GRAPHICAL ABSTRACT

Highlights

• The prevalence and incidence of T1DM-CKD have risen globally in young populations.

• Mortality and DALYs of T1DM-CKD declined among young populations.

• Epidemiological trends of T1DM-CKD vary by gender, region, and SDI.

• Projections show shifting disease patterns with lower prevalence.

INTRODUCTION

Chronic kidney disease (CKD) is a progressive disease characterized by gradual renal function deterioration [1]. Among its various causes, type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) are particularly significant [2,3]. Among patients with T1DM, the risk of CKD is markedly elevated due to hyperglycemia-induced renal microvascular damage, leading to T1DM related CKD (T1DM-CKD) [3,4]. Approximately 30% to 40% of T1DM patients develop T1DM-CKD [2]. Simultaneously, T1DM-CKD significantly elevates the risk of cardiovascular disease (CVD) and all-cause mortality, consequently increasing the economic burden on patients and the healthcare systems [5-7].

Recent studies have indicated a persistent increase in the burden of T1DM among global youth populations [8-10]. Adolescents and young adults (aged 15 to 39 years) with T1DM are currently in a critical phase of their physical and social development, and they also contend with health complications and societal pressures [11-13]. Moreover, the prevalence of T1DM-CKD is elevated in young patients, but relevant studies remain limited [9,14]. The Global Burden of Disease (GBD) study, which offers extensive data on prevalence, incidence, mortality and disability-adjusted life years (DALYs), has proven invaluable in analyzing the global burden of various diseases [15-17].

This study aimed to systematically analyze the global burden of T1DM-CKD among adolescents and young adults, and forecast future trends using GBD study 2021 [16]. Our findings indicated a significant increase in the prevalence and incidence of T1DM-CKD, coupled with a decrease in mortality and DALYs. Notably, the trends varied considerably across genders, sociodemographic index (SDI), geographical region, and country. High fasting blood glucose still persisted as the primary risk factor for T1DM-CKD, whereas exposure to extreme temperatures significantly contributed to severe disease burden. The forecast analysis suggested a continued global increase in T1DM-CKD cases by 2035.

METHODS

Data source

This study used data from the GBD study 2021. It is the largest and most comprehensive global observational epidemiological database, covering the global burden of 371 diseases, injuries and 88 risk factors in 204 countries and regions from 1990 to 2021. The GBD study 2021 is publicly available via the Global Health Data Exchange (GHDx) query tool (http://ghdx. healthdata.org/GBD-results-tool). This interactive platform provides comprehensive health metrics including prevalence, incidence, mortality, years of life lost due to premature death, years of life lived with disability, DALYs and their respective rates of change. The methodological framework, including research objectives, data acquisition protocols, and analytical approaches of the GBD study, have been thoroughly detailed in previous literatures [18-20]. Based on etiological factors, CKD was stratified into five distinct categories: T1DM, T2DM, hypertension, glomerulonephritis, and other unspecified causes. T1DM-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) [18,19]. For mortality estimation, it was defined (ICD-10: E10.2) [20]. Three standardized tools were mainly used in the GBD 2021 study: Cause of Death Ensemble model (CODEm), spatiotemporal Gaussian process regression (ST-GPR), and Disease Modelling Meta-Regression version 2.1 (DisMod-MR 2.1) for consistency across regions and time. Detailed descriptions of these methods can be found in literature [18,19].

Study population

Our study constituted a secondary analysis of estimates derived from the GBD 2021 study. Utilizing the GHDx query tool (https://vizhub.healthdata.org/gbd-results/), we extracted epidemiological metrics, including prevalence, incidence, mortality, and DALYs rates, for T1DM-CKD in the 15 to 39 years age group from 1990 to 2021, and DALYs for associated risk factors.

Sociodemographic index

The SDI is a composite measure, calculated based on factors such as fertility rate, education level, and per capita income, to assess the socioeconomic development level of a country or region. Its range is 0–1, with higher values indicating higher levels of socioeconomic development. The countries and regions are divided into five SDI areas (low, low-middle, middle, high-middle, and high) to explore the relationship between the burden of T1DM-CKD among adolescents and young adults and socioeconomic development [18,19].

Statistical analysis

Descriptive analysis

Age-standardized rate (ASR) is an important statistical tool that allows for the comparison of disease incidence or mortality rates across different geographic regions and time periods by applying a standard global population structure. We calculated the ASR of T1DM-CKD per 100,000 and reported the 95% confidence interval (CI) according to the GBD algorithm [21].

Joinpoint regression analysis

To assess the time trends of the age-standardized prevalence rate (ASPR), age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR), and age-standardized disability-adjusted life years rate (ASDR), we employed joinpoint regression analysis. The average annual percentage change (AAPC) and 95% CI were calculated to measure time trends. The criteria for determining trend changes were increase (AAPC >0 and 95% CI not including 0), decrease (AAPC <0 and 95% CI not including 0), or stable (95% CI including 0) [22]. Additionally, we used Pearson correlation analysis to evaluate the correlation between SDI and population indicators and AAPC of T1DM-CKD to assess the strength and direction of their linear relationship in different regions.

Bayesian age-period-cohort model

The Bayesian age-period-cohort (BAPC) model is an advanced statistical tool that integrates prior knowledge of unknown parameters with sample data to estimate posterior distributions and infer these parameters. The BAPC model has shown high accuracy in predicting disease burden [23]. We used the R packages BAPC and integrated nested Laplace approximation (INLA) to predict global disease trends of T1DM-CKD across all age groups and among individuals aged 15 to 39 years from 2022 to 2035. All data analyses were conducted using the open-source software R version 4.3.3 (R Foundation for Statistical Computing, Vienna, Austria).

Ethics approval and consent to participate

All analyses we performed were based on publicly available summary statistics, which did not require additional ethical approval and consent.

RESULTS

Global trends of T1DM-CKD among adolescents and young adults

From 1990 to 2021, the prevalence cases of T1DM-CKD among adolescents and young adults increased by 107.5%, reaching 3.32 million, accounting for more than 50% of all age groups (Fig. 1A), while the proportion of incidence rose from 8.42% to 14.92% (Fig. 1B). However, the proportion of mortality and DALYs significantly decreased, consistent with the global trend of the disease (Fig. 1C and D).

In 2021, the ASPR of T1DM-CKD aged 15 to 39 rose to 111.44 per 100,000 (AAPC, 1.33%) (Table 1). Similarly, the ASIR increased to 0.48 per 100,000 (AAPC, 2.19%) (Table 1). However, the ASMR decreased from 0.65 to 0.50 per 100,000 (AAPC, –0.87%), and ASDR declined from 39.45 to 30.61 per 100,000 (AAPC, –0.83%) (Supplementary Table 1).

Notably, compared to other age groups (0–14, 40–64, ≥65 years), the 15–39 years cohort experienced the most substantial increases in both ASPR and ASIR, with AAPCs of 1.33% and 2.19%, respectively (Supplementary Table 2). The result suggested that the young population was facing a severe burden of T1DM-CKD. Furthermore, between 1990 and 2021, the ASIR of T1DM-CKD aged 15–39 showed a notable increase compared to T1DM and other CKD subtypes, with an AAPC reaching 2.19% (Supplementary Fig. 1).

Global trends of T1DM-CKD among adolescents and young adults by genders

Between 1990 and 2021, there were significant gender differences in T1DM-CKD among adolescents and young adults. Except for ASPR, other indicators (ASIR, ASMR, ASDR) were higher in males than in females (Table 1, Fig. 2). By 2021, the ASPR of T1DM-CKD was higher in females (141.48 per 100,000) than in males (82.22 per 100,000) (Table 1). However, the AAPC of ASPR in females (AAPC, 1.31%) was slightly lower than that in males (AAPC, 1.38%) (Table 1, Fig. 2A). The ASIR was lower in females (0.37 per 100,000) than in males (0.58 per 100,000) and the AAPC in females (1.94%) was lower compared to males (AAPC, 2.36%) (Table 1, Fig. 2B). Interestingly, the AAPC of ASMR showed a greater decline in females (–1.37%) than in males (–0.47%). Similarly, the decrease in ASDR was more pronounced in females (AAPC, –1.31%) than in males (AAPC, –0.45%) (Fig. 2C and D, Supplementary Table 1).

SDI regional trends of T1DM-CKD among adolescents and young adults

From 1990 to 2021, the ASPR and ASIR of T1DM-CKD among adolescents and young adults showed an increasing trend across all SDI subgroups (Fig. 3A and B). For prevalence, the ASPR of T1DM-CKD in this age group was the highest in the high SDI region (144.51 per 100,000) and lowest in the low SDI region (92.59 per 100,000) (Table 1). The most significant increase in ASRP was observed in the high-middle SDI region (AAPC, 2.05%), while the smallest increase in ASPR occurred in the high SDI region (AAPC, 0.85%) (Supplementary Fig. 2A). Regarding incidence, the ASIR for T1DM-CKD in this age group was highest in the middle SDI region (0.54 per 100,000) and lowest in low SDI region (0.39 per 100,000) (Table 1). Furthermore, the increase was highest in high-middle SDI region (AAPC, 2.99%) and lowest in high SDI region (AAPC, 1.15%) (Supplementary Fig. 2B).

In contrast to the increasing trends in prevalence and incidence, the ASMR and ASDR of T1DM-CKD in this population showed a decreasing trend across all SDI subgroups from 1990 to 2021 (Fig. 3C and D). In 2021, both ASMR and ASDR were the highest in the middle SDI region (ASMR, 0.76 per 100,000; ASDR, 45.70 per 100,000) and the lowest in the high SDI region (ASMR, 0.16 per 100,000; ASDR, 11.49 per 100,000) (Supplementary Table 1). Notably, the most rapid declines in both ASMR (AAPC, –1.43%) and ASDR (AAPC, –1.3%) were observed in the high-middle SDI region (Supplementary Fig. 2C and D).

Geographic regional trends of T1DM-CKD among adolescents and young adults

Between 1990 and 2021, the prevalence and incidence of T1DM-CKD among adolescents and young adults exhibited an upward trend across 21 regions worldwide (Table 1). In 2021, Eastern Europe demonstrated the highest ASPR (318.85 per 100,000) and a rapid increase (AAPC, 3.1%) (Supplementary Fig. 3A and B). Central Asia reported the highest ASIR (1.88 per 100,000), while the most significant rise was in Eastern Europe (AAPC, 4.2%) (Supplementary Fig. 3C and D). Regarding mortality and DALYs, Oceania had the highest ASMR (2.49 per 100,000) and ASDR (148.14 per 100,000) in 2021(Supplementary Table 1, Supplementary Fig. 3E and G). The most rapid increases were observed in Central Latin America for ASMR (AAPC, 1.63%) and in Australasia for ASDR (AAPC, 1.62%) (Supplementary Fig. 3F and H).

National trends of T1DM-CKD among adolescents and young adults

Data from 204 countries between 1990 and 2021 indicated that there were 80 countries experienced an ASPR higher than the global average, and 124 countries had an ASPR lower than the global average (Supplementary Table 3). By 2021, Canada (553.51 per 100,000) had the highest ASPR, while China (21.73 per 100,000) was the lowest (Fig. 4A). Ireland (AAPC, 3.99%) displayed the most rapid growth (Fig. 4B). In terms of incidence until 2021, among the 204 countries analyzed, 69 countries presented an ASIR above the global average, while 135 were below (Supplementary Table 3). Bulgaria demonstrated the highest ASIR (2.83 per 100,000), with China exhibiting the lowest at 0.06 per 100,000 (Fig. 4C). Albania demonstrated the fastest increase in ASIR (AAPC, 5.16%) (Fig. 4D).

Regarding mortality, countries with middle to high SDI tended to have higher ASMRs in 2021, with 52 countries above the global average and 152 below (Supplementary Table 4). American Samoa (8.37 per 100,000), Nauru (7.37 per 100,000), and the Marshall Islands (7.02 per 100,000) exhibited the top three ASMRs, while Norway (0.01 per 100,000) demonstrated the lowest (Supplementary Fig. 4A). Ukraine (AAPC, 9.79%), Armenia (AAPC, 4.36%), and Lesotho (AAPC, 3.76%) had the fastest growth, whereas Poland (AAPC, –4.60%), Singapore (AAPC, –3.69%), and Republic of Korea (AAPC, –3.47%) displayed the most substantial decreases (Supplementary Fig. 4B). For DALYs, T1DM-CKD in this age group increased in ASDR in 65 countries and decreased in ASDR in 139 countries globally, with the fastest growing and largest decreasing countries consistent with ASMR (Supplementary Table 4, Supplementary Fig. 4). American Samoa had the highest ASDR (490.59 per 100,000), with Iceland having the lowest at 1.46 per 100,000 (Supplementary Fig. 4C). Ukraine demonstrated the fastest increase in ASDR with an AAPC of 6.91% (Supplementary Fig. 4D).

Factors influencing the AAPC in the global burden of T1DM-CKD

A positive correlation was found between countries’ SDI levels and ASPR or ASIR in 2021 (P<0.01) (Supplementary Fig. 5A and B). Furthermore, we found that the AAPCs of ASPR and ASIR were positively correlated with the SDI levels (P<0.05) (Supplementary Fig. 6A and B). These findings suggest that the increasing trends in ASPR and ASIR for T1DM-CKD are closely linked to regional SDI levels. However, no significant correlations were observed between SDI levels and ASMR or ASDR, nor between SDI levels and their corresponding AAPCs (Supplementary Figs. 5C and D, 6C and D).

Risk factors for T1DM-CKD among adolescents and young adults

In this study, we analyzed the association between DALYs and various risk factors for T1DM-CKD among adolescents and young adults, including high fasting glucose, environmental/occupational risks, and temperature extremes (hypothermia and hyperthermia) (Supplementary Table 5). The results showed that high fasting blood glucose remained the major risk factor for T1DM-CKD among adolescents and young adults. In young T1DM-CKD patients, the ASDR due to high fasting glucose was 16.99 per 100,000 in 2021, but showed a decreasing trend (AAPC, –0.08%). Notably, high SDI countries experienced the most rapid increase in heat risk from 0.05 per 100,000 in 1990 to 0.25 per 100,000 in 2021 (AAPC, 5.39%). Globally, environmental/occupational risks decreased from 2.26 per 100,000 to 1.44 per 100,000 (AAPC, –1.41%), across most SDI regions. However, a slight increase was observed in high SDI regions, where the rates rose from 0.73 per 100,000 to 0.82 per 100,000 (Supplementary Table 5).

Forecasts of global burden of T1DM-CKD among adolescents and young adults

The forecast analysis indicated that the prevalence, incidence, mortality and DALYs of T1DM-CKD are expected to continue to increase by 2035 in all age groups compared to 2021(Fig. 5A). Among adolescents and young adults, the number of T1DM-CKD cases was expected to decline to 2.864 million by 2035, with an ASPR of 89.67 per 100,000 (Fig. 5A, Supplementary Table 6). The cases of incidence and ASIR showed a slight increasing trend, while mortality cases, ASMR, DALYs, and ASDR demonstrated modest declines (Fig. 5B-D). Moreover, by 2035, the 15 to 39 age group would still maintain over 40% of the total prevalence across all ages, with incidence proportion increasing to 16%. Notably, both mortality and DALYs proportions displayed downward trends (Fig. 5E).

DISCUSSION

The increasing prevalence of T1DM-related complications in recent years has led to growing attention on young patients with T1DM-CKD [24,25]. This study demonstrated a significant upward trend in the burden of T1DM-CKD among adolescents and young adults from 1990 to 2021. This age group accounted for over half of the global prevalence of T1DM-CKD, and its prevalence and incidence were projected to continue to increase. Global epidemiological studies have observed a trend towards earlier onset of T1DM, which may significantly contribute to the rising burden of T1DM-CKD among young adults [8,9,26]. Data reported from Philadelphia indicated that the incidence of T1DM in children exhibited a steadily increasing trend from 2000 to 2004, with the incidence in children under 5 years old increasing by 70% [27]. This trend may be attributed to a variety of factors, including environmental changes, genetic susceptibility and lifestyle changes [28-30]. For instance, environmental factors such as early exposure to viral infections, air pollution, and exposure to modern chemicals might increase the risk of T1DM among young children [28,29]. Furthermore, alterations in dietary structure might also affect the development of the immune system and promote the early onset of T1DM, such as premature intake of infant formula or cow’s milk [30]. Additionally, the overweight and obesity have increased significantly globally, which not only increases the incidence of T1DM in children but also contributes to the burden of T1DM-CKD among the young population [31,32].

Significant gender differences existed in the burden of T1DM-CKD among individuals aged 15 to 39 years. The incidence, mortality, and DALYs were notably higher in males, potentially due to gender-specific factors such as lifestyle choices, smoking, blood pressure management, and hormonal variations [33,34]. Furthermore, male patients with T1DM-CKD have a higher risk of developing severe complications like end-stage renal disease (ESRD) compared to females [35,36]. Consequently, males display a poorer prognosis. Conversely, the prevalence of T1DM-CKD was significantly higher in females. While this observation aligns with previous findings, the data found in this study differ from those of previous reports [37]. This discrepancy may be due to methodological changes in the GBD study 2021, including participant inclusion criteria adjustments and data modeling alterations [19,21]. Studies have found that female T1DM patients face challenges in controlling glycemic levels, body mass index, and managing incidences of hypoglycemia and diabetic ketoacidosis [34,36,38]. These challenges might diminish the protective effects of female hormones, resulting in a higher prevalence of T1DM-CKD among young females. Further studies are necessary to elucidate the potential mechanisms.

Beyond gender, the SDI also significantly impacted the global burden of T1DM-CKD. As an indicator of socioeconomic development levels, the SDI elucidates the characteristics of the burden of disease in different regions. For instance, regions with low SDI primarily suffer from communicable, maternal, neonatal, and nutritional diseases (CMNNs), while regions with high SDI have a severe burden of noncommunicable diseases [15,19,39]. The burden of T1DM-CKD, a chronic metabolic noncommunicable disease, is more pronounced in developed regions [37]. Our study demonstrated that regions with high-middle and middle SDI experienced the highest prevalence and incidence of T1DM-CKD, yet these regions had a significant reduction in mortality and DALYs. Notably, the prevalence and incidence of T1DM-CKD was rapidly increasing in regions with low-middle and low SDI, likely driven by global T1DM trends and regional disparities in health policies [8]. These results underscore the need to enhance prevention and control strategies of T1DM-CKD across different regions.

T1DM-CKD is a complex disease influenced by multiple factors [2]. In this study, despite a reduction in DALYs associated with high fasting glucose, it persisted as a predominant risk factor for T1DM-CKD. This finding indicates the essential role of glycemic control in disease management, and reaffirms the critical role of glycemic control in preventing and decelerating the progression of T1DM-CKD [40,41]. Furthermore, our research revealed that extreme temperatures (low and high) significantly influenced the DALYs of T1DM-CKD, while the high temperature was associated with substantial increases in DALYs. This finding is consistent with recent studies on the impact of extreme temperature on CKD [42,43]. Temperature changes would affect insulin sensitivity [44]. Moreover, extreme temperature exacerbates the risk of diabetes mellitus, possibly because extreme temperature would increase the incidence of obesity, insulin resistance, and CVD [45,46]. However, since the data of risk are estimated and bases on the cross-sectional design in the GBD database [18], the correlation between extreme temperature and T1DM-CKD should be cautiously interpreted. Future cohort studies incorporating multivariate analysis are necessary to substantiate the impact of extreme temperatures on T1DM-CKD.

Remarkably, the projection model demonstrated an upward trend in the prevalence and incidence among young patients with T1DM-CKD, reflecting a significance towards younger onset. This highlights the critical need for improved screening, early diagnosis, and prompt intervention in young T1DMCKD patients. Nevertheless, the forecasts also disclosed encouraging trends: The prevalence, mortality and DALYs associated with T1DM-CKD in the 15 to 39 age group continued to show a declining trend. Furthermore, their proportion relative to global indicators also contracted. This improvement may be attributed to advancements in medical technology, enhanced patient education and optimized allocation of healthcare resources worldwide. Additionally, the promising trend may be attributed to the introduction of sodium-dependent glucose transporter 2 (SGLT2) inhibitors, a game-changing treatment for diabetic nephropathy and CKD [47]. SGLT2 inhibitors have demonstrated the potential to not only reduce the risk of CKD in patients with DM by 30% to 49%, but also to significantly decrease the risk of ESRD and cardiovascular events [48,49].

This study has several limitations. (1) Data availability and quality vary across regions. (2) Owing to the complexity of T1DM-CKD, certain potential risk factors or interactions may not have been thoroughly considered, and their varied impact on different subgroups (e.g., age, genders) might be overlooked. (3) Since the data of GBD study 2021 were derived from systematic data processing and modeling methods, it is not feasible to access the source data for multivariate corrections. Future research should delve into these intricate relationships to enable more precise risk assessments and intervention strategies.

In conclusion, the global burden of T1DM-CKD among individuals aged 15 to 39 years had increased from 1990 to 2021 and was projected to rise further by 2035. Notable disparities in the disease burden were observed across gender, SDI levels, and geographic regions. This study provides critical scientific evidence for improving the prevention and treatment of T1DM-CKD in young populations globally. It underscores the importance of early diagnosis and intervention among young patients with T1DM-CKD. Additionally, future research should delve into the underlying mechanisms of these disparities to develop more precise and personalized intervention strategies.

SUPPLEMENTARY MATERIALS

NOTES

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTIONS

Conception or design: X.Q., C.L.

Acquisition, analysis, or interpretation of data: X.Q.

Drafting the work or revising: all authors.

Final approval of the manuscript: all authors.

FUNDING

This work was supported by grants from the National Natural Science Foundation of China (grant number 81870622), the Hunan Provincial Natural Science Foundation of China (grant number 2022JJ30828 and 2023JJ30747), grants from Hunan Provincial Clinical Medicine Research Center for Intelligent Management of Chronic Disease (grant number 2023SK4042), Hunan Provincial Health High-level Talents Program, the Fundamental Research Funds for the Central Universities of Central South University (grant number 2022ZZTS0947 and 2023ZZTS0585), Degree& Postgraduate Education Reform Project of Central South University (grant number 512190112) and Scientific Research Project of Hunan Provincial Health Commission (grant number C202303067096), Bethune Charitable Foundation, BCF (grant number GX2021B04), Central Subsidy for Prevention and Control of Major Infectious Diseases and HANS CARING Inc.

ACKNOWLEDGMENTS

All analyses in this manuscript were conducted using data provided by the IHME and curated by the GBD core team. The authors gratefully acknowledge the contributions of the GBD Study 2021 collaborators.

All data are available in the corresponding databases (http://ghdx.healthdata.org/GBD-results-tool). The code used is available from the authors on request.

Fig. 1.

The changes in the case proportion of prevalence, incidence, mortality, and disability-adjusted life years (DALYs) among type 1 diabetes mellitus related chronic kidney disease aged 15 to 39 years from 1990 to 2021. (A) Case proportion of prevalence, (B) case proportion of incidence, (C) case proportion of mortality, and (D) case proportion of DALYs.

Fig. 2.

Temporal trend of age-standardized prevalence, incidence, mortality, and disability-adjusted life years (DALYs) rates of type 1 diabetes mellitus related chronic kidney disease aged 15 to 39 years at global from 1990 to 2021 by sex. (A) Age standardized prevalence rate (ASPR), (B) age standardized incidence rate (ASIR), (C) age standardized mortality rate (ASMR), and (D) age standardized disability-adjusted life years rate (ASDR).

Fig. 3.

Temporal trend of age-standardized rate of type 1 diabetes mellitus related chronic kidney disease aged 15 to 39 years at sociodemographic index (SDI) levels from 1990 to 2021 by sex. (A) Age standardized prevalence rate (ASPR), (B) age standardized incidence rate (ASIR), (C) age standardized mortality rate (ASMR), and (D) age standardized disability-adjusted life years rate (ASDR). DALY, disability-adjusted life year.

Fig. 4.

Age standardized prevalence, incidence rates and average annual percentage change (AAPC) among type 1 diabetes mellitus related chronic kidney disease aged 15 to 39 years in 204 countries and territories between 1990 and 2021. (A) Age standardized prevalence rate (ASPR), (B) AAPC of age standardized prevalence rate, (C) age standardized incidence rate (ASIR), and (D) AAPC of incidence rate.

Fig. 5.

Global type 1 diabetes mellitus related chronic kidney disease burden projections 1990 to 2035: all ages, ages 15–39, and proportion of 15–39 cases. (A) Number of cases and age-standardized prevalence rate, (B) number of cases and age-standardized incidence rate, (C) number of cases and age-standardized mortality rate, (D) number of cases and age-standardized disability-adjusted life years (DALYs) rates, and (E) proportion of cases in prevalence, incidence, mortality, and DALYs.

REFERENCES

• 1. Kalantar-Zadeh K, Jafar TH, Nitsch D, Neuen BL, Perkovic V. Chronic kidney disease. Lancet 2021;398:786-802.ArticlePubMed
• 2. Heerspink HJ, Cherney DZ, Groop PH, Matthieu C, Rossing P, Tuttle KR, et al. People with type 1 diabetes and chronic kidney disease urgently need new therapies: a call for action. Lancet Diabetes Endocrinol 2023;11:536-40.ArticlePubMed
• 3. Popovic DS, Patoulias D, Gnudi L, Mantzoros CS. Diabetic kidney disease in type 1 diabetes: challenges and differences from type 2 diabetes. Metabolism 2024;151:155763.ArticlePubMed
• 4. Chen Y, Lee K, Ni Z, He JC. Diabetic kidney disease: challenges, advances, and opportunities. Kidney Dis (Basel) 2020;6:215-25.ArticlePubMedPMCPDF
• 5. Verges B. Cardiovascular disease in type 1 diabetes, an underestimated danger: epidemiological and pathophysiological data. Atherosclerosis 2024;394:117158.ArticlePubMed
• 6. Bakris GL, Molitch M. Are all patients with type 1 diabetes destined for dialysis if they live long enough?: probably not. Diabetes Care 2018;41:389-90.ArticlePubMedPDF
• 7. de Boer IH, Bakris GL. Diabetic kidney disease: a determinant of cardiovascular risk in type 1 diabetes. Diabetes Care 2018;41:662-3.ArticlePubMedPMCPDF
• 8. Gong B, Yang W, Xing Y, Lai Y, Shan Z. Global, regional, and national burden of type 1 diabetes in adolescents and young adults. Pediatr Res 2024 Mar 5 [Epub]. https://doi.org/10.1038/s41390-024-03107-5.ArticlePubMed
• 9. Francis A, Harhay MN, Ong AC, Tummalapalli SL, Ortiz A, Fogo AB, et al. Chronic kidney disease and the global public health agenda: an international consensus. Nat Rev Nephrol 2024;20:473-85.ArticlePubMedPDF
• 10. Lawrence JM, Yi-Frazier JP, Black MH, Anderson A, Hood K, Imperatore G, et al. Demographic and clinical correlates of diabetes-related quality of life among youth with type 1 diabetes. J Pediatr 2012;161:201-7.ArticlePubMedPMC
• 11. Ma Z, He W, Zhou Y, Mai L, Xu L, Li C, et al. Global burden of stroke in adolescents and young adults (aged 15-39 years) from 1990 to 2019: a comprehensive trend analysis based on the global burden of disease study 2019. BMC Public Health 2024;24:2042.ArticlePubMedPMCPDF
• 12. Chao AM, Minges KE, Park C, Dumser S, Murphy KM, Grey M, et al. General life and diabetes-related stressors in early adolescents with type 1 diabetes. J Pediatr Health Care 2016;30:133-42.ArticlePubMedPMC
• 13. Anderson BJ, Laffel LM, Domenger C, Danne T, Phillip M, Mazza C, et al. Factors associated with diabetes-specific health-related quality of life in youth with type 1 diabetes: the global TEENs study. Diabetes Care 2017;40:1002-9.ArticlePubMedPMCPDF
• 14. Bello AK, Okpechi IG, Levin A, Ye F, Damster S, Arruebo S, et al. An update on the global disparities in kidney disease burden and care across world countries and regions. Lancet Glob Health 2024;12:e382. -95.PubMed
• 15. GBD 2021 Forecasting Collaborators. Burden of disease scenarios for 204 countries and territories, 2022-2050: a forecasting analysis for the Global Burden of Disease Study 2021. Lancet 2024;403:2204-56.PubMedPMC
• 16. Murray CJ; GBD 2021 Collaborators. Findings from the global burden of disease study 2021. Lancet 2024;403:2259-62.ArticlePubMed
• 17. Murray CJ. The global burden of disease study at 30 years. Nat Med 2022;28:2019-26.ArticlePubMedPDF
• 18. GBD 2021 Risk Factors Collaborators. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024;403:2162-203.PubMedPMC
• 19. GBD 2021 Diseases and Injuries Collaborators. Global incidence, prevalence, years lived with disability (YLDs), disabilityadjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024;403:2133-61.PubMedPMC
• 20. GBD 2021 Causes of Death Collaborators. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the global burden of disease study 2021. Lancet 2024;403:2100-32.PubMedPMC
• 21. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet 2020;396:1204-22.PubMedPMC
• 22. Kim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med 2000;19:335-51.ArticlePubMed
• 23. Chen J, Li C, Bu CL, Wang Y, Qi M, Fu P, et al. Global burden of non-communicable diseases attributable to kidney dysfunction with projection into 2040. Chin Med J (Engl) 2024 May 28 [Epub]. https://doi.org/10.1097/CM9.0000000000003143.ArticlePubMed
• 24. Baek JH, Lee WJ, Lee BW, Kim SK, Kim G, Jin SM, et al. Age at diagnosis and the risk of diabetic nephropathy in young patients with type 1 diabetes mellitus. Diabetes Metab J 2021;45:46-54.PubMed
• 25. Chan JC, O CK, Luk AO. Young-onset diabetes in east Asians: from epidemiology to precision medicine. Endocrinol Metab (Seoul) 2024;39:239-54.ArticlePubMedPMCPDF
• 26. Weng J, Zhou Z, Guo L, Zhu D, Ji L, Luo X, et al. Incidence of type 1 diabetes in China, 2010-13: population based study. BMJ 2018;360:j5295.ArticlePubMedPMC
• 27. Lipman TH, Levitt Katz LE, Ratcliffe SJ, Murphy KM, Aguilar A, Rezvani I, et al. Increasing incidence of type 1 diabetes in youth: twenty years of the Philadelphia pediatric diabetes registry. Diabetes Care 2013;36:1597-603.PubMedPMC
• 28. Zorena K, Michalska M, Kurpas M, Jaskulak M, Murawska A, Rostami S. Environmental factors and the risk of developing type 1 diabetes: old disease and new data. Biology (Basel) 2022;11:608.ArticlePubMedPMC
• 29. Op de Beeck A, Eizirik DL. Viral infections in type 1 diabetes mellitus: why the β cells? Nat Rev Endocrinol 2016;12:263-73.ArticlePubMedPMCPDF
• 30. Patelarou E, Girvalaki C, Brokalaki H, Patelarou A, Androulaki Z, Vardavas C. Current evidence on the associations of breastfeeding, infant formula, and cow’s milk introduction with type 1 diabetes mellitus: a systematic review. Nutr Rev 2012;70:509-19.ArticlePubMed
• 31. Hill CJ, Cardwell CR, Maxwell AP, Young RJ, Matthews B, O’Donoghue DJ, et al. Obesity and kidney disease in type 1 and 2 diabetes: an analysis of the national diabetes audit. QJM 2013;106:933-42.ArticlePubMed
• 32. Wrzosek M, Wisniewska K, Sawicka A, Talalaj M, Nowicka G. Early onset of obesity and adult onset of obesity as factors affecting patient characteristics prior to bariatric surgery. Obes Surg 2018;28:3902-9.ArticlePubMedPMCPDF
• 33. Maric C. Sex, diabetes and the kidney. Am J Physiol Renal Physiol 2009;296:F680-8.ArticlePubMedPMC
• 34. Giandalia A, Giuffrida AE, Gembillo G, Cucinotta D, Squadrito G, Santoro D, et al. Gender differences in diabetic kidney disease: focus on hormonal, genetic and clinical factors. Int J Mol Sci 2021;22:5808.ArticlePubMedPMC
• 35. Mollsten A, Svensson M, Waernbaum I, Berhan Y, Schon S, Nystrom L, et al. Cumulative risk, age at onset, and sex-specific differences for developing end-stage renal disease in young patients with type 1 diabetes: a nationwide population-based cohort study. Diabetes 2010;59:1803-8.PubMedPMC
• 36. Harjutsalo V, Maric C, Forsblom C, Thorn L, Waden J, Groop PH, et al. Sex-related differences in the long-term risk of microvascular complications by age at onset of type 1 diabetes. Diabetologia 2011;54:1992-9.ArticlePubMedPDF
• 37. Deng Y, Li N, Wu Y, Wang M, Yang S, Zheng Y, et al. Global, regional, and national burden of diabetes-related chronic kidney disease from 1990 to 2019. Front Endocrinol (Lausanne) 2021;12:672350.ArticlePubMedPMC
• 38. Manicardi V, Russo G, Napoli A, Torlone E, Li Volsi P, Giorda CB, et al. Gender-disparities in adults with type 1 diabetes: more than a quality of care issue: a cross-sectional observational study from the AMD annals initiative. PLoS One 2016;11:e0162960.ArticlePubMedPMC
• 39. GBD 2019 Viewpoint Collaborators. Five insights from the global burden of disease study 2019. Lancet 2020;396:1135-59.PubMedPMC
• 40. DeFronzo RA, Reeves WB, Awad AS. Pathophysiology of diabetic kidney disease: impact of SGLT2 inhibitors. Nat Rev Nephrol 2021;17:319-34.ArticlePubMedPDF
• 41. Neumiller JJ, Hirsch IB. Management of hyperglycemia in diabetic kidney disease. Diabetes Spectr 2015;28:214-9.ArticlePubMedPMCPDF
• 42. He L, Xue B, Wang B, Liu C, Gimeno Ruiz de Porras D, Delclos GL, et al. Impact of high, low, and non-optimum temperatures on chronic kidney disease in a changing climate, 1990-2019: a global analysis. Environ Res 2022;212(Pt A):113172.ArticlePubMedPMC
• 43. 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.ArticlePubMed
• 44. Morton GJ, Muta K, Kaiyala KJ, Rojas JM, Scarlett JM, Matsen ME, et al. Evidence that the sympathetic nervous system elicits rapid, coordinated, and reciprocal adjustments of insulin secretion and insulin sensitivity during cold exposure. Diabetes 2017;66:823-34.ArticlePubMedPMCPDF
• 45. Li Y, Lan L, Wang Y, Yang C, Tang W, Cui G, et al. Extremely cold and hot temperatures increase the risk of diabetes mortality in metropolitan areas of two Chinese cities. Environ Res 2014;134:91-7.ArticlePubMed
• 46. Vallianou NG, Geladari EV, Kounatidis D, Geladari CV, Stratigou T, Dourakis SP, et al. Diabetes mellitus in the era of climate change. Diabetes Metab 2021;47:101205.ArticlePubMed
• 47. Nuffield Department of Population Health Renal Studies Group; SGLT2 inhibitor Meta-Analysis Cardio-Renal Trialists’ Consortium. Impact of diabetes on the effects of sodium glucose co-transporter-2 inhibitors on kidney outcomes: collaborative meta-analysis of large placebo-controlled trials. Lancet 2022;400:1788-801.PubMedPMC
• 48. Perkovic V, Jardine MJ, Neal B, Bompoint S, Heerspink HJ, Charytan DM, et al. Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. N Engl J Med 2019;380:2295-306.ArticlePubMed
• 49. Heerspink HJ, Stefansson BV, Correa-Rotter R, Chertow GM, Greene T, Hou FF, et al. Dapagliflozin in patients with chronic kidney disease. N Engl J Med 2020;383:1436-46.ArticlePubMed

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Qu X, Liu C, Sun L, Sheng Z. Global Burden of Type 1 Diabetes Mellitus Related Chronic Kidney Disease among Adolescents and Young Adults, and Projections to 2035: Results from the Global Burden of Disease Study 2021. Diabetes Metab J. 2025 Mar 10. doi: 10.4093/dmj.2024.0544. Epub ahead of print.

Received: Sep 04, 2024; Accepted: Dec 12, 2024

DOI: https://doi.org/10.4093/dmj.2024.0544.

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