality of care, while 100 signifies the highest quality. The formula is as follows ¹³ , ²⁵ :

$$\mathrm{QCI}=\frac{\mathrm{PC}1-\mathrm{PC}{1}_{\min }}{\mathrm{PC}{1}_{\max }-\mathrm{PC}{1}_{\min }}\times 100$$

This standardization not only enhances the index's intuitiveness but also ensures comparability across countries and years. Overall, the QCI effectively integrates burden information from multiple dimensions of CKD‐type 1 diabetes, including mortality control, disease severity, long‐term management, and age structure, offering a comprehensive, reliable, and standardized framework for evaluating and comparing healthcare quality at global and regional levels.

Data processing and statistical analysis

Data processing and analysis were conducted using R Studio, version 4.4.2 (R Project for Statistical Computing). The ggplot2 package was used for data visualization. Statistical significance was defined as a P < 0.05. All models were thoroughly tested to ensure the reliability and accuracy of the results.

RESULTS

Global and regional trends in the burden of CKD‐T1DM

From 1990 to 2023, the global ASIR of CKD‐type 1 diabetes decreased from 0.72 per 100,000 to 0.60 per 100,000 (EAPC = −0.57, [95% CI: −0.63, −0.52]), showing a significant decline. In contrast, the ASMR increased from 0.81 to 0.85 per 100,000 (EAPC = 0.16, [95% CI: 0.10, 0.21]), while the ASDR rose from 32.40 to 34.68 per 100,000 (EAPC = 0.19, [95% CI: 0.14, 0.25]), indicating a continued increase in the global mortality and disability burden of CKD‐type 1 diabetes (Table 1, Figure 1). Meanwhile, the global number of incidences, deaths, and DALYs rose from 36,600, 36,100, and 1.49 million to 49,000, 76,500, and 3.06 million, respectively, further highlighting the ongoing rise in the absolute disease burden (Table 2).

Table 1.

Age‐standardized incidence, mortality and DALYs rates, and temporal trends of CKD‐T1DM in East Asian countries and globally, 1990–2021, by sex

Measure	ASIR			ASMR			ASDR
	1990	2023	1990–2023	1990	2023	1990–2023	1990	2023	1990–2023
	(95% UI, per 100,000 population)		EAPC (95% CI)	(95% UI, per 100,000 population)		EAPC (95% CI)	(95% UI, per 100,000 population)		EAPC (95% CI)
Both
Global	0.72 (0.62, 0.86)	0.60 (0.53, 0.69)	−0.57 (−0.63, −0.52)	0.81 (0.60, 1.06)	0.85 (0.63, 1.09)	0.16 (0.10, 0.21)	32.40 (24.43, 41.37)	34.68 (26.20, 43.88)	0.19 (0.14, 0.25)
East Asia	0.43 (0.35, 0.54)	0.32 (0.26, 0.40)	−0.96 (−1.09, −0.83)	0.61 (0.41, 0.89)	0.24 (0.16, 0.33)	−2.90 (−3.12, −2.68)	23.46 (16.44, 32.82)	9.90 (7.15, 13.30)	−2.65 (−2.87, −2.42)
China	0.43 (0.34, 0.54)	0.31 (0.25, 0.39)	−0.97 (−1.11, −0.84)	0.61 (0.41, 0.90)	0.23 (0.16, 0.31)	−3.04 (−3.28, −2.81)	23.56 (16.46, 33.09)	9.50 (6.85, 12.77)	−2.79 (−3.02, −2.55)
Democratic People's Republic of Korea	0.51 (0.32, 1.03)	0.47 (0.27, 0.97)	−0.38 (−0.44, −0.33)	0.50 (0.30, 0.76)	0.56 (0.35, 0.84)	0.19 (0.13, 0.25)	20.25 (13.41, 30.53)	22.40 (14.97, 32.16)	0.16 (0.10, 0.21)
Japan	0.67 (0.52, 0.84)	0.54 (0.44, 0.69)	−0.77 (−0.88, −0.66)	0.60 (0.44, 0.78)	0.26 (0.19, 0.35)	−2.38 (−2.71, −2.05)	26.03 (19.78, 32.95)	16.46 (11.75, 21.94)	−1.27 (−1.59, −0.94)
Mongolia	1.05 (0.63, 2.26)	0.96 (0.55, 2.28)	−0.38 (−0.43, −0.33)	0.42 (0.26, 0.61)	0.29 (0.20, 0.43)	−1.75 (−1.99, −1.51)	17.08 (10.90, 25.12)	12.31 (8.59, 17.43)	−1.63 (−1.87, −1.39)
Republic of Korea	0.49 (0.30, 1.03)	0.35 (0.19, 0.87)	−1.04 (−1.16, −0.91)	0.72 (0.49, 1.16)	0.39 (0.25, 0.54)	−1.79 (−2.08, −1.50)	25.20 (18.30, 37.44)	19.53 (14.45, 25.53)	−0.65 (−0.89, −0.40)
Male
Global	0.80 (0.69, 0.94)	0.68 (0.60, 0.77)	−0.52 (−0.56, −0.48)	0.92 (0.65, 1.27)	0.92 (0.66, 1.24)	0.05 (0.01, 0.09)	36.63 (26.79, 49.18)	37.33 (27.50, 49.26)	0.10 (0.07, 0.14)
East Asia	0.44 (0.36, 0.55)	0.33 (0.27, 0.42)	−0.89 (−1.00, −0.77)	0.62 (0.38, 1.04)	0.27 (0.18, 0.38)	−2.56 (−2.76, −2.35)	24.15 (15.67, 38.12)	11.46 (8.10, 16.03)	−2.24 (−2.44, −2.03)
China	0.43 (0.34, 0.54)	0.32 (0.26, 0.41)	−0.90 (−1.02, −0.78)	0.62 (0.37, 1.05)	0.26 (0.17, 0.37)	−2.69 (−2.91, −2.48)	24.13 (15.53, 38.45)	11.01 (7.65, 15.34)	−2.36 (−2.58, −2.14)
Democratic People's Republic of Korea	0.56 (0.34, 1.14)	0.51 (0.30, 1.08)	−0.37 (−0.41, −0.32)	0.67 (0.37, 1.09)	0.61 (0.34, 1.04)	−0.40 (−0.46, −0.35)	27.21 (16.52, 44.22)	25.44 (15.27, 40.78)	−0.35 (−0.40, −0.30)
Japan	0.71 (0.57, 0.88)	0.60 (0.48, 0.77)	−0.70 (−0.79, −0.60)	0.74 (0.56, 1.01)	0.38 (0.27, 0.50)	−1.91 (−2.21, −1.62)	33.01 (24.89, 42.70)	22.05 (15.56, 29.68)	−1.07 (−1.36, −0.78)
Mongolia	0.93 (0.57, 1.91)	0.83 (0.47, 1.84)	−0.40 (−0.45, −0.36)	0.41 (0.25, 0.66)	0.36 (0.22, 0.55)	−1.01 (−1.30, −0.72)	16.58 (10.35, 25.16)	14.96 (9.71, 22.11)	−0.88 (−1.18, −0.57)
Republic of Korea	0.52 (0.32, 1.11)	0.37 (0.19, 0.96)	−0.95 (−1.07, −0.83)	0.95 (0.62, 1.66)	0.48 (0.30, 0.71)	−1.84 (−2.09, −1.59)	32.69 (22.37, 53.06)	24.85 (17.55, 33.91)	−0.65 (−0.87, −0.43)
Female
Global	0.64 (0.54, 0.77)	0.52 (0.45, 0.60)	−0.64 (−0.72, −0.57)	0.70 (0.50, 0.95)	0.78 (0.57, 1.04)	0.28 (0.21, 0.36)	28.23 (20.58, 37.88)	32.09 (23.62, 41.69)	0.31 (0.22, 0.40)
East Asia	0.43 (0.34, 0.54)	0.30 (0.24, 0.39)	−1.04 (−1.19, −0.90)	0.59 (0.38, 0.91)	0.21 (0.14, 0.29)	−3.31 (−3.56, −3.07)	22.75 (14.88, 33.99)	8.29 (5.85, 11.28)	−3.17 (−3.41, −2.92)
China	0.42 (0.33, 0.54)	0.29 (0.23, 0.37)	−1.07 (−1.22, −0.91)	0.60 (0.39, 0.92)	0.20 (0.13, 0.28)	−3.47 (−3.73, −3.21)	22.97 (14.87, 34.55)	7.95 (5.50, 10.93)	−3.33 (−3.58, −3.07)
Democratic People's Republic of Korea	0.47 (0.29, 1.00)	0.42 (0.24, 0.94)	−0.43 (−0.49, −0.37)	0.36 (0.20, 0.58)	0.50 (0.27, 0.85)	0.84 (0.77, 0.90)	14.26 (8.52, 22.17)	19.37 (11.58, 31.78)	0.77 (0.72, 0.83)
Japan	0.62 (0.48, 0.79)	0.48 (0.39, 0.61)	−0.86 (−0.99, −0.74)	0.47 (0.33, 0.61)	0.15 (0.11, 0.20)	−3.34 (−3.73, −2.95)	19.46 (14.42, 25.04)	10.95 (8.04, 14.56)	−1.69 (−2.09, −1.29)
Mongolia	1.17 (0.69, 2.67)	1.08 (0.61, 2.69)	−0.37 (−0.43, −0.31)	0.42 (0.24, 0.70)	0.23 (0.15, 0.35)	−2.56 (−2.82, −2.29)	17.64 (10.08, 28.04)	9.95 (6.73, 14.36)	−2.45 (−2.70, −2.20)
Republic of Korea	0.45 (0.28, 0.94)	0.32 (0.19, 0.76)	−1.11 (−1.26, −0.96)	0.54 (0.37, 0.84)	0.29 (0.20, 0.40)	−1.88 (−2.24, −1.53)	18.69 (13.34, 26.72)	14.11 (10.56, 18.29)	−0.79 (−1.07, −0.50)

Abbreviations: ASIR, Age‐standardized incidence rate; ASMR: age‐standardized mortality rate; ASDR, age‐standardized DALYs rate; CI, confidence interval; CKD‐T1DM: type 1 diabetes‐related chronic kidney disease; EAPC, estimated annual percentage change; UI, uncertainty interval.

Figure 1.

Trends in age‐standardized rates of CKD‐type 1 diabetes in East Asian Countries and Globally, 1990–2021, by sex. (a) age‐standardized incidence rate; (b) age‐standardized mortality rate; (c) age‐standardized DALYs rate. CKD‐T1DM: type 1 diabetes‐related chronic kidney disease.

Table 2.

Number of incidence, Deaths and DALYs Cases of CKD‐T1DM in East Asian Countries and Globally, 1990–2021, by sex

Measure	Incidence number (95% UI)		Deaths number (95% UI)		Dalys number (95% UI)
	1990	2023	1990	2023	1990	2023
Both
Global	36615.93 (31229.26, 43273.07)	49043.07 (42875.48, 55726.16)	36074.49 (26887.84, 46805.75)	76498.37 (56401.30, 98445.02)	1491849.87 (1148780.39, 1902825.43)	3060459.64 (2318378.38, 3889857.76)
East Asia	4595.94 (3677.15, 5760.97)	5184.58 (4342.87, 6144.69)	6329.04 (4346.91, 9145.46)	5436.82 (3747.35, 7574.86)	254902.47 (176956.96, 360136.63)	216956.80 (154743.61, 298059.12)
China	4354.71 (3428.83, 5543.29)	4888.25 (4075.29, 5804.03)	6138.86 (4193.49, 8903.42)	5053.26 (3432.39, 7041.82)	246843.96 (170921.27, 349822.49)	201542.56 (142984.29, 279019.89)
Democratic People's Republic of Korea	96.79 (58.17, 203.81)	122.97 (79.04, 214.91)	95.92 (59.82, 148.38)	198.29 (121.17, 300.35)	4048.52 (2670.16, 6118.49)	7864.77 (5182.29, 11404.39)
Japan	902.80 (693.33, 1137.51)	853.88 (701.93, 1025.13)	1019.63 (743.16, 1338.53)	685.30 (472.67, 958.41)	43195.50 (32485.78, 55226.44)	32917.16 (23638.09, 44546.53)
Mongolia	21.43 (10.97, 54.80)	33.44 (18.31, 82.83)	5.36 (3.35, 7.72)	9.78 (6.71, 14.14)	236.94 (153.83, 337.60)	429.46 (294.69, 606.55)
Republic of Korea	179.68 (105.88, 391.99)	194.50 (139.88, 317.63)	258.51 (181.37, 393.89)	371.21 (237.38, 525.36)	9706.31 (7166.28, 14022.02)	16955.34 (12199.55, 22548.59)
Male
Global	20497.57 (17543.21, 23876.69)	27496.47 (24137.78, 31180.29)	20178.56 (14420.61, 27815.42)	40925.11 (29012.11, 55501.44)	836988.41 (625642.84, 1123871.19)	1644612.79 (1206314.70, 2175143.35)
East Asia	2373.43 (1892.74, 2995.74)	2720.19 (2270.86, 3185.38)	3301.23 (2044.97, 5489.76)	3015.41 (1975.82, 4341.13)	134808.45 (86147.12, 209154.99)	125322.22 (86646.48, 179196.82)
China	2239.18 (1745.42, 2841.65)	2562.40 (2136.79, 3006.24)	3194.79 (1944.83, 5361.18)	2801.99 (1805.93, 4066.62)	130145.87 (82474.11, 203600.29)	116506.44 (80279.98, 167727.99)
Democratic People's Republic of Korea	49.44 (27.86, 111.40)	64.03 (41.39, 112.91)	57.37 (32.53, 95.69)	103.04 (55.26, 174.90)	2497.51 (1520.60, 3955.95)	4304.81 (2605.33, 7014.68)
Japan	441.15 (345.36, 553.75)	422.63 (356.37, 506.53)	587.28 (434.79, 795.22)	466.02 (315.47, 656.13)	26243.43 (19698.42, 34331.75)	21881.99 (15294.57, 29203.28)
Mongolia	9.31 (4.51, 24.04)	14.23 (7.38, 33.13)	2.55 (1.56, 3.98)	5.65 (3.52, 8.53)	111.89 (70.23, 166.70)	248.87 (160.43, 369.66)
Republic of Korea	94.96 (52.31, 220.14)	90.60 (64.23, 157.87)	154.78 (104.92, 259.03)	226.51 (140.27, 338.23)	5929.82 (4190.84, 9186.12)	10856.25 (7496.45, 15011.25)
Female
Global	16118.36 (13598.86, 19333.57)	21546.60 (18712.56, 24945.67)	15895.93 (11481.77, 21461.07)	35573.25 (25742.67, 47201.23)	654861.46 (479172.95, 874314.47)	1415846.85 (1042490.97, 1854340.44)
East Asia	2222.51 (1755.85, 2836.67)	2464.38 (2021.62, 2973.76)	3027.81 (1947.27, 4621.23)	2421.40 (1609.89, 3467.70)	120094.02 (78311.38, 178195.44)	91634.58 (62875.17, 124814.00)
China	2115.52 (1622.70, 2720.36)	2325.85 (1904.74, 2814.77)	2944.07 (1881.34, 4518.83)	2251.26 (1475.34, 3222.45)	116698.10 (75596.66, 173737.79)	85036.12 (57446.42, 115676.67)
Democratic People's Republic of Korea	47.35 (29.42, 100.90)	58.94 (37.03, 105.76)	38.56 (21.42, 60.76)	95.25 (48.68, 160.39)	1551.01 (924.47, 2385.98)	3559.96 (2075.90, 5829.06)
Japan	461.65 (344.91, 596.81)	431.25 (345.67, 523.64)	432.35 (309.69, 568.53)	219.28 (150.04, 303.95)	16952.07 (12517.35, 21901.08)	11035.17 (7948.22, 14896.56)
Mongolia	12.12 (6.35, 31.98)	19.21 (10.63, 48.90)	2.81 (1.60, 4.65)	4.14 (2.60, 6.08)	125.06 (71.73, 199.11)	180.59 (122.40, 265.76)
Republic of Korea	84.72 (53.04, 179.65)	103.91 (74.81, 162.55)	103.73 (72.23, 157.11)	144.70 (97.87, 205.78)	3776.49 (2755.73, 5355.26)	6099.09 (4482.17, 7925.79)

Abbreviations: CKD‐T1DM, type 1 diabetes‐related chronic kidney disease; DALYs, disability‐adjusted life years; UI, uncertainty interval.

Contrary to global trends, East Asia saw a significant decline in all three ASRs from 1990 to 2023: ASIR dropped from 0.43 to 0.32 (EAPC = −0.96, [95% CI: −1.09, −0.83]), ASMR fell from 0.61 to 0.24 (EAPC = −2.90, [95% CI: −3.12, −2.68]), and the ASDR decreased from 23.46 to 9.90 (EAPC = −2.65, [95% CI: −2.87, −2.42]) (Table 1, Figure 1). Although the number of incidence cases slightly increased from 4,596 to 5,185, both deaths and DALYs saw significant reductions (deaths: from 6,329 to 5,437; DALYs: from 254,902 to 216,957), indicating that, despite the rise in new cases, East Asia has made meaningful progress in reducing mortality and disability (Table 2).

Heterogeneity in the burden of CKD‐T1DM across east Asian countries

At the national level, China, Japan, and South Korea all demonstrated a consistent decline in the burden of CKD‐type 1 diabetes from 1990 to 2023. In all three countries, the ASIR, ASMR, and ASDR showed significant reductions. In China, the ASIR decreased from 0.43 to 0.31, the ASMR from 0.61 to 0.23, and the ASDR from 23.56 to 9.50. In Japan, these indicators decreased to 0.54, 0.26, and 16.46, respectively. South Korea followed a similar downward trend (from 0.49 to 0.35; from 0.72 to 0.39). In terms of absolute numbers, the number of incidence, deaths, and DALYs in both China and Japan decreased in parallel, further confirming progress in disease control, complication management, and survival improvement in these countries (Table 1, Figures 1 and 2).

Figure 2.

Age‐standardized rates of CKD‐T1DM in East Asian countries. (a) ASIR in 1990; (b) ASMR in 1990; (c) ASIR in 2023; (d) ASMR in 2023. ASIR, age‐standardized incidence rate; ASMR, age‐standardized mortality rate; CKD‐T1DM, Type 1 diabetes‐related chronic kidney disease.

In contrast, North Korea and Mongolia displayed different burden trajectories. North Korea's ASIR remained relatively stable (from 0.51 to 0.47), but its ASMR rose to the highest in the region (0.56 per 100,000), and the ASDR increased from 20.25 to 22.40. Both number of deaths and DALYs showed significant increases (deaths: from 95.92 to 198.29; DALYs: from 4,048.52 to 7,864.77), indicating a continued deterioration in the mortality and disability burden from CKD‐type 1 diabetes. Mongolia maintained the highest ASIR in the region throughout the period (from 1.05 to 0.96), but its ASMR remained the lowest (from 0.42 to 0.29), presenting a ‘high incidence, low mortality’ pattern, even though the number of incidence, deaths, and DALYs cases all increased. While South Korea demonstrated a downward trend in age‐standardized indicators, its number of deaths and DALYs rose to 371.21 and 16,955.34, respectively, revealing ongoing pressure between increasing case burden and disease management (Table 1, Figure 2).

Age and gender variations in the bimodal distribution of CKD‐T1DM

The incidence of CKD‐type 1 diabetes in both East Asia and globally exhibits a typical ‘bimodal’ age structure, with two main peaks: one in childhood and the other in middle/older adulthood. The first peak occurs in the 10–14 age group, followed by a decline in the 15–39 age group. After age 40, the incidence rises again, with the second peak observed between 55 and 69 years, and a gradual decline in incidence after age 80. This distribution pattern is seen across most countries, with males generally having a higher ASIR than females in all age groups, with the greatest difference observed in middle and older age groups. The UIs are wider in the pediatric and elderly groups, where case numbers are lower (Figure 3a,b).

Figure 3.

Age and gender specific incidence rates and cases of CKD‐T1DM in 2023. (a) Globally; (b) East Asia; (c) China; (d) Japan; (e) South Korea; (f) Democratic People's Republic of Korea; (g) Mongolia. CKD‐T1DM, Type 1 diabetes‐related chronic kidney disease.

Although differences remain between countries, China, Japan, and South Korea all show a typical ‘bimodal’ distribution, though the peak shapes and gender differences vary slightly. In China, the primary peaks occur in both children (10–14 years) and middle/older adults (55–69 years), with males generally having higher incidence rates than females throughout the lifespan. In Japan, the bimodal peaks occur at 10–14 years and 60–69 years, with gender differences following a pattern where middle‐aged women have slightly higher rates, but older men dominate the later peak. South Korea exhibits a relatively flatter bimodal shape, with a less pronounced second peak in middle/older adults compared with other countries. Additionally, females in South Korea show slightly higher rates than males in the 50–69 age group. Mongolia stands out with a more prominent childhood peak, where the ASIR in children aged 10–14 years is significantly higher than in other East Asian countries, particularly in males. After age 40, the incidence rises slowly, and the middle/older adult peak is less marked. North Korea generally follows a bimodal trend, but the plateau in the 55–69 age range is wider, and males consistently have higher incidence rates than females throughout the lifespan. This suggests that the country's unique age structure and chronic disease management context may influence the incidence curve (Figure 3).

Geographic disparities in QCI for CKD‐T1DM in east Asian countries

Significant geographic disparities in CKD‐type 1 diabetes care quality persisted across East Asia between 1990 and 2023. In 1990, the QCI was highest in South Korea and Japan (46.8–83.9%), reflecting the early establishment of robust national diabetes and kidney disease management systems in these high‐income countries. Conversely, China and Mongolia showed significantly lower performance (3.8–38.9%), indicating constraints in chronic disease prevention and resource allocation. North Korea consistently registered the lowest scores (4.1–11.1%). By 2023, this regional gradient remained largely unchanged: Japan and South Korea maintained high QCI scores (63.8–75.0%), while China and Mongolia saw a decline to the 25.7–34.2% range (Figure 4a,b). This persistent divergence—with high‐income nations consistently leading and middle‐to‐low‐income nations showing limited progress—underscores that long‐term structural differences in economic development and healthcare system capacity are the primary determinants of CKD‐type 1 diabetes care quality in the region.

Figure 4.

Geographic disparities in QCI for CKD‐T1DM in East Asian countries. CKD‐T1DM, type 1 diabetes‐related chronic kidney disease; QCI, quality of care index.

The QCI's internal validity is supported by the correlation structure among its four proxy indicators. The mortality‐related indicators, DPR and YLR, were perfectly correlated (r = 1.00), and both exhibited a strong positive correlation with MIR (r = 0.72), confirming consistency across mortality and severity measures. PIR (Prevalence‐to‐Incidence Ratio) correlated positively with MIR (r = 0.62) but moderately negatively with YLR (r = −0.64), reflecting the distinct influence of disability burden vs the age structure of the population. PCA demonstrated that the first two components (PC1 and PC2) captured the total variance, explaining 60.8 and 39.2%, respectively, validating the QCI as an effective integrative measure. Mortality (MIR: 27%) and prevention (DPR: 26%) were the highest contributors to PC1, followed closely by disability (PIR: 24%), establishing mortality control and disability management as the core dimensions of the index. YLR contributed most significantly to PC2 (23%), providing essential, independent age‐related information for care assessment (Figure 4c,d).

DISCUSSION

This study utilized GBD 2023 data to systematically evaluate the disease burden and QCI for CKD‐type 1 diabetes globally and across East Asian countries from 1990 to 2023. Our analysis of long‐term trends in ASIR, ASMR, and ASDR, alongside absolute incidence counts, deaths, and DALYs, revealed two critical findings. Globally, while the ASIR of CKD‐type 1 diabetes slightly decreased, the ASMR and ASDR continued to rise. However, in East Asia, ASRs showed a simultaneous decline, diverging significantly from the global pattern. Significant improvements were observed in Japan and South Korea, while North Korea and Mongolia consistently exhibited lower burden and care quality. QCI analysis further highlighted a stable gradient of ‘Japan and South Korea leading, China and Mongolia at moderate levels, and North Korea at the lowest,’ indicating substantial and persistent disparities in care quality within East Asia.

The divergence between the global decrease in ASIR and the concurrent increase in ASMR and ASDR reflects that amidst the continuing rise in diabetes prevalence and population aging, the extended survival of type 1 diabetes patients and the accumulation of comorbidities are driving the sustained increase in the mortality and disability burden of CKD‐type 1 diabetes ²⁶ , ²⁷ . This suggests that simply ‘reducing incidence’ is insufficient to address the overall burden. The key to future prevention and control strategies must be finding ways to extend survival while decisively slowing kidney function decline and reducing ESRD and cardiovascular events. In sharp contrast, the concurrent decline across ASRs (ASIR, ASMR, and ASDR) observed in East Asia suggests the region has successfully implemented this strategy. This regional success is largely attributable to significant and sustained improvements in blood glucose and blood pressure control, the widespread adoption of kidney protective therapies such as RAS inhibitors, enhanced early screening efforts, and potentially bolstered in recent years by the introduction of novel agents like SGLT2 inhibitors ²⁸ , ²⁹ , ³⁰ . At the national level, China, Japan, and South Korea have shown a continuous decline in ASIR, ASMR, and ASDR, while North Korea has experienced an increase in ASMR and ASDR, and Mongolia has consistently maintained the highest ASIR in the region, with the lowest ASMR. These trends highlight significant disparities in healthcare systems within East Asia. In China and Japan, the absolute numbers of incidence, deaths, and DALYs have declined together, suggesting coordinated progress in the management of type 1 diabetes across its entire disease course, early diabetic nephropathy detection, and the provision of dialysis/transplantation services ³¹ , ³² . In South Korea, although the ASIR has decreased, deaths and DALYs continue to rise, indicating that the increasing burden of existing cases, particularly among the elderly, continues to strain the healthcare system ³³ . Mongolia's ‘high incidence, low mortality’ pattern likely reflects better registration of type 1 diabetes and detection of kidney disease in children, as well as more effective treatment of acute complications. However, it also suggests gaps in long‐term follow‐up and risk factor control ⁹ . In contrast, North Korea maintained a stable ASIR throughout the study period, but its ASMR and ASDR rose sharply from low regional levels to the highest, coupled with an extremely low QCI, likely indicating structural issues such as weak chronic disease prevention systems, limited access to essential medications and renal replacement therapy, and a lack of chronic disease follow‐up ³⁴ . Similar phenomena observed in middle‐ and low‐SDI regions suggest that even when incidence rates are low, mortality and DALYs can rise significantly due to insufficient treatment opportunities and the heavy burden of complications. This heterogeneity highlights the need to consider the complex interactions between disease incidence, healthcare access, and medical quality when evaluating the burden of CKD‐type 1 diabetes ³⁵ .

This study confirms the typical bimodal incidence pattern of CKD‐type 1 diabetes globally and in East Asia, characterized by peaks in childhood/adolescence (around 10–14 years) and again in middle/older adulthood (55–69 years). The early peak may be associated with the combined impact of pubertal hormonal fluctuations and prolonged hyperglycemic exposure in patients diagnosed young, leading to overt kidney damage ³⁶ . The later peak reflects the cumulative effect of extended diabetes duration and acquired comorbidities such as hypertension, dyslipidemia, and obesity ³⁷ . We also observed that males generally exhibit a higher ASIR than females across most age groups, a disparity that is particularly pronounced in older populations and aligns with known literature regarding unfavorable risk factor exposure in men ³⁸ . However, heterogeneity exists; for instance, females in Japan and South Korea show slightly higher rates in the 50–69 age bracket, indicating complex influences of sex hormones, lifestyle, and unique healthcare utilization patterns within specific cultural contexts ³⁸ , ³⁹ , ⁴⁰ .

In the assessment of quality of care, we found that both the 1990 and 2023 QCI scores for East Asia exhibited a significant gradient: South Korea and Japan have consistently remained in the high range, while China and Mongolia were notably lower, and North Korea remained the lowest. The QCI levels strongly corresponded with each country's development and healthcare coverage ¹³ . Furthermore, our study revealed a high correlation between the four proxy indicators. The first two principal components explained all the variance, with MIR and DPR contributing the most to PC1, followed by PIR, and YLR predominantly loading onto PC2. This suggests that mortality control and disability management form the core dimensions of the QCI, while age structure provides independent supplementary information ²⁴ , ²⁵ . These findings not only confirm that the QCI effectively integrates multidimensional burden data but also imply that QCI should be interpreted as a ‘relative indicator of care quality at a given prevalence level,’ rather than an absolute measure independent of disease incidence ⁴¹ .

Our study indicates that relying solely on changes in incidence or prevalence underestimates the profound impact of healthcare services and policies on reducing mortality and disability. We, therefore, advocate for the integration of the QCI into future noncommunicable disease (NCD) monitoring systems. QCI can identify countries with varying outcomes (e.g., ‘high disease burden but good care quality’ vs. ‘moderate disease burden but poor care quality’), offering precise guidance for resource allocation and policy interventions. In practice, clinical strategies must include: First, early integration of endocrine and nephrology care following a type 1 diabetes diagnosis, involving routine monitoring of microalbuminuria and eGFR. Comprehensive interventions targeting blood glucose, pressure, and lipids must be implemented immediately, focusing particularly on high‐risk children/adolescents and middle‐aged adults ⁴² . Second, in regions with lower development levels and persistently low QCI, priority must be given to enhancing the accessibility of essential therapies, including insulin and RAS inhibitors ⁴³ . Furthermore, recent therapeutic advancements, such as SGLT2 inhibitors, should be rapidly deployed, alongside strengthened provision of life‐sustaining treatments (dialysis and transplantation) to establish a true lifespan management system for type 1 diabetes patients ⁴⁴ .

This study also has several limitations. First, it relies entirely on the modeled estimates from GBD 2023. Although methods such as CODEm and DisMod‐MR have been validated in multiple rounds of GBD studies, in countries with extremely limited baseline data (such as North Korea and Mongolia), the results inevitably depend on indirect inferences and extrapolations of covariates, which may lead to an underestimation or overestimation of the true burden. Second, the case definition for CKD‐type 1 diabetes is based on ICD‐9/10 codes, which makes it difficult to fully differentiate true type 1 diabetes from insulin‐dependent type 2 diabetes, particularly among elderly patients. This misclassification could introduce bias in the estimation of age structure and gender differences. Third, the construction of the QCI is based on four ratio indicators and the linear assumptions of PCA. Although this method has been widely applied to various cancers and cardiovascular diseases, the index itself is still influenced by DALY estimation methods, the width of UIs, and extreme values. This study has not conducted systematic sensitivity analyses for different administrative levels or combinations of indicators. Finally, national‐level summary data were used, which means individual‐level factors, such as blood glucose control, disease duration, comorbidity profiles, and socioeconomic status could not be controlled. Therefore, the correlations observed at the national level should not be simply interpreted as causal relationships at the individual level.

CONCLUSION

This GBD 2023 analysis reveals that the CKD‐type 1 diabetes burden in East Asia is diverging significantly from the global trend, demonstrating a concurrent decline in ASIR, ASMR, and ASDR between 1990 and 2023. However, this success is uneven, as highlighted by a persistent, structural gradient in the QCI, with high‐income nations leading and others facing challenges. We advocate for integrating the QCI into NCD monitoring systems, as it shifts the strategic focus from merely reducing incidence to assessing and enhancing the quality of chronic disease management. Future public health efforts must prioritize comprehensive, lifespan care—including the timely deployment of advanced therapies like RAS inhibitors and SGLT2 inhibitors—to effectively extend survival and mitigate the accumulating mortality and disability burden of CKD‐type 1 diabetes across the region.

FUNDING

This research was funded by the China National Health Development Research Center (CNHDRC), National Health Commission (WKZX2023CX14001).

DISCLOSURE

The authors declare that they have no competing interests regarding the publication of this work.

Approval of the research protocol: N/A.

Informed Consent: N/A.

Approval date of Registry and the Registration number of the study: N/A.

Animal Studies: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study utilized publicly available data from the Global Burden of Disease (GBD) database, which does not include confidential or personally identifiable information. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The Institutional Review Board of the University of Washington reviewed and approved a waiver of informed consent for GBD studies (https://ghdx.healthdata.org/gbd‐2023).

AUTHOR CONTRIBUTIONS

Qiongfang Zhang: Conceptualization, Data analysis, Manuscript writing – original draft. Huan Wang: Data collection, Methodology, Manuscript writing – review and editing. Mei Sun: Data collection, manuscript writing – review and editing. Pan Xie and Yi Wu: Conceptualization, Supervision, Manuscript writing – review and editing, Funding acquisition. All authors have read and approved the final manuscript.

DATA AVAILABILITY STATEMENT

All data used in this study are publicly available and can be accessed from the Global Health Data Exchange website, provided by the Institute for Health Metrics and Evaluation (https://ghdx.healthdata.org/gbd‐2023).