Trends and projections of the global burden of T2DM-associated CKD related to high BMI: A global burden of disease study 2021

Jing Zhang; Wenxuan Li; Zhen Sun; Yunyang Wang; Yu Xue; Ke Si; Yajing Huang; Wenshan Lv; Lili Xu; Yangang Wang

doi:10.1371/journal.pone.0333672

. 2025 Oct 10;20(10):e0333672. doi: 10.1371/journal.pone.0333672

Trends and projections of the global burden of T2DM-associated CKD related to high BMI: A global burden of disease study 2021

Jing Zhang ^1,^#, Wenxuan Li ^1,^#, Zhen Sun ², Yunyang Wang ¹, Yu Xue ¹, Ke Si ¹, Yajing Huang ¹, Wenshan Lv ¹, Lili Xu ¹, Yangang Wang ^1,^*

Editor: Claudio Alberto Dávila-Cervantes³

PMCID: PMC12513629 PMID: 41071800

Abstract

Background

Type 2 diabetic-associated chronic kidney disease (T2DM-Associated CKD), a leading cause of end-stage renal disease, is exacerbated by rising particularly high body mass index (BMI) rates. This study examines the global burden of T2DM-Associated CKD attributable to high BMI from 1990 to 2021 and projects future trends using the Global Burden of Disease (GBD) 2021 data.

Methods

GBD 2021 data from 204 countries were analyzed to assess mortality, disability-adjusted life years (DALYs) and corresponding age-standardized rates of T2DM-Associated CKD linked to high BMI. Bayesian Age-Period-Cohort modeling was used for projections, with stratification by age, gender, and Socio-Demographic Index (SDI). Statistical analyses were conducted using R software.

Results

In 2021, high BMI-related T2DM-Associated CKD caused 173,263 deaths and 4.3 million DALYs. Age-standardized rates declined globally but showed regional disparities, with Andean Latin America having the highest burden. Women had higher absolute burdens, while men showed higher standardized rates. Projections indicate continued increases in mortality and DALY rates through 2050. Emerging therapies, such as GLP-1 receptor agonists (RAs) and SGLT2 inhibitors (SGLT2i), could potentially alter these trends, especially in high-risk regions.

Conclusions

High BMI significantly drives the T2DM-Associated CKD burden, necessitating targeted overweight/obesity prevention and improved healthcare access, particularly in high-risk regions. Monitoring trends is crucial for effective interventions.

Introduction

In recent years, type 2 diabetes (T2D) and its associated complications, particularly type 2 diabetic-associated chronic kidney disease (T2DM-Associated CKD), have emerged as major global health concerns. T2DM-Associated CKD remains one of the most common and serious complications of T2D, contributing significantly to the rising burden of end-stage renal disease (ESRD). According to cohort studies on diabetes in European and American countries, it is estimated that more than half of T2D patients may develop T2DM-Associated CKD [1]. T2DM-Associated CKD causes more than 50% of all ESRD [2], placing a heavy burden on socio-economic and medical resources.

Meanwhile, the prevalence of overweight/obesity continues to rise worldwide and has become a significant public health issue [3]. For example, the United States, one of the countries with the highest prevalence of overweight/obesity and overweight, is expected to see the prevalence increase to nearly 50% and 30%, respectively, by 2030. It is estimated that the total direct healthcare and indirect costs of obese and overweight individuals in the United States in 2016 were $1.72 trillion [4]. Numerous studies have shown that a high BMI increases the risk of diabetic nephropathy (DN) [5,6]. A meta-analysis of observational studies showed that the risk of DN increased by 16% for every 5 kg/m2 increase in BMI [7]. Additionally, a Mendelian Randomization study showed a significant association between an increase of one standard deviation in BMI and a 2.76-fold increase in the risk of DN [5].

Recently, Tan et al. conducted a comprehensive analysis of the global burden of CKD attributable to high BMI from 1990 to 2021, revealing a significant increase in CKD mortality and disability-adjusted life years (DALYs). They also predicted that the burden of CKD will continue to grow by 2035, particularly in low- and middle-income countries [8]. In contrast, our study extends the projection horizon to 2050, offers a more granular stratification by age, sex, and region. By integrating data from GBD 2021, we aim to provide insights into potential public health interventions to address this growing challenge.

Methods

Study design and data sources

This study utilized data from the GBD 2021 study, which contains comprehensive data from 204 countries and regions. It covers 369 diseases and injuries, providing indicators such as prevalence, mortality, and DALYs at the global, regional, and national levels, as well as a comparative analysis of 87 risk factors [9]. The dataset covers the period from 1 January 1990–31 December 2021. The GBD data come from population censuses, household surveys, civil registration and vital statistics, disease notifications, disease registries, health service use, air pollution monitoring, satellite images and other sources [10]. These data sources provide reliable estimates of the burden of disease using standardized methods across regions and time periods.

We selected individuals aged 25 to ≥95, including males, females, and both sexes combined, to assess the global burden of disease attributed to high BMI-related T2DM-Associated CKD in 204 countries and territories from 1990 to 2021. The analysis included data on the number of deaths, DALYs, and their corresponding age-standardized rates (per 100,000 people), ensuring comprehensive coverage of trend analysis. In this study, T2DM-associated CKD was attributed based on GBD 2021 data. It should be noted that the GBD database does not provide indicators such as proteinuria, eGFR, or KDIGO staging to distinguish specific clinical phenotypes. Therefore, we used the term “T2DM-associated CKD” to avoid overinterpretation of clinical phenotypes. GBD data can be obtained from https://vizhub.healthdata.org/gbd-results/.

Definitions

For adults aged 20 and above, a high BMI is defined as a BMI of more than 25 kg/m². DALYs represent the sum of years of life lost due to premature mortality and disability, and a DALY can be regarded as all healthy life years lost. It is currently the most widely used and representative indicator for assessing and measuring the overall burden of disease [11]. The calculation of the uncertainty interval (UI) is based on 1000 sample estimates for each parameter. The range of the 95% UI is determined by the 25th and 975th values of the 1000 sorted estimates, which are used as the lower and upper limits of the interval, respectively. The Socio-demographic Index (SDI) is an index proposed in the World Development Report 2015 to reflect the development status of a geographical location. It is calculated based on the lagged distribution of per capita income, the average education level of the population aged 15 and above, and the total fertility rate of the population under 25 [9]. The 204 countries and territories are divided into five groups according to their SDI quintiles: high SDI (>0.805126), high-middle SDI (0.689504–0.805126), middle SDI (0.607679–0.689504), low-middle SDI (0.454743–0.607679) and low SDI (≤0.454743).

Statistical analysis

Joinpoint regression analysis.

We performed regression analyses using Joinpoint software (version 5.4.0; National Cancer Institute) to identify key turning points in trends in the burden of disease. Joinpoint regression analyses identify best-fit points, or ‘inflection points,’ by dividing the data into line segments and fitting regressions to each segment, and assesses the change in trend between the inflection points. An annual percentage change (APC) was calculated for each line segment, indicating the annual rate of change over a given time period. By weighting and averaging the APC of each segment, the estimation yields the average annual percentage change (AAPC), which reflects the overall trend of the entire time series. Statistical significance was tested by Monte Carlo permutation (MC permutation), and a p-value of <0.05 was considered statistically significant [12].

Decomposition analysis.

This study used decomposition analysis to assess the relative contributions of ageing, population, and epidemiological changes to the global burden of T2DM-Associated CKD attributable to high BMI. Ageing of the population was assessed by analyzing changes in the age structure – the effect of changes in the proportion of the elderly population on the burden of disease. Population was determined by changes in the overall size of the population. Epidemiological changes refer to changes in the incidence of T2DM-Associated CKD related to high BMI, which are independent of population dynamics [13].

Bayesian Age-Period-Cohort (BAPC).

We used the BAPC model to predict ASMR and ASDR from 2022 to 2050. The core idea is to use a Bayesian method to fit historical data to obtain the past distributions of age, period, and cohort effects, and then use Bayesian inference to extrapolate future disease burden trends using current data [14].

All analyses and graphical visualizations were performed using R software (version 4.3.0). P < 0.05 was considered statistically significant.

Ethical consideration

This study used publicly available data from the GBD study and therefore did not require ethical approval. No human participants were directly involved. All methods were performed in accordance with the relevant guidelines and regulations.

Results

In 2021, there were 173,263 deaths from T2DM-Associated CKD worldwide related to high BMI, with an ASMR of 2.07 per 100,000, an increase of 78.4% since 1990 (Table 1). Meanwhile, the number of DALYs has increased by 3125105 over the past 32 years, with an ASDR of 50.14 per 100,000 population in 2021 (Table 2). Among the 21 GBD regions, the highest T2DM-Associated CKD ASMR and ASDR associated with high BMI were in Andean Latin America (6.9 and 147.4 per 100,000 population, respectively), followed by Central Latin America (5.06 and 125.48 per 100,000 population, respectively) and the Caribbean (4.85 and 112.51 per 100,000 population, respectively). The regions with the lowest ASMR and ASDR are Eastern Europe (0.51 per 100,000 population) and Australasia (15.98 per 100,000 population). In terms of EAPC, between 1990 and 2021, ASMR EAPC and ASDR EAPC were positive everywhere except Central Europe, High-income Asia Pacific and Southern Latin America, implying that the burden of disease due to high BMI is increasing in the vast majority of regions. (Tables 1 and 2).

Table 1. Deaths and ASMR of T2DM-Associated CKD attributable to high BMI in 1990 and 2021 and the temporal trends from 1990 to 2021.

Death location	1990		2021		1990–2021
Death location	Deaths (95% UI)	ASMR per 100,000 (95% UI)	Deaths (95% UI)	ASMR per 100,000 (95% UI)	EAPC in ASMR (95% CI)
Southeast Asia	1833 (646, 3904)	0.79 (0.28, 1.68)	10263 (3643, 22044)	1.67 (0.59, 3.62)	2.53 (2.48, 2.58)
East Asia	8853 (3190, 18825)	1.33 (0.49, 2.77)	39725 (16343, 73073)	2 (0.81, 3.67)	1.26 (1.12, 1.39)
Global	40479 (17726, 67320)	1.16 (0.51, 1.92)	173263 (76311, 288454)	2.07 (0.91, 3.47)	2.02 (1.94, 2.11)
Oceania	74 (30, 136)	2.98 (1.18, 5.7)	287 (113, 506)	4.57 (1.76, 8.27)	1.27 (1.13, 1.4)
Central Asia	179 (86, 261)	0.39 (0.19, 0.59)	720 (374, 1085)	0.94 (0.48, 1.45)	2.28 (1.77, 2.8)
Central Europe	931 (469, 1336)	0.66 (0.33, 0.94)	1480 (732, 2376)	0.62 (0.31, 0.99)	−0.23 (−0.47, 0)
Eastern Europe	542 (256, 797)	0.2 (0.09, 0.29)	1859 (948, 2603)	0.51 (0.26, 0.71)	3.07 (2.66, 3.48)
High-income Asia Pacific	2197 (1028, 3989)	1.24 (0.57, 2.27)	7347 (3055, 13133)	1.13 (0.49, 1.98)	−0.25 (−0.38, −0.13)
Australasia	69 (36, 99)	0.32 (0.16, 0.45)	325 (161, 477)	0.53 (0.27, 0.78)	2.48 (1.98, 2.98)
Western Europe	4453 (2041, 6873)	0.74 (0.34, 1.13)	11387 (4914, 18310)	0.91 (0.39, 1.41)	1.1 (0.88, 1.32)
Southern Latin America	1313 (603, 1978)	3 (1.37, 4.52)	2551 (1173, 3911)	2.81 (1.29, 4.26)	0.08 (−0.29, 0.45)
High-income North America	4667 (2251, 6961)	1.29 (0.62, 1.88)	28244 (12799, 45533)	4.05 (1.86, 6.4)	4.02 (3.77, 4.27)
Caribbean	619 (257, 1060)	2.57 (1.07, 4.42)	2643 (1100, 4369)	4.85 (2.02, 7.98)	2.74 (2.52, 2.96)
Andean Latin America	812 (306, 1336)	4.38 (1.64, 7.27)	3938 (1682, 6305)	6.9 (2.93, 11.19)	1.41 (1.13, 1.7)
Central Latin America	1911 (843, 3178)	2.65 (1.14, 4.57)	12418 (5868, 19475)	5.06 (2.39, 8)	2.63 (2.11, 3.15)
Tropical Latin America	2090 (873, 3375)	2.63 (1.09, 4.4)	9399 (4315, 14026)	3.77 (1.72, 5.68)	1.14 (0.93, 1.35)
North Africa and Middle East	4419 (1942, 6922)	3.12 (1.31, 5)	16440 (7457, 25621)	4.17 (1.85, 6.63)	1.01 (0.89, 1.12)
South Asia	2914 (1216, 5660)	0.56 (0.24, 1.13)	15754 (6786, 28269)	1.12 (0.48, 2.03)	2.27 (2.19, 2.35)
Central Sub-Saharan Africa	366 (137, 710)	1.97 (0.72, 3.72)	1270 (467, 2385)	2.92 (1.04, 5.64)	1.07 (0.95, 1.18)
Eastern Sub-Saharan Africa	906 (314, 1963)	1.41 (0.5, 3.1)	3135 (1153, 5897)	2.26 (0.83, 4.37)	1.39 (1.32, 1.46)
Southern Sub-Saharan Africa	245 (95, 402)	1.02 (0.38, 1.74)	936 (365, 1567)	1.89 (0.73, 3.34)	2.5 (2.18, 2.83)
Western Sub-Saharan Africa	1086 (388, 1971)	1.46 (0.52, 2.79)	3141 (1377, 5301)	1.97 (0.82, 3.47)	0.7 (0.56, 0.85)

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Table 2. DALYs and ASDR of T2DM-Associated CKD attributable to high BMI in 1990 and 2021 and the temporal trends from 1990 to 2021.

DALY	1990		2021		1990–2021
Location	DALYs (95% UI)	ASDR per 100,000 (95% UI)	DALYs (95% UI)	ASDR per 100,000 (95% UI)	EAPC in ASDR (95% CI)
Global	1197972 (539562, 1925808)	30.82 (13.87, 49.54)	4323077 (1942948, 6820314)	50.14 (22.56, 79.15)	1.68 (1.61, 1.76)
Southeast Asia	58753 (20349, 126543)	21.99 (7.69, 47.03)	306887 (110217, 640064)	44.54 (15.93, 93.97)	2.45 (2.37, 2.53)
East Asia	261721 (93741, 564170)	31.37 (11.36, 66.67)	993095 (395037, 1787618)	45.95 (18.54, 82.57)	1.26 (1.12, 1.4)
Oceania	2439 (998, 4377)	77.67 (31.42, 141.79)	8561 (3387, 15057)	109.87 (43.34, 192.6)	1.03 (0.9, 1.17)
Central Asia	13048 (6594, 17962)	27.74 (13.97, 38.46)	33078 (18149, 45984)	39.17 (21.27, 55.11)	0.86 (0.58, 1.14)
Central Europe	32716 (17770, 44575)	21.97 (11.97, 29.66)	43859 (22920, 63967)	19.72 (10.44, 28.06)	−0.32 (−0.42, −0.21)
Eastern Europe	38076 (19365, 52454)	13.74 (6.86, 18.81)	65939 (36890, 87680)	18.41 (10.32, 24.6)	0.79 (0.69, 0.89)
High-income Asia Pacific	53801 (25195, 92291)	27.53 (12.7, 47.96)	127359 (55706, 222942)	25.17 (11.56, 43.11)	−0.15 (−0.27, −0.04)
Australasia	2803 (1462, 3827)	12.15 (6.35, 16.47)	8665 (4701, 11963)	15.98 (8.7, 21.88)	1.13 (0.86, 1.4)
Western Europe	125066 (56308, 182818)	21.34 (9.73, 30.8)	220490 (99501, 327778)	21.57 (9.99, 31.71)	0.17 (0.08, 0.27)
Southern Latin America	30034 (14170, 43185)	65.49 (30.74, 93.96)	51699 (25085, 74471)	58.73 (28.84, 84.13)	−0.09 (−0.41, 0.23)
Caribbean	15970 (7014, 25427)	62.07 (27.05, 99.53)	60834 (26524, 93605)	112.51 (49.26, 172.98)	2.55 (2.36, 2.73)
High-income North America	133408 (66239, 185639)	38.61 (19.45, 53.55)	607345 (285454, 911077)	94.03 (44.88, 138.06)	3.16 (2.94, 3.39)
Andean Latin America	19405 (7651, 29693)	96.96 (37.64, 151.31)	86513 (38663, 133617)	147.4 (65.4, 228.82)	1.3 (1.03, 1.57)
Central Latin America	53959 (25535, 83738)	65.91 (30.45, 104.61)	318450 (159394, 480567)	125.48 (62.43, 190.67)	2.52 (2.02, 3.02)
North Africa and Middle East	119890 (56801, 179674)	72.24 (33.46, 109)	423076 (210977, 631071)	93.06 (44.34, 140.97)	0.86 (0.82, 0.91)
Tropical Latin America	60216 (26267, 89549)	66.26 (28.34, 100.9)	219886 (104654, 313390)	85.51 (40.51, 122.33)	0.69 (0.48, 0.91)
South Asia	98184 (39673, 186530)	16.29 (6.6, 31.86)	496214 (217181, 861080)	32.31 (14.05, 56.5)	2.35 (2.27, 2.42)
Central Sub-Saharan Africa	11116 (4463, 21206)	49.23 (19.26, 94.05)	38651 (15190, 69360)	70.05 (26.82, 129.83)	0.95 (0.86, 1.05)
Eastern Sub-Saharan Africa	24353 (8486, 51559)	32.76 (11.45, 70.01)	82433 (31225, 148717)	49.93 (18.52, 92.38)	1.2 (1.14, 1.27)
Southern Sub-Saharan Africa	9270 (4178, 13819)	33.62 (14.58, 50.72)	30524 (14275, 48108)	52.65 (23.54, 84.77)	1.69 (1.46, 1.92)
Western Sub-Saharan Africa	33744 (13091, 57151)	38.94 (14.72, 67.27)	99519 (48176, 152321)	50.51 (23.27, 80.56)	0.67 (0.57, 0.77)

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Between 1990 and 2021, the number of deaths and DALYs associated with T2DM-Associated CKD related to high BMI and their corresponding age-standardized rates showed a continuous growth trend worldwide. The data shows that the disease burden in women is consistently higher than in men, and the gender gap has gradually widened over time. However, when we look at ASMR and ASDR, we find that the burden on men is consistently higher than that on women, and the gender gap is widening year by year. (Fig 1)

Fig 1 — **(A)** Trends in deaths and ASMR; and **(B)** Trends in DALYs and ASDR. BMI, body mass index; DALYs, disability-adjusted life years; ASMR, Age-standardized mortality rate; ASDR, Age-standardized disability-adjusted life year rate.

Among the 204 countries and territories, American Samoa had the highest T2DM-Associated CKD ASMR and ASDR related to high BMI in 2021, at 26 and 565.86 per 100,000, respectively. This was followed by the Northern Mariana Islands (20.31 and 434.07 per 100,000, respectively) and Nauru (18.49 and 453.6 per 100,000, respectively). In contrast, the countries with the lowest ASMR and ASDR were Ukraine (0.13 per 100,000) and Turkana (10.79 per 100,000). (Tables in S1 and S2 Tables)

In 2021, ASMR and ASDR as a whole showed a downward trend with increasing SDI, indicating that the higher the level of socioeconomic development, the lower the ASMR and ASDR of T2DM-Associated CKD caused by high BMI. However, this trend is not linear. In areas with an SDI < 0.7, ASMR and ASDR actually increase with increasing SDI, which may be related to the gradual improvement of medical resources and the increase in disease diagnosis rates in these areas. In addition, lifestyle changes in low SDI areas during the early stages of economic development, such as the westernization of the diet and a reduction in physical activity, may also lead to an increase in the disease burden.

Over the past 32 years, the global ASMR and ASDR associated with high BMI-related T2DM-Associated CKD have generally decreased with increasing SDI. However, there are significant regional differences, mainly reflected in the fact that ASMR and ASDR have increased in regions with low SDI, while they have mostly remained stable or decreased in regions with high SDI. (Fig 2)

Fig 2 — **(A)** ASMR; and **(B)** ASDR. BMI, body mass index; ASMR, Age-standardized mortality rate; ASDR, Age-standardized disability-adjusted life year rate.

Both ASMR and ASDR are expected to continue increasing between 1990 and 2021, both globally and in the five SDI regions. ASMR is expected to increase the most in the high SDI region and the least in the low SDI region. ASDR is expected to increase the most in the middle SDI region and the least in the low SDI region. In 2021, ASMR and ASDR are highest in the middle SDI region (2.46 and 58.59 per 100,000, respectively) and lowest in the low SDI region (1.50 and 38.01 per 100,000, respectively). In addition, the ASMR and ASDR in the middle SDI region have been the highest for 32 years. In summary, although the ASMR and ASDR have increased in different regions worldwide, the differences between the SDI regions are still significant. (Fig 3)

Fig 3 — **(A)** ASMR; and **(B)** ASDR. BMI, body mass index; ASMR, Age-standardized mortality rate; ASDR, Age-standardized disability-adjusted life year rate. SDI, socio-demographic index.

From 1990 to 2021, the age-standardized burden of T2DM-Associated CKD related to high BMI showed a significant upward trend. The plot of the joinpoint regression model for the global ASMR of T2DM-Associated CKD related to high BMI from 1990 to 2021 contains five inflection points and is divided into six segments: 1990–1997, APC = 1.64%; 1997–2000, APC = 2.84%; 2003–2007, APC = 1.01%; 2007–2015, APC = 2.13%; 2015–2021, APC = 1.11%. Male ASMR increased annually by an average of 1.90%, i.e., AAPC = 1.90 (95% CI, 1.79–2.0; P < 0.05), and female ASMR had an AAPC = 1.87 (95% CI, 1.75–2.0; P < 0.05). Overall, the AAPC for the global ASMR of T2DM-Associated CKD related to high BMI from 1990 to 2021 was 1.90 (95% CI, 1.78–2.02; P < 0.05).(additional file 1) The ASDR joinpoint regression model chart contains five turning points and is divided into six sections: 1990–1996, APC = 1.19%; 1996–2003, APC = 2.64%; 2003–2007, APC = 1.03%; 2007–2016, APC = 1.66%; 2016–2019, APC = 0.40%; 2019–2021, APC = 1.65%. The AAPC for males, females and both sexes were 1.63 (95% CI, 1.54–1.73, P < 0.05), 1.53 (95% CI, 1.43–1.63, P < 0.05), and 1.58 (95% CI, 1.49–1.68, P < 0.05), respectively. (Fig 4).

Fig 4 — **(A)** Time trends in ASMR; and **(B)** Time trends in ASMR. BMI, body mass index; ASMR, Age-standardized mortality rate; ASDR, Age-standardized disability-adjusted life year rate.

Fig 5 shows the factor decomposition of the changes in ASMR and ASDR for T2DM-Associated CKD related to high BMI from 1990 to 2021 globally. For ASMR, epidemiological changes are the most important driver, followed by population, and ageing has the smallest effect. Similar to ageing, epidemiological changes affect women more than men, while population affects men and women similarly. For ASDR, epidemiological changes remain the dominant factor driving overall growth for both sexes, followed by population. In contrast, ageing has a negative effect on the increase in ASDR, although this protective effect is minimal. (Fig 5).

Over the next 26 years, the ASMR and ASDR for both males and females are expected to rise steadily. By 2050, the ASMR of T2DM-Associated CKD related to high BMI in men is expected to increase to 5.21 per 100,000, an increase of 30.90% compared to 3.98 per 100,000 in 2021; the ASMR in women will increase from 3.66 per 100,000 in 2021 to 4.90 per 100,000, an increase of 33.88%. In addition, the ASDR for men will increase from 0.00094 per 100,000 in 2021 to 0.00127 per 100,000 in 2050, while for women it will increase from 0.00089 per 100,000 to 0.00124 per 100,000. These changes indicate that the impact of BMI on the disease burden of T2DM-Associated CKD will further increase in the future, and there is a significant upward trend for both men and women. (Fig 6).

Fig 6 — **(A)** ASMR for males. **(B)** ASMR for females. **(C)** ASDR for males. **(D)** ASDR for females. BMI, body mass index; ASMR, Age-standardized mortality rate; ASDR, Age-standardized disability-adjusted life year rate.

Discussion

This study included 204 countries and regions, assessed the global burden pattern of T2DM-Associated CKD associated with high BMI over a 32-year period and predicted the disease burden trend over the next 29 years. Compared with 1990, ASMR and ASDR caused by T2DM-Associated CKD related to high BMI showed an upward trend in 2021, with increases of 78.4% and 62.7%, respectively (Table 1). There are significant geographical differences: the ASMR and ASDR in Andean Latin America is 6.9/100,000 and 147.4/100,000, the highest in the world; while Eastern Europe is the lowest, at 0.51/100,000. With a sound medical system and high disease diagnosis rates, high-income regions can detect and intervene earlier, thereby effectively reducing the disease burden [15,16] In contrast, medical resources are relatively scarce in low- and middle-income areas. The westernization of diets and reduced physical activity brought about by economic development in many developing countries have further contributed to the prevalence of overweight/obesity and diseases related to high BMI [17]. The prevailing diet in Andean Latin America is dominated by high-calorie, low-nutrient-density foods, and diabetes mellitus and T2DM-Associated CKD have a high prevalence and are often undertreated in patients. DN remains one of the leading causes of ESRD, accounting for approximately 36% of CKD cases. In the states of Jalisco and Aguascalientes, the prevalence of DN reaches as high as 48% [18]. In high-income Latin American countries such as Argentina and Brazil, dialysis coverage is relatively high. By contrast, in low-income countries like Nicaragua, coverage remains limited. Overall, dialysis availability shows a positive correlation with both GDP and life expectancy. A study of Mexican patients with kidney failure revealed that most patients did not receive specialized kidney care, and that patients’ renal function was very poor at the time of initiation of dialysis, leading to a high post-dialysis mortality rate. Mexican patients had almost three times the risk of death compared to patients in the United States [19]. Additionally, in Latin America, particularly in agricultural regions, long-term environmental factors such as heat stress and pesticide exposure significantly increase the risk of CKD. For example, Guatemala and Honduras have reported CKD cases attributed to these factors, and CKD patients in these regions are often young men, further exacerbating the public health burden [18]. Through region-specific evidence, we can see that common risk factors such as overweight/obesity and diabetes have differing health impacts across regions, closely tied to socio-economic conditions, cultural backgrounds and accessibility of health services. Based on these differences, it becomes particularly important to develop region-specific public health interventions. For example, in high-income countries, interventions may focus more on improving dietary composition and promoting exercise, whereas in low-income countries, increasing access to early screening and treatment of diabetes is more urgent. (Fig 1).

High BMI significantly increases the risk of developing T2DM-Associated CKD through a variety of biological mechanisms, including insulin resistance, chronic inflammatory response and high glomerular filtration [20,21]. First, insulin resistance is a core metabolic feature of individuals with high BMI, which leads to lipid metabolic disorders and elevated levels of free fatty acids (FFAs). FFAs impair glomerular function by activating inflammatory signaling pathways. Their derivatives, such as diacylglycerol (DAG) and ceramide, inhibit key steps in the insulin signaling pathway, including the phosphorylation of insulin receptor substrates, thereby exacerbating insulin resistance and kidney damage [22,23]. Second, chronic inflammatory response is another significant mechanism. The adipose tissue of individuals with high BMI is excessively expanded, which can secrete pro-inflammatory cytokines (such as TNF-α, IL-6 and IL-1β), induce oxidative stress by activating signal pathways such as NF-κB, thereby damaging glomeruli and renal tubular cells [24,25]. In addition, these pro-inflammatory factors can attract macrophages and T cells into the renal tissue, exacerbating the local inflammatory response and leading to glomerulosclerosis [24,26]. Finally, a high filtration state is an important feature of high BMI-related T2DM-Associated CKD. High BMI leads to increased intra-glomerular pressure and hemodynamic abnormalities. Long-term high filtration state promotes thickening and sclerosis of the glomerular basement membrane, accelerating the decline of renal function [23,26]. As the overweight/obesity rate continues to rise worldwide, the disease burden of T2DM-Associated CKD may further increase in the future, and there is an urgent need to develop new treatments. A randomized controlled trial involving patients with T2D and CKD showed that semaglutide significantly reduced the risk of major events in kidney disease, including the onset of renal failure and the rate of decline in renal function, by 24% compared with the placebo group (Hazard ratio, 0.76; P = 0.0003) [27]. The future burden of T2DM-Associated CKD may change with the use of these emerging drugs, and the long-term impact needs to be further assessed in follow-up studies.

The burden of T2DM-Associated CKD in low-, medium-, and high-SDI regions exhibits a U-shaped trend, reflecting the complex changes in the disease across different stages of economic development. In high-SDI regions, these indicators show a declining trend, primarily attributed to advancements in medical technology and optimized disease management [16]. The incidence rates of ASMR and ASDR are lowest in low-SDI regions (1.50 and 38.01 per 100,000, respectively), but these rates may be underestimated due to underdiagnosis [17]. In regions with moderate SDI, ASMR and ASDR reached their peak levels (2.46 and 58.59 per 100,000, respectively). Many countries have experienced a shift in disease burden from infectious diseases to chronic non-communicable diseases (such as cardiovascular diseases, diabetes, and cancer) [28]. This shift is often accompanied by changes in socioeconomic structure, Westernization of lifestyles (such as dietary habits and reduced physical activity), and aging [17]. These shifts in disease burden require public health interventions to adapt during the transition process. Overall, the impact of economic development on the disease burden is dual in nature. On one hand, improved medical resources and strengthened public health policies have significantly reduced the burden of disease. On the other hand, lifestyle changes and increased rates of overweight/obesity during the early stages of economic development may lead to an increased disease burden in the short term [29]. These trends reveal the profound impact of socioeconomic development, medical resource allocation and lifestyle changes on the global burden of disease, providing an important reference for optimizing public health policies.

There are significant differences between the sexes in T2DM-Associated CKD related to high BMI (Fig 5). The number of deaths due to T2DM-Associated CKD caused by high BMI is higher in women than in men, but the ASMR in men is higher than in women, which reflects the higher mortality risk in men of the same age. This phenomenon may be closely related to the interplay of biological and social behavioral factors. Men are more prone to accumulating visceral fat than women, and the accumulation of visceral fat is closely associated with metabolic issues related to diabetes [30]. Additionally, men have lower metabolic tolerance, which may lead to a higher risk of complications in cases of high BMI. Estrogen plays a certain metabolic protective role in women, helping them better regulate body fat distribution, particularly abdominal fat, thereby reducing the risk of developing T2DM-Associated CKD [31]. In addition to biological factors, social behavioral factors also play an important role in gender differences. Men are generally more likely than women to delay seeking medical care, leading to a lack of early diagnosis and intervention, which in turn exacerbates the condition and increases the risk of mortality. Meanwhile, women have stronger awareness of health management and self-care, which helps them identify diseases earlier and receive effective treatment [32]. These behavioral differences further exacerbate gender disparities in ASMR. Notably, the gap in T2DM-Associated CKD-related disease burden between men and women has widened over time. In recent years, gender differences in overweight/obesity rates and the rising overweight/obesity rate among men may be key factors contributing to this widening gap [33]. Gender differences in overweight/obesity are closely associated with socioeconomic factors, lifestyle factors (such as the Westernization of dietary habits), and work-related stress, which may be more pronounced in men.

Potential interventions and policy options cover a number of areas. First, through health education and behavioral change campaigns, people are encouraged to adopt healthier lifestyles, such as eating a balanced diet and increasing physical activity. Australia has launched a program called “Heart Foundation Walking” aimed at promoting physical activity among residents through community walking. The success of the program has not only improved the health of participants but also reduced rates of cardiovascular disease and obesity [34]. Secondly, enhancing early screening and diagnosis of diseases and promoting early detection and intervention, especially regular medical check-ups for high-risk groups. Public health facilities and telemedicine services are being strengthened. Third, promoting health promotion measures through legislation and policies, such as improving food labelling, promoting salt and sugar control policies, and implementing a sugar tax. Mexico implemented a tax policy on sugary drinks in 2014, reducing consumption by 6% and alleviating obesity to some extent [35]. In 2018, the Philippines implemented a 20% tax on sugary beverages, projected to prevent approximately 2,775 deaths over 20 years. This measure is expected to reduce 13,632 cases of ischemic heart disease and new cardiac events, 5,287 ischemic strokes, and 21,763 cases of T2D [36]. In addition, reducing poverty and improving education and employment opportunities through socio-economic policies can also help to improve health fundamentally.

This study has some limitations. Firstly, although the study utilized the GBD database, which covers 204 countries and territories, there are limitations regarding the completeness and reliability of data for some low-income countries and territories. While the GBD data offer estimates of the prevalence and burden of T2DM-associated CKD, they do not include specific metrics such as eGFR, KDIGO stages, or the urinary albumin-to-creatinine ratio, all of which are essential for staging CKD. Both of these may have implications for the generalizability and accuracy of the study’s findings. Secondly, the BAPC model assumes that past trends will continue, but public health interventions, new drug therapies (such as GLP-1RAs and SGLT2i), and socioeconomic changes may have a significant impact on these predictions. Therefore, the BAPC model is not intended for short-term policy forecasting but rather to illustrate potential trend trajectories under the assumption of the current status quo. However, the BAPC model relies on historical data for trend forecasting and is sensitive to sudden events or nonlinear changes (such as public health interventions and economic fluctuations), which may affect the accuracy of future trend forecasts. To enhance the model’s responsiveness, future research could incorporate data on external factors (such as public health interventions and economic fluctuations) and adopt nonlinear modeling methods or system dynamics models to better capture nonlinear changes in health trends. Thirdly, it is also worth noting that because the GBD model uses data based on the Geisinger Health System for the derivation of cause distributions, and in particular because the model applies data from the United States to the rest of the world, these data should not be used as specific country-level projections. Instead, they are primarily used to reveal changes in burden trends between global and regional levels. Future research should endeavor to improve these limitations by using more representative and region-specific health data to improve the accuracy and reliability of projections. In addition, this study did not fully incorporate potential factors such as dietary culture differences and genetic backgrounds, which may significantly affect BMI levels and the risk of related diseases in different regions and populations. Therefore, future studies should expand the scope of data coverage, especially strengthen data collection and quality control in low-income areas; and integrate multi-dimensional factors such as dietary culture and genetic backgrounds to more comprehensively assess the disease burden of T2DM-Associated CKD caused by high BMI.

Supporting information

S1 Table. ASMR of T2DM-Associated CKD associated with high BMI in 204 countries and regions in 2021.

(XLSX)

pone.0333672.s001.xlsx^{(45KB, xlsx)}

S2 Table. ASDR of T2DM-Associated CKD associated with high BMI in 204 countries and regions in 2021.

(XLSX)

pone.0333672.s002.xlsx^{(47.8KB, xlsx)}

Acknowledgments

We sincerely thank the Global Burden of Disease research team for providing the publicly available data that formed the basis of the study, and the co-authors for their invaluable support in data collection, analyses, and writing of the paper.

Data Availability

The dataset supporting the conclusions of this article is available in the Global Burden of Disease (GBD) repository, https://vizhub.healthdata.org/gbd-results/.

Funding Statement

The author(s) received no specific funding for this work.

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The manuscript titled “Trends and Projections of the Global Burden of Type 2 Diabetic Nephropathy Related to High BMI: A Global Burden of Disease Study 2021” addresses an important and timely public health issue, using comprehensive GBD 2021 data to describe trends and make projections. The topic is relevant, the scope is global, and the statistical approaches (Joinpoint regression, decomposition analysis, Bayesian Age-Period-Cohort modeling) are appropriate. The manuscript is generally well-structured and clear, with a logical flow from introduction to discussion.

However, there are some methodological, interpretative, and presentation issues that should be addressed before the manuscript can be considered for publication.

Major Comments

1. Clarity on Data Sources and GBD Versions

The manuscript alternately refers to GBD 2021 and GBD 2024 data. This is confusing and should be clarified to avoid inconsistencies. If GBD 2024 updates were used, the title, abstract, and methods should reflect that clearly.

The authors should specify whether they used raw GBD estimates or age-standardized, modeled data from IHME, and how they handled missing or low-certainty data for some regions.

2. Definition of “High BMI”

The definition (>20–23 kg/m² depending on region) is unconventional, as most obesity-related research uses ≥25 kg/m² for overweight and ≥30 kg/m² for obesity in adults. This threshold should be justified, with clear reference to GBD’s operational definition and why it differs from WHO’s cut-offs.

3. Interpretation of Regional Trends

The discussion sometimes attributes differences solely to dietary habits or healthcare access without citing region-specific studies. While plausible, these interpretations would be stronger with region-specific epidemiological evidence.

The SDI-related trends (e.g., U-shaped relationships in low-to-middle SDI settings) deserve deeper exploration, potentially with supporting literature on health transition stages.

4. Projection Model Limitations

The Bayesian Age-Period-Cohort model assumes continuation of past trends. The manuscript should emphasize that public health interventions, new pharmacotherapies (e.g., GLP-1 receptor agonists, SGLT2 inhibitors), and socio-economic changes could substantially alter these projections.

The text should note explicitly that the model is not designed for short-term policy forecasting but for illustrating possible trajectories under status quo assumptions.

5. Sex Differences

The manuscript notes that men have higher ASMR/ASDR despite fewer absolute deaths than women. The discussion could benefit from integrating biological (e.g., fat distribution, hormonal effects) and socio-behavioral explanations, with literature support.

The widening gap between sexes over time should be contextualized with trends in obesity prevalence by sex.

6. Policy Implications

While the discussion suggests general interventions (diet, exercise, taxation), these remain broad. The paper could be strengthened by including specific examples of successful programs in high-burden regions, linked to the observed epidemiological patterns.

Minor Comments

1. Language and Style

The English is generally clear, but some sentences are overly long and would benefit from simplification for readability.

Certain terms should be standardized (e.g., “high body mass index” vs. “high BMI”).

2. Figures and Tables

Figure legends should be fully self-contained, describing abbreviations and clarifying that rates are age-standardized.

Consider including a supplementary table with country-level ASMR/ASDR values for transparency.

3. Referencing

Some statements in the discussion lack direct citations, especially those linking trends to dietary patterns or healthcare access.

References should be checked for format consistency with the journal’s style.

4. Ethics Statement

Although GBD data are publicly available, the ethics statement could note explicitly that no human participants were directly involved.

Recommendation:

Major Revision – The study is valuable and relevant, but revisions are needed to clarify definitions, ensure methodological transparency, strengthen the interpretation of results, and align discussion points more closely with evidence.

Reviewer #2: Dear Academic Editor,

Please find below my consolidated reviewer comments for the manuscript entitled “Trends and Projections of the Global Burden of Type 2 Diabetic Nephropathy Related to High BMI: A Global Burden of Disease Study 2021”.

This research manuscript explores the global burden of type 2 diabetic nephropathy (T2DN) associated with high body mass index (BMI), using data from the Global Burden of Disease (GBD) Study 2021. The study analyzes historical trends from 1990 to 2021 and projects scenarios through 2050, examining mortality and disability-adjusted life years (DALYs). It highlights that high BMI is an important driver of increasing T2DN, with notable regional and sex disparities. The research emphasizes the urgency of interventions to prevent obesity and to improve access to healthcare.

I would recommend a Major Revision focused on clarifying the manuscript’s novel contribution, strengthening the methodological justification, and improving precision in terminology (chronic kidney disease due to type 2 diabetes) and the BMI cut-points used. Implementing the suggested revisions would increase the manuscript’s clarity, reproducibility, and usefulness for policy-makers.

Context and novelty relative to recent studies

Why: Several recent GBD-based analyses address the burden of chronic kidney disease (CKD) attributable to type 2 diabetes and the role of high BMI (e.g., Wang et al., 2025 and others). Clarifying what is new in this manuscript will help readers and editors assess its added value.

Suggestion: The authors could add a brief paragraph in the Introduction that cites these recent studies and explains what the present manuscript adds (for example: projection horizon extended to 2050; an alternative projection method; more granular subregional/age/sex stratification; additional sensitivity analyses).

Clarify and justify the definition of “high BMI”

Why: Using thresholds such as 20–23 kg/m² labeled as “high BMI” can be confused with international categories for overweight/obesity (≥25/≥30 kg/m²). Interpreting results as “obesity-driven” without clarifying this distinction can be misleading.

Suggestion: The authors could clarify the exact source of these thresholds (e.g., GBD documentation, WHO Expert Consultation for Asian populations, or another reference), indicate which countries/regions apply which threshold (if applicable), and — where appropriate — use the term “elevated/high BMI” in Results and Conclusions instead of “obesity.”

Consistent terminology tied to the GBD definition (T2DM-Associated CKD / DKD)

Why: The term “diabetic nephropathy” usually implies clinical phenotypes (albuminuria, eGFR, KDIGO stages) that the GBD attributional category does not permit distinguishing. Using a neutral, explicit label avoids overinterpretation and is consistent with terminology adopted in other GBD-based works.

Suggestion: The authors could define and consistently use “T2DM-Associated CKD”, “CKD-T2DM” (or alternatively “Diabetic Kidney Disease, DKD” when used as a broad, non-phenotypic term). As noted in their Limitations (GBD does not distinguish proteinuria nor KDIGO stages), the Methods should state this explicitly.

Methodological details for reproducibility (Joinpoint, BAPC, code)

Why: Small modeling details influence replicability and the interpretation of projections.

Suggestion: The authors could provide methodological details in Methods: how data were imported into Joinpoint (R package or script), the maximum number of joinpoints allowed, the number of permutations used for permutation tests, significance thresholds, BAPC parameters (priors, MCMC iterations), software and package versions. If feasible, deposit analysis scripts in a public repository (e.g., GitHub, Zenodo) and provide the link.

Make the Data Availability Statement explicit

Why: Citing the GBD portal is helpful, but specifying the exact outputs improves reproducibility.

Suggestion: The authors could list the exact outputs extracted (deaths, DALYs, ASMR, ASDR), years, age groups, the cause definition used, and file formats. Providing the extracted dataset (CSV) as supplementary material would be useful.

Addressing these points would significantly improve the manuscript’s methodological transparency, terminological precision, and compliance with PLOS ONE’s style and reporting expectations.

Thank you for the opportunity to review this work.

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6. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: No

Reviewer #2: No

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PLoS One. 2025 Oct 10;20(10):e0333672. doi: 10.1371/journal.pone.0333672.r002

Author response to Decision Letter 1

4 Sep 2025

Thank you for your constructive comments on our manuscript entitled ‘Trends and Projections of the Global Burden of Type 2 Diabetic Nephropathy Related to High BMI: A Global Burden of Disease Study 2021’ (ID: PONE-D-25-38032). We appreciate the opportunity to revise and improve our manuscript. We have carefully addressed all of your comments and have made the necessary changes to the manuscript. Following this, we have responded to your comments point by point.

To the academic editor:

Comment1: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response: Thank you for your reminder. We have made the revisions according to the PLOS ONE format template you provided.

Comment2: We note that Figure 2 in your submission contain [map/satellite] images which may be copyrighted.

Response: The issue you raised is very valuable. We have removed Figure 2 and provided the relevant data as Supporting Information.

Comment3: We note you have included a table to which you do not refer in the text of your manuscript. Please ensure that you refer to Table 3 and 4 in your text.

Response: Thank you for your suggestion. Our article does not contain Tables 3 and 4. We have conducted a thorough review to ensure that such errors will not occur again.

Comment4: Please include a new copy of Table 1, 2, 3, and 4 in your manuscript; the current table is difficult to read.

Response: We have added new versions of Tables 1 and 2 to the manuscript (pages 8–9).

Comment5: Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly.

Response: We have listed the titles of the Supporting Information files at the end of the paper and verified the citations in the main text (page 27, lines 527–531).

Comment6: Please remove all personal information, ensure that the data shared are in accordance with participant consent, and re-upload a fully anonymized data set.

Response: Our research is based on the GBD database, whose data is publicly available and does not contain personal information, only aggregated regional data. We have uploaded a new data set.

Comment7: If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Response: We thank the editor for the guidance regarding citation of reviewer-recommended references. We carefully examined the suggested works and, in accordance with the editorial instructions, did not cite them solely on the basis of reviewer recommendation. Instead, we selected references strictly based on their direct relevance to the content and arguments of our manuscript. We hope this approach aligns with the editorial policy.

To the reviewer 1�

Major Comment1: Clarity on Data Sources and GBD Versions. The manuscript alternately refers to GBD 2021 and GBD 2024 data. This is confusing and should be clarified to avoid inconsistencies. If GBD 2024 updates were used, the title, abstract, and methods should reflect that clearly. The authors should specify whether they used raw GBD estimates or age-standardized, modeled data from IHME, and how they handled missing or low-certainty data for some regions.

Response: We sincerely thank the reviewer for the careful reading and constructive comments. The mention of “GBD 2024” in the manuscript was an unintentional error on our part, and we have corrected it to “GBD 2021” throughout the text (page 4, line 69). We apologize for this oversight.

As clarified, our analysis was based on the age-standardized, modeled estimates provided by the Institute for Health Metrics and Evaluation (IHME), rather than raw GBD data. Regarding data quality, we conducted a careful data-cleaning process. Specifically, we removed duplicate entries and ensured internal consistency across indicators. For regions with missing or low-certainty estimates, we followed the IHME protocol of using modeled age-standardized estimates, which incorporate statistical adjustments to account for data sparsity. Where IHME provided no reliable estimate for a given region, we excluded these data points from the analysis to avoid introducing bias.

We hope this clarification resolves the reviewer’s concern and improves the transparency of our methodology.

Major Comment2: Definition of “High BMI”. The definition (>20–23 kg/m² depending on region) is unconventional, as most obesity-related research uses ≥25 kg/m² for overweight and ≥30 kg/m² for obesity in adults. This threshold should be justified, with clear reference to GBD’s operational definition and why it differs from WHO’s cut-offs.

Response: We appreciate the valuable comments provided by the reviewers. After reviewing relevant literature in the field, we found that most studies adopt BMI ≥ 25 kg/m² as the criterion for overweight and ≥ 30 kg/m² as the criterion for obesity. Upon consulting the GBD database, we observed that the original data utilized a BMI range of >20–23 kg/m². It is important to note that the publicly available GBD data has been cleaned and standardized to a BMI threshold of ≥25 kg/m². Based on this, we have made corresponding revisions to the manuscript (page 5, line 87). We are grateful for the reviewer's reminder.

Major Comment3: Interpretation of Regional Trends. The discussion sometimes attributes differences solely to dietary habits or healthcare access without citing region-specific studies. While plausible, these interpretations would be stronger with region-specific epidemiological evidence.

The SDI-related trends (e.g., U-shaped relationships in low-to-middle SDI settings) deserve deeper exploration, potentially with supporting literature on health transition stages.

Response: We sincerely appreciate the reviewers' valuable feedback on our paper, particularly the suggestions regarding the interpretation of regional trends. Based on your comments, we have incorporated additional region-specific epidemiological data and supporting literature into the first paragraph of the Discussion section (pages 12-13, lines 216-218, 220-225, 227-232). Specifically, we cited data from the 2019 Latin American Dialysis and Renal Transplant Registry (LADRTR) to detail variations in diabetic nephropathy prevalence across different countries and regions. Additionally, we discussed non-traditional CKD cases in parts of Latin America linked to agricultural environmental factors, particularly emphasizing the impact of heat stress and pesticide exposure on disease occurrence. These modifications enhance the regional specificity of our discussion and provide more targeted epidemiological evidence.

Regarding your suggestion about discussing the U-shaped relationship in low-to-medium SDI regions, we fully agree with your perspective and have further explored this trend in the article. In the revised manuscript, we incorporated the U-shaped pattern of changes in the burden of diabetes-related kidney disease across low, medium, and high SDI regions, highlighting the complex influence of economic development stages on disease burden. Specifically, we detail how advances in medical technology and optimized disease management significantly reduce the burden of diabetic kidney disease in high-SDI regions. Conversely, in medium-SDI regions, the transition of disease burden from infectious to chronic non-communicable diseases, coupled with Westernized lifestyles and population aging, exacerbates the disease burden (Page 13, lines 236-241). Furthermore, we cite relevant literature to further substantiate these interpretations of the changes and highlight the importance of this trend for optimizing public health policies.

Once again, we appreciate your meticulous review of our article and believe these revisions enhance its comprehensiveness and depth.

Major Comment4: Projection Model Limitations. The Bayesian Age-Period-Cohort model assumes continuation of past trends. The manuscript should emphasize that public health interventions, new pharmacotherapies (e.g., GLP-1 receptor agonists, SGLT2 inhibitors), and socio-economic changes could substantially alter these projections.

The text should note explicitly that the model is not designed for short-term policy forecasting but for illustrating possible trajectories under status quo assumptions.

Response: Thank you for your reminder. We fully recognize the limitations of the BAPC model in assuming the continuation of historical trends and acknowledge that public health interventions, new drug therapies, and socioeconomic changes may profoundly impact future trends.

Following your suggestion, we have further emphasized this point in the revised manuscript, explicitly stating that the BAPC model is not designed for short-term policy forecasting but rather to illustrate potential trend trajectories under the status quo assumption. We specifically added that the BAPC model projects trends based on historical data and thus cannot fully capture the impact of sudden events or nonlinear changes—such as public health interventions and economic fluctuations—on future trends. To enhance the model's resilience, future research could incorporate external factors like public health interventions and economic fluctuations, and adopt nonlinear modeling approaches or system dynamics models to better capture nonlinear shifts in health trends (page 17-18, lines 332-341).

We sincerely thank you again for your insightful contributions to our work. Your recommendations have been instrumental in improving this manuscript.

Major Comment5: Sex Differences. The manuscript notes that men have higher ASMR/ASDR despite fewer absolute deaths than women. The discussion could benefit from integrating biological (e.g., fat distribution, hormonal effects) and socio-behavioral explanations, with literature support.

The widening gap between sexes over time should be contextualized with trends in obesity prevalence by sex.

Response: Thank you for your meticulous review and valuable suggestions regarding my paper. I have made the relevant revisions based on your feedback.

Firstly, regarding the relationship between gender differences and ASMR/ASDR, you suggested incorporating biological and sociological explanations. In my revisions, I have supplemented the biological factors. Specifically, I noted that males are more prone to accumulating visceral fat, which is closely associated with diabetes and metabolic disorders (Page 15, Lines 293-296). Additionally, males exhibit relatively lower metabolic tolerance, potentially leading to heightened complication risks under high BMI conditions. In contrast, female estrogen exerts protective effects, particularly in body fat distribution, by regulating abdominal fat and thereby reducing T2DN risk (Page 16, Lines 297-299). Relevant literature supporting this section has also been incorporated.

Secondly, regarding the relationship between gender differences and obesity rate trends, I highlighted that the recent rise in male obesity rates may be a key factor exacerbating gender disparities in the disease burden associated with T2DN. Gender disparities in obesity are closely linked to socioeconomic factors, lifestyle factors (such as the prevalence of Western dietary habits), and work-related stress, particularly pronounced among men (Page 16, lines 308-310).Once again, thank you for your valuable suggestions. Your feedback has significantly enhanced the quality of this paper. Should you have any further recommendations or questions, please do not hesitate to share them. I would be delighted to continue refining this work.

Major Comment6: Policy Implications. While the discussion suggests general interventions (diet, exercise, taxation), these remain broad. The paper could be strengthened by including specific examples of successful programs in high-burden regions, linked to the observed epidemiological patterns.

Response: Thank you for your meticulous review and valuable suggestions regarding my paper. Based on your feedback, we have revised the article and incorporated specific success stories to enhance its practicality and relevance.

Specifically, we have added the case study of Mexico's 2014 sugar-sweetened beverage tax policy. Research indicates this policy reduced consumption of sugary drinks by 6% and effectively mitigated obesity issues (Page 17, Lines 321-322). Additionally, we supplemented the article with the successful case of Australia's “Heart Foundation Walk Program.” This initiative promoted physical activity among residents through community walking events, significantly improving participants' health levels while reducing cardiovascular disease and obesity rates (Page 16, Lines 313-316).

We believe these concrete examples effectively support the interventions discussed in the paper. Combined with the epidemiological models, they make the discussion section more specific and practical. Thank you once again for your valuable suggestions, which have greatly enhanced the article's quality. Should you have any further recommendations or questions, please do not hesitate to inform me, and I will continue to refine the manuscript.

Minor Comment1: Language and Style. The English is generally clear, but some sentences are overly long and would benefit from simplification for readability. Certain terms should be standardized (e.g., “high body mass index” vs. “high BMI”).

Response: We appreciate the reviewers' valuable feedback. We have revised the manuscript according to their suggestions, streamlined certain sentences to enhance readability, and standardized the use of terminology.

Minor Comment2: Figure legends should be fully self-contained, describing abbreviations and clarifying that rates are age-standardized.

Consider including a supplementary table with country-level ASMR/ASDR values for transparency.

Response: We appreciate the reviewer's suggestions. We have revised the figure and table captions to ensure they are self-contained and clearly describe the abbreviations, explicitly noting that all ratios are age-standardized. Additionally, we have submitted supplementary tables containing more detailed data to enhance transparency.

Minor Comment3: Some statements in the discussion lack direct citations, especially those linking trends to dietary patterns or healthcare access. References should be checked for format consistency with the journal’s style.

Response: We appreciate the reviewers' detailed feedback. We have incorporated direct citations and additional case examples into the Discussion section. Furthermore, all citations have been reviewed and standardized according to the journal's formatting requirements.

Minor Comment4: Ethics Statement. Although GBD data are publicly available, the ethics statement could note explicitly that no human participants were directly involved.

Response: We have explicitly stated in the “method” and “Ethics approval and consent to participate” section that the GBD data used in this paper is publicly available and does not directly involve human participants. Thank you; your feedback helps further enhance the rigor of the article.

To the reviewer 2�

Comment1: Context and novelty relative to recent studies. Why: Several recent GBD-based analyses address the burden of chronic kidney disease (CKD) attributable to type 2 diabetes and the role of high BMI (e.g., Wang et al., 2025 and others). Clarifying what is new in this manuscript will help readers and editors assess its added value. Suggestion: The authors could add a brief paragraph in the Introduction that cites these recent studies and explains what the present manuscript adds (for example: projection horizon extended to 2050; an alternative projection method; more granular subregional/age/sex stratification; additional sensitivity analyses).

Response: Thank you for your valuable feedback on our paper. We have revised the relevant sections of the manuscript as per your suggestions to ensure our research's unique contributions and relationship to prior work are more clearly articulated.

In the Introduction, we cited Tan et al.'s analy

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Submitted filename: Response to Reviewers.docx

pone.0333672.s004.docx^{(29.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0333672.r003

Decision Letter 1

Claudio Dávila-Cervantes

8 Sep 2025

PONE-D-25-38032R1Trends and Projections of the Global Burden of T2DM-Associated CKD Related to High BMI: A Global Burden of Disease Study 2021PLOS ONE

Dear Dr. wang,

Please submit your revised manuscript by Oct 23 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Claudio Alberto Dávila-Cervantes, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Reviewer #1: This is a timely and relevant analysis of the global burden of T2DM-associated CKD attributable to high BMI using GBD 2021 data. The projection horizon to 2050 and the stratification by sex, age, and SDI add clear value.

Strengths include comprehensive use of global data, improved terminology (“T2DM-associated CKD”), and region-specific discussion supported by epidemiological evidence.

Minor points for further improvement:

In the abstract/conclusions, highlight regional disparities and clarify that emerging therapies (GLP-1 RAs, SGLT2i) could alter future trajectories.

Ensure consistent use of terminology (“high BMI” vs. “obesity”; harmonize with GBD definitions).

Provide clarity on data availability (whether extracted CSV or repository link will be accessible).

Simplify long sentences to improve readability, and standardize abbreviations in figures/tables.

Add one more policy example from Asia or Africa to balance global representation.

Overall, this is a solid and well-revised manuscript; I recommend minor revisions to improve clarity and consistency.

This revised manuscript represents a substantial improvement and provides a valuable contribution to the literature on the global burden of T2DM-associated CKD attributable to high BMI. The use of GBD 2021 data, projections to 2050, and stratification by sex, age, and SDI add novelty and strengthen its public health relevance.

The authors have addressed the major reviewer concerns appropriately, including clarification of data sources, consistency of terminology, methodological transparency, and region-specific interpretation. The discussion is richer and includes relevant policy examples.

Remaining issues are minor: improving consistency of terminology (“high BMI” vs. “obesity”), ensuring clarity in the abstract/conclusions (particularly regarding regional disparities and the potential impact of emerging therapies), providing a clear data availability statement, and light language/formatting adjustments.

Reviewer #2: All comments have been addressed by the authors; thank you for the thorough revisions and attention to detail.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: Yes: Ana M Cebrián-Cuenca

Reviewer #2: Yes: Juan Rodrigo Gómez-Bernal

**********

PLoS One. 2025 Oct 10;20(10):e0333672. doi: 10.1371/journal.pone.0333672.r004

Author response to Decision Letter 2

16 Sep 2025

Thank you for your thoughtful and constructive feedback on our manuscript. We greatly appreciate the time and effort you have put into reviewing our work. Below, we outline how we have addressed the points you raised:

Reviewer 1:

Comment1: In the abstract/conclusions, highlight regional disparities and clarify that emerging therapies (GLP-1 RAs, SGLT2i) could alter future trajectories.

Response: In response to your suggestion, we have incorporated a discussion on emerging therapies, including GLP-1 receptor agonists (RAs) and SGLT2 inhibitors (SGLT2i), in the abstract. We acknowledge the potential impact of these therapies on the future trends of T2DM-associated CKD, particularly in regions with a high disease burden. (page2, line 34-35)

Comment2: Ensure consistent use of terminology (“high BMI” vs. “obesity”; harmonize with GBD definitions).

Response: We truly appreciate your careful attention to the terminology used throughout the manuscript. In response to your comment regarding the consistent use of terminology (“high BMI” vs. “obesity”), we have carefully reviewed the manuscript and made the necessary revisions. Specifically, we have replaced instances of "obesity" with "overweight/obesity" to better align with the BMI thresholds defined in the methods section (BMI ≥ 25 kg/m²). This change ensures consistency and harmonizes the terminology with the Global Burden of Disease (GBD) definitions, as per your suggestion.

We believe this revision improves the clarity and precision of the manuscript. Thank you once again for your valuable input, and we hope the updated manuscript now meets your expectations.

Comment3: Provide clarity on data availability (whether extracted CSV or repository link will be accessible).

Response: We appreciate your suggestion to clarify the access to the data. As noted in the Methods section, we have provided the link to the relevant database. To further enhance the reproducibility of our work, we have also prepared the extracted data in CSV format as supplementary material. However, due to file size limitations, we were unable to upload the CSV file via the submission platform. If needed, we are happy to provide the data through alternative methods, such as via email or a file-sharing service, and will be glad to share it with you or the editorial team at your convenience. We hope this addresses your concern.

Comment4: Simplify long sentences to improve readability, and standardize abbreviations in figures/tables.

Response: Thank you for your valuable feedback. I have carefully considered your suggestions to simplify long sentences and improve readability. In response to your comment, I have revised several sections of the manuscript. The sentences in lines 59, 79-83, 160-164, 234-237, 257-260, 288-292, and 334-339 have been simplified for better clarity and flow. Additionally, I have standardized the abbreviations used in the figures and tables to ensure consistency throughout the manuscript. I believe these revisions significantly enhance the readability of the manuscript. Thank you again for your thoughtful suggestions, which have greatly contributed to improving the quality of the work.

Comment5: Add one more policy example from Asia or Africa to balance global representation.

Response: Thank you for your valuable feedback. Regarding your suggestion to include policy examples from Asia or Africa, we have incorporated the case of the Philippines—which implemented a sugar tax policy in 2018. The revised version adds the following statement: "In 2018, the Philippines implemented a 20% tax on sugary beverages, projected to prevent approximately 2,775 deaths over 20 years. This measure is expected to reduce 13,632 cases of ischemic heart disease and new cardiac events, 5,287 ischemic strokes, and 21,763 cases of T2D." (page17, line 328-331) This addition aims to present a more balanced perspective by incorporating policy examples from Southeast Asia and South Asia, providing broader context for tax measures addressing global health challenges stemming from unhealthy diets.

We sincerely thank the reviewers for their valuable suggestions on this paper. We have carefully revised each piece of feedback to enhance the accuracy and readability of the manuscript. We believe these modifications will significantly improve the quality of the article. Once again, we extend our gratitude for your valuable time and hard work.

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PLoS One. doi: 10.1371/journal.pone.0333672.r005

Decision Letter 2

Claudio Dávila-Cervantes

17 Sep 2025

Trends and Projections of the Global Burden of T2DM-Associated CKD Related to High BMI: A Global Burden of Disease Study 2021

PONE-D-25-38032R2

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Acceptance letter

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PONE-D-25-38032R2

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. ASMR of T2DM-Associated CKD associated with high BMI in 204 countries and regions in 2021.

(XLSX)

pone.0333672.s001.xlsx^{(45KB, xlsx)}

S2 Table. ASDR of T2DM-Associated CKD associated with high BMI in 204 countries and regions in 2021.

(XLSX)

pone.0333672.s002.xlsx^{(47.8KB, xlsx)}

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Submitted filename: Review Comments to the Author.docx

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pone.0333672.s004.docx^{(29.9KB, docx)}

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Submitted filename: Response_to_Reviewers_auresp_2.docx

pone.0333672.s005.docx^{(19.2KB, docx)}

Data Availability Statement

The dataset supporting the conclusions of this article is available in the Global Burden of Disease (GBD) repository, https://vizhub.healthdata.org/gbd-results/.

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PERMALINK

Trends and projections of the global burden of T2DM-associated CKD related to high BMI: A global burden of disease study 2021

Jing Zhang

Wenxuan Li

Zhen Sun

Yunyang Wang

Yu Xue

Ke Si

Yajing Huang

Wenshan Lv

Lili Xu

Yangang Wang

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Study design and data sources

Definitions

Statistical analysis

Joinpoint regression analysis.

Decomposition analysis.

Bayesian Age-Period-Cohort (BAPC).

Ethical consideration

Results

Table 1. Deaths and ASMR of T2DM-Associated CKD attributable to high BMI in 1990 and 2021 and the temporal trends from 1990 to 2021.

Table 2. DALYs and ASDR of T2DM-Associated CKD attributable to high BMI in 1990 and 2021 and the temporal trends from 1990 to 2021.

Fig 1. Trends in global Deaths and DALYs and corresponding age-standardized rates of T2DM-Associated CKD attributable to high BMI, 1990-2021.

Fig 2. ASMR and ASDR of T2DM-Associated CKD attributable to high BMI across 21 GBD regions by the socio-demographic index for both sexes combined, 1990–2019.

Fig 3. Changes in the ASMR and ASDR of T2DM-Associated CKD attributable to high BMI globally and in different SDI regions from 1990 to 2021.

Fig 4. Changes in time trends of joinpoint regression of T2DM-Associated CKD attributable to high BMI, 1990–2021.

Fig 5. Decomposition analysis of changes in ASMR and ASDR attributable to high BMI in global T2DM-Associated CKD from 1990 to 2021, by aging (yellow), epidemiological changes (green), and population growth (orange).

Fig 6. Global trends in the burden of T2DM-Associated CKD related to high BMI, 2022-2050.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Claudio Dávila-Cervantes

Roles

Author response to Decision Letter 1

Decision Letter 1

Claudio Dávila-Cervantes

Roles

Author response to Decision Letter 2

Decision Letter 2

Claudio Dávila-Cervantes

Roles

Acceptance letter

Claudio Dávila-Cervantes

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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