Global, regional, and national burden of chronic kidney disease attributable to dietary risks, 1990 to 2021: A systematic analysis for the Global Burden of Disease Study 2021

Jungmin Park; Seung Ha Hwang; Jiyeon Oh; Yuseon Kang; Damiano Pizzol; Lee Smith; Jinseok Lee; Hayeon Lee; Hyeon Seok Hwang; Dong Keon Yon

doi:10.1097/MD.0000000000050207

. 2026 Aug 14;105(33):e50207. doi: 10.1097/MD.0000000000050207

Global, regional, and national burden of chronic kidney disease attributable to dietary risks, 1990 to 2021

A systematic analysis for the Global Burden of Disease Study 2021

Jungmin Park ^a,^b, Seung Ha Hwang ^a,^c,^d, Jiyeon Oh ^a,^b, Yuseon Kang ^a,^b, Damiano Pizzol ^e,^f, Lee Smith ^g,^h, Jinseok Lee ^c,^d, Hayeon Lee ^a,^c,^d, Hyeon Seok Hwang ⁱ, Dong Keon Yon ^a,^b,^d,^j,^*

PMCID: PMC13480894 PMID: 42601774

Abstract

Dietary risks are key modifiable contributors to chronic kidney disease (CKD) worldwide; however, their association with geographic or socioeconomic status remains underexplored. We aimed to quantify the global, regional, and national burden of CKD attributable to dietary risks from 1990 to 2021 using estimates from the Global Burden of Disease Study (GBD) 2021. Estimates of disability-adjusted life-years (DALYs) of CKD attributable to dietary risks were stratified by age, sex, location, and Socio-demographic Index (SDI) across 204 countries and territories from 1990 to 2021. We performed a comprehensive risk assessment for 7 dietary factors of CKD stratified by underlying causes: CKD due to type 2 diabetes mellitus (T2DM), hypertension, glomerulonephritis, and other unspecified causes. Every DALY estimate was accompanied by a 95% uncertainty interval. In 2021, the global age-standardized DALY rates of CKD attributable to dietary risks were 93.52 (95% uncertainty interval, 54.29–134.38) per 1,00,000. The leading dietary contributors were low fruit (38.68 [20.15–57.77] per 1,00,000) and vegetable (30.84 [14.80–50.20]) intake and high sodium (19.81 [2.51–54.57]) intake. The DALY rates increased sharply with age, with higher rates among males, reaching 920.46 in males and 840.16 in females at ages 95 years or above for low fruit intake. Low SDI regions showed a higher burden from CKD due to hypertension, attributable to fruit (54.23) and vegetable (55.99) deficiencies, while high SDI regions exhibited a greater burden from CKD due to T2DM, attributable to highly processed meat (11.36) and a relatively high burden from red meat (6.59) and sugar-sweetened beverages (4.01), with the greatest increase from high sugar-sweetened beverage intake (121.08%). The global burden of CKD attributable to dietary risk remains substantial, with persistent disparities in age, sex, location, and SDI. Targeted dietary strategies are needed to address these disparities and reduce the CKD burden worldwide.

Keywords: chronic kidney disease, diet, disability-adjusted life years, Global burden of disease

1. Introduction

Chronic kidney disease (CKD), resulting in permanent loss of kidney function, has emerged as a significant global health concern.^[1] Its global burden has been rising substantially, with prevalence estimates ranging from 9.1% to 13.4% worldwide, and it is ranked as the seventh leading global risk factor for mortality.^[2,3] Given its close association with cardiovascular diseases such as coronary artery disease, atrial fibrillation, and stroke, and its potential progression to end-stage kidney disease, the cost and impact of CKD worldwide are notable.^[3,4] As treatment options for late-stage CKD are primarily limited to kidney replacement therapy, such as hemodialysis and kidney transplantation, substantial regional disparities have emerged, placing significant strain on healthcare systems, particularly in low- and lower-middle-income countries.^[5]

Moreover, CKD is associated with diet-related diseases, including type 2 diabetes mellitus (T2DM) and hypertension (HTN), as well as noncommunicable diseases, such as glomerulonephritis (GN), in which dietary factors are crucial for disease management. The growing prevalence of these underlying causes continues to contribute significantly to the increasing incidence of CKD, along with its substantial complications, elevated morbidity and mortality burdens, escalated healthcare spending, and widening socioeconomic disparities.^[2] Therefore, the influence of dietary risks on CKD is becoming increasingly significant as a modifiable lifestyle factor in the prevention and management of CKD progression.^[6] However, to our knowledge, no study has provided a comprehensive assessment of the global burden of CKD attributable to dietary factors and associated disparities, resulting in poor recognition and underdeveloped strategies for intervention.

Hence, we analyzed estimates from the Global Burden of Diseases, Injuries, and Risk Factor Study (GBD) 2021 to assess the global, regional, and national burden of CKD attributable to dietary risks across 204 countries and territories. We evaluated all 7 dietary risk factors delineated by the GBD framework to assess the impact of each risk factor on the 4 CKD types classified by their underlying causes. We examined the trends from 1990 to 2021, focusing on prevalence and disability-adjusted life years (DALYs).

2. Materials and methods

2.1. Overview

We utilized the GBD 2021 to assess CKD attributable to dietary risks, stratified by cause across 204 countries and territories from 1990 to 2021. The study offers incidence; years of life lost; years lived with disability; and DALYs per 1,00,000 population, by age, sex, and Socio-demographic Index (SDI), using the GBD world population standard. All analytical methods followed the guidelines for accurate and transparent health estimate reporting (GATHER)^[7] using Python (version 3.9.15; Python Software Foundation, Wilmington) and R (version 4.2.1; R Foundation for Statistical Computing, Vienna, Austria). The complete GATHER checklist is presented in Table S1, Supplemental Digital Content 1. GBD uses de-identified data, and the waiver of informed consent was reviewed and approved by the University of Washington Institutional Review Board (study number 9060).

2.2. Case definition

According to the GBD 2021 guidelines, CKD is defined as chronic, progressive loss of kidney function lasting at least 3 months, measured by the estimated glomerular filtration rate and urinary albumin-to-creatinine ratio. For individuals aged 18 years and older, the CKD-EPI estimated glomerular filtration rate equation was applied, while the Schwartz equation was used for those under 18 years. CKD is classified into 6 stages according to the degree of kidney function loss or receipt of kidney replacement therapy. The following International Classification of Diseases-10 codes associated with CKD were used: N18.1–N18.9.^[1] Detailed definitions and criteria are available in previous studies.^[8]

2.3. Input data

CKD modeling in the GBD 2021 used updated scientific literature extracted from the Global Health Data Exchange, population-based surveys measuring kidney function,^[8] and data from the European Renal Association–European Dialysis and Transplant Association (1998–2017) and the China National Health Survey. Studies that were not nationally representative, did not provide primary epidemiological data, or focused solely on a specific CKD etiology were excluded to avoid selective reporting bias.

2.4. Modeling strategy

The CKD model used separate DisMod-MR 2.1 models for each CKD stage and an aggregate stage 3 to 5 model, ensuring consistency across stage-specific estimates to generate estimates by age, sex, year, and country.^[8] To address CKD progression, which was not explicitly modeled in DisMod-MR 2.1, remission was employed as a proxy, assuming no true remission. Progression rates were back-calculated using the ratio of subsequent stage incidence to prior stage prevalence. For stage 5 CKD, remission was set to 0, and the excess mortality parameter was used to capture progression to end-stage kidney disease and mortality, with bounds informed by meta-analyses of survival in untreated stage 5 CKD. Covariate selection was based on expert feedback and the epidemiological understanding of CKD. Full methodological details are provided in previous publications.^[8]

2.5. Assessment of risk factor contributions to CKD

Diet-related CKD was assessed by using the GBD 2021 Comparative Risk Assessment Framework. Population-attributable fractions were computed by combining population-level exposure distributions with meta-analytic relative risks and applying the theoretical minimum risk exposure levels as counterfactuals.^[9,10] Dietary intake estimates from representative surveys and surveillance systems were modeled for consistency.^[10] To address the overlapping effects among dietary exposures and to avoid population-attributable fraction overestimation, mediation adjustments were incorporated. Estimates were disaggregated by age, sex, location, and year and applied to DALYs to quantify each dietary contributor’s burden.^[9] Seven dietary risks from the GBD 2021 were analyzed: low whole grain, fruit, and vegetable intake and high sodium, sugar-sweetened beverage (SSB), red meat, and processed meat intake. These findings provide a comprehensive assessment of individual and combined dietary contributions to the CKD burden.

2.6. Data presentation

The uncertainty was incorporated into 500 random samples per estimate at each stage. The results capturing variations across location, year, age, and sex were gathered, with each sample treated as an independent observation. The 95% uncertainty intervals (UIs) for each result, together with the mean estimate of DALYs, were derived from the 2.5th and 97.5th percentiles of the distribution of draws. All rates are presented as age-standardized rates per 1,00,000 population, except for age-specific rates.

3. Results

3.1. Global burden of CKD attributable to dietary risks in 2021

Figure 1 presents the age-standardized DALY rates of CKD attributable to dietary risks across 204 countries and territories classified by cause. In 2021, the DALY rate for CKD attributable to dietary risks globally was 93.52 (95% UI, 54.29–134.38) per 1,00,000 population, while the DALY rates for CKD due to GN, HTN, T2DM, and other unspecified causes were 4.09 (1.07–8.23), 50.41 (30.80–67.24), 20.55 (8.42–32.26), and 9.07 (2.38–17.96), respectively. Central sub-Saharan Africa had the highest total CKD burden (229.23 [128.39–350.76]), which was 6 times higher than the lowest rate in Eastern Europe (38.27 [21.62–56.27]). Central Latin America showed the highest burden for CKD due to GN (31.75 [9.39–61.08]) and other unspecified causes (31.34 [8.83–61.38]); central sub-Saharan Africa, for CKD due to HTN (161.71 [96.28–234.13]); and high-income North America, for CKD due to T2DM (161.71 [96.28–234.13]; Tables 1 and S2–S6, Supplemental Digital Content 2).

Table 1.

Global and regional age-standardized rate of DALYs due to chronic kidney disease in 1990 and 2021 and their percentage change in both sexes combined.

Location	Year	Total CKD	CKD due to GN	CKD due to HTN	CKD due to T2DM	CKD due to other
Global	1990	84.12 (49.75–120.66)	4.09 (1.07–8.23)	50.41 (30.80–67.24)	20.55 (8.42–32.26)	9.07 (2.38–17.96)
	2021	93.52 (54.29–134.38)	5.27 (1.50–10.57)	55.60 (34.85–74.53)	23.21 (9.95–36.61)	9.44 (2.49–18.97)
	% change	0.11 (0.00–0.19)	0.29 (0.15–0.55)	0.10 (−0.02 to 0.20)	0.13 (0.01–0.26)	0.04 (−0.08 to 0.17)
Central Asia	1990	60.24 (33.09–91.16)	5.28 (1.78–9.13)	14.82 (10.73–19.70)	15.57 (7.85–23.69)	24.58 (7.84–44.30)
	2021	68.65 (36.41–106.02)	6.96 (2.45–12.58)	14.42 (10.38–20.46)	16.80 (8.37–25.05)	30.47 (11.10–53.95)
	% change	0.14 (−0.01 to 0.30)	0.32 (0.04–0.67)	−0.03 (−0.16 to 0.13)	0.08 (−0.08 to 0.26)	0.24 (0.02–0.52)
Central Europe	1990	75.13 (41.02–111.06)	12.05 (3.97–21.25)	26.17 (19.21–34.03)	11.99 (6.10–18.00)	24.91 (8.65–43.86)
	2021	59.87 (32.57–91.84)	7.60 (2.63–13.43)	21.84 (15.40–30.10)	9.62 (4.95–15.10)	20.82 (7.42–36.71)
	% change	−0.20 (−0.29 to 0.12)	−0.37 (−0.47 to 0.25)	−0.17 (−0.26 to 0.07)	−0.20 (−0.29 to 0.11)	−0.16 (−0.27 to 0.01)
Eastern Europe	1990	39.22 (22.20–57.11)	8.42 (2.41–15.21)	11.19 (8.51–14.28)	10.99 (5.08–16.43)	8.63 (2.58–15.50)
	2021	38.27 (21.62–56.27)	8.79 (2.68–15.54)	11.55 (8.59–14.86)	9.00 (4.48–13.60)	8.92 (2.76–15.80)
	% change	−0.02 (−0.12 to 0.08)	0.04 (−0.11–0.30)	0.03 (−0.06 to 0.14)	−0.18 (−0.33 to 0.01)	0.03 (−0.08 to 0.23)
Australasia	1990	40.54 (23.24–58.83)	6.31 (2.21–10.78)	14.60 (10.74–17.96)	7.47 (3.44–11.52)	12.16 (4.38–21.01)
	2021	42.46 (24.39–60.99)	6.30 (2.14–10.86)	17.65 (11.94–22.67)	8.37 (3.70–13.03)	10.13 (3.65–18.55)
	% change	0.05 (−0.06 to 0.16)	−0.00 (−0.23 to 0.29)	0.21 (0.05–0.39)	0.12 (−0.04–0.27)	−0.17 (−0.38 to 0.07)
High-income Asia Pacific	1990	74.49 (41.28–111.42)	1.52 (0.27–3.38)	23.18 (16.23–30.83)	29.20 (15.28–43.02)	20.59 (5.67–38.55)
	2021	46.81 (25.77–70.53)	0.70 (0.13–1.57)	17.15 (10.88–23.86)	18.93 (8.98–28.32)	10.04 (2.46–19.22)
	% change	−0.37 (−0.44 to 0.30)	−0.54 (−0.64 to 0.43)	−0.26 (−0.35 to 0.20)	−0.35 (−0.47 to 0.26)	−0.51 (−0.62 to 0.43)
High-income North America	1990	58.04 (35.15–82.18)	2.18 (0.44–5.07)	29.10 (17.64–38.77)	23.00 (9.35–35.74)	3.76 (0.71–8.75)
	2021	125.15 (76.36–172.48)	5.62 (1.18–12.56)	67.08 (39.92–88.80)	47.82 (17.94–73.13)	4.63 (1.08–9.98)
	% change	1.16 (0.98–1.35)	1.58 (0.46–3.97)	1.30 (1.02–1.61)	1.08 (0.80–1.41)	0.23 (−0.25 to 1.42)
Southern Latin America	1990	125.07 (74.08–176.07)	3.99 (0.72–9.73)	75.80 (45.05–103.83)	32.57 (12.21–53.52)	12.71 (2.35–27.32)
	2021	101.68 (58.55–148.07)	4.60 (1.15–10.25)	57.89 (34.45–82.00)	24.26 (9.93–39.12)	14.93 (3.90–29.78)
	% change	−0.19 (−0.26 to 0.11)	0.15 (−0.11 to 0.94)	−0.24 (−0.33 to 0.14)	−0.26 (−0.36 to 0.05)	0.18 (−0.09–0.85)
Western Europe	1990	44.32 (25.40–64.81)	3.73 (1.05–6.85)	15.25 (10.94–19.92)	14.04 (6.41–21.64)	11.31 (3.27–21.19)
	2021	44.32 (25.37–64.23)	3.54 (1.04–6.56)	17.94 (12.72–23.32)	11.89 (5.35–18.39)	10.94 (3.07–20.42)
	% change	−0.00 (−0.06 to 0.06)	−0.05 (−0.21 to 0.16)	0.18 (0.09–0.26)	−0.15 (−0.23 to 0.07)	−0.03 (−0.18 to 0.16)
Andean Latin America	1990	153.17 (94.16–213.04)	2.91 (0.37–8.15)	114.76 (66.20–157.84)	33.58 (11.37–58.71)	1.92 (0.22–5.18)
	2021	191.06 (112.76–273.42)	7.46 (1.50–17.75)	135.22 (78.81–186.16)	43.69 (16.77–76.52)	4.69 (0.82–11.26)
	% change	0.25 (0.01–0.56)	1.56 (0.70–4.67)	0.18 (−0.05–0.48)	0.30 (−0.04–1.01)	1.44 (0.70–4.76)
Caribbean	1990	114.79 (71.68–157.67)	5.91 (1.54–12.71)	73.80 (48.98–96.57)	31.48 (12.93–50.63)	3.60 (0.87–7.84)
	2021	141.05 (87.18–201.24)	8.88 (2.51–18.18)	87.87 (56.43–118.51)	39.96 (18.02–66.79)	4.34 (1.16–9.09)
	% change	0.23 (0.06–0.42)	0.50 (0.18–1.12)	0.19 (0.03–0.39)	0.27 (0.06–0.48)	0.21 (0.03–0.59)
Central Latin America	1990	144.20 (86.55–203.25)	16.07 (4.44–31.90)	81.53 (56.17–103.24)	28.83 (12.59–46.01)	17.77 (4.65–36.61)
	2021	224.86 (129.21–327.66)	31.75 (9.39–61.08)	116.83 (80.60–155.47)	44.94 (20.14–71.41)	31.34 (8.83–61.38)
	% change	0.56 (0.40–0.73)	0.98 (0.69–1.43)	0.43 (0.28–0.61)	0.56 (0.34–0.85)	0.76 (0.51–1.20)
Tropical Latin America	1990	132.91 (83.54–180.64)	8.92 (2.02–18.37)	82.13 (55.33–104.17)	34.39 (14.81–54.83)	7.46 (1.58–15.94)
	2021	124.37 (77.61–168.66)	8.83 (2.64–17.53)	70.84 (48.49–92.56)	37.19 (16.04–59.24)	7.50 (2.10–14.86)
	% change	−0.06 (−0.15 to 0.02)	−0.01 (−0.18 to 0.42)	−0.14 (−0.20 to 0.06)	0.08 (−0.04–0.22)	0.01 (−0.16 to 0.46)
North Africa and Middle East	1990	92.87 (50.62–145.34)	1.76 (0.38–4.08)	61.30 (32.10–97.62)	20.37 (7.78–35.35)	9.44 (2.18–20.89)
	2021	87.79 (49.91–130.38)	2.43 (0.67–5.42)	56.37 (32.44–82.15)	17.65 (6.78–29.90)	11.34 (3.37–23.89)
	% change	−0.05 (−0.41 to 0.20)	0.38 (−0.12 to 1.26)	−0.08 (−0.43 to 0.20)	−0.13 (−0.43 to 0.14)	0.20 (−0.23 to 0.92)
South Asia	1990	92.75 (52.60–138.03)	4.20 (0.60–9.80)	54.83 (36.03–70.18)	21.25 (8.66–34.94)	12.46 (1.93–28.63)
	2021	105.16 (58.46–160.45)	5.90 (1.01–13.19)	58.34 (40.03–75.10)	25.33 (10.52–43.10)	15.58 (2.72–34.71)
	% change	0.13 (−0.06 to 0.30)	0.41 (0.14–0.99)	0.06 (−0.13 to 0.27)	0.19 (−0.07 to 0.50)	0.25 (0.07–0.65)
East Asia	1990	72.47 (41.13–107.44)	0.19 (0.01–0.68)	49.84 (25.88–74.63)	21.51 (6.33–37.83)	0.94 (0.05–3.18)
	2021	48.70 (25.79–74.68)	0.18 (0.01–0.62)	29.38 (13.45–48.66)	18.46 (5.75–32.68)	0.68 (0.04–2.16)
	% change	−0.33 (−0.49 to 0.16)	−0.07 (−0.70 to 1.64)	−0.41 (−0.57 to 0.24)	−0.14 (−0.36 to 0.15)	−0.27 (−0.72 to 0.90)
Oceania	1990	55.50 (29.74–84.75)	0.13 (0.01–0.46)	26.59 (12.50–41.18)	28.15 (7.59–51.39)	0.64 (0.04–1.95)
	2021	64.85 (35.61–99.93)	0.26 (0.02–0.77)	31.78 (16.95–48.74)	31.79 (9.99–57.16)	1.02 (0.11–2.84)
	% change	0.17 (−0.16 to 0.59)	1.06 (0.30–4.40)	0.20 (−0.08 to 0.55)	0.13 (−0.23–0.71)	0.61 (0.13–2.74)
Southeast Asia	1990	152.30 (86.86–218.30)	0.52 (0.01–1.88)	130.41 (72.14–182.44)	19.28 (6.13–34.36)	2.10 (0.09–6.61)
	2021	157.82 (88.52–235.07)	0.67 (0.02–2.29)	133.60 (73.46–191.59)	21.51 (6.85–39.70)	2.03 (0.13–6.28)
	% change	0.04 (−0.14 to 0.19)	0.30 (−0.01–1.36)	0.02 (−0.16 to 0.18)	0.12 (−0.10 to 0.39)	−0.03 (−0.20 to 0.47)
Central Sub-Saharan Africa	1990	232.44 (133.34–343.60)	19.41 (4.36–45.74)	156.30 (92.58–221.17)	38.50 (14.00–65.95)	18.23 (4.35–41.12)
	2021	229.23 (128.39–350.76)	18.18 (4.07–45.63)	161.71 (96.28–234.13)	32.62 (11.15–58.23)	16.72 (3.82–41.20)
	% change	−0.01 (−0.25 to 0.29)	−0.06 (−0.38 to 0.35)	0.03 (−0.21 to 0.34)	−0.15 (−0.39 to 0.15)	−0.08 (−0.37 to 0.25)
Eastern Sub-Saharan Africa	1990	196.26 (109.00–296.74)	21.30 (2.62–48.75)	124.43 (76.81–170.29)	39.33 (15.91–69.14)	11.20 (1.37–25.30)
	2021	186.90 (105.39–283.60)	29.55 (5.84–60.29)	101.18 (66.87–136.92)	40.98 (17.42–69.45)	15.19 (2.87–32.61)
	% change	−0.05 (−0.17 to 0.09)	0.39 (0.13–1.38)	−0.19 (−0.30 to 0.06)	0.04 (−0.16 to 0.36)	0.36 (0.14–1.18)
Southern Sub-Saharan Africa	1990	118.16 (65.44–177.78)	9.33 (1.82–22.48)	80.64 (47.91–110.70)	16.67 (6.45–29.41)	11.53 (2.36–27.11)
	2021	196.76 (110.55–283.63)	14.60 (3.65–33.35)	144.23 (87.16–191.29)	19.57 (8.21–33.40)	18.36 (4.29–41.15)
	% change	0.67 (0.37–0.91)	0.56 (0.19–1.28)	0.79 (0.46–1.05)	0.17 (−0.06 to 0.50)	0.59 (0.25–1.21)
Western Sub-Saharan Africa	1990	159.33 (88.06–233.19)	5.36 (0.94–13.84)	127.81 (68.79–180.56)	20.69 (6.80–35.96)	5.48 (0.91–14.49)
	2021	174.29 (96.00–261.75)	9.78 (2.01–23.98)	134.99 (76.19–188.78)	20.18 (7.41–33.81)	9.34 (1.92–23.40)
	% change	0.09 (−0.07 to 0.27)	0.83 (0.35–1.72)	0.06 (−0.11 to 0.23)	−0.02 (−0.23 to 0.39)	0.70 (0.34–1.46)

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CKD = chronic kidney disease; DALY = disability-adjusted life year, GN = glomerulonephritis, HTN = hypertension, T2DM = type 2 diabetes mellitus.

3.2. Global and regional trends in diet-related CKD burden from 1990 to 2021

Between 1990 and 2021, the age-standardized DALY rates of diet-related total CKD remained relatively stable globally, from 84.12 (49.75–120.66) in 1990 to 93.52 (54.29–134.38) in 2021. The DALY rates for CKD due to HTN and T2DM were dominant, increasing from 50.41 (30.80–67.24) to 55.60 (34.85–74.53) and from 20.55 (8.42–32.26) to 23.21 (9.95–36.61), respectively. Similar patterns were observed for CKD due to GN, with the highest percentage change, increasing from 4.09 (1.07–8.23) to 5.27 (1.50–10.57), and CKD due to other unspecified causes. Notably, high-income North America exhibited an upward trend with UIs ranging from 58.04 (35.12–82.18) to 125.15 (76.36–172.48) for total diet-related CKD, while high-income Asia Pacific showed a downward change, with UIs ranging from 74.49 (41.28–111.42) to 46.81 (25.77–70.53), across all 4 CKD causes (Table 1).

3.3. Global trend of dietary risk factors on CKD

Figure 2 presents the global trends in age-standardized DALY rates attributable to dietary risks for total CKD. The relative contributions of the factors remained unchanged between 1990 and 2021, showing modest but steady increases across all 7 contributors. Low fruit (38.68 [20.15–57.77]) and vegetable (30.84 [14.80–50.20]) intake were the leading dietary contributors to total CKD, followed by high sodium intake (19.81 [2.51–54.57]) in 2021. The top 3 contributors remained stable from 1990 to 2021, with the highest rates of CKD due to HTN. Persistent increments were also observed in low whole grain (6.46 [1.59–12.52]) and highly processed meat (5.95 [1.53–11.00]), red meat (5.50 [0.00–12.01]), and SSB (2.22 [1.02–3.74]) intakes. While this trend among dietary risks was consistent for CKD due to HTN, GN, and other unspecified causes, the contributions of low whole grain intake (4.86 [1.25–8.81]) and highly processed meat (4.39 [1.11–7.64]) and red meat (4.18 [0.00–8.99]) intake were notably higher for CKD due to T2DM than for other CKDs, exceeding the contribution of low vegetable intake (3.12 [0.83–6.88]; Tables S7–S11, Supplemental Digital Content 3).

3.4. Age- and sex-specific burden of diet-related CKD

Figure 3 illustrates the DALY rates for CKD attributable to dietary risk factors stratified by age and sex in 2021. The DALY rates increased with advanced age for both sexes across all dietary factors, exhibiting an exponential rise after age 65 years, peaking at age 95 years or above. Burdens from low fruit and vegetable intake, identified as leading dietary contributors, increased significantly with age from 14.51 (6.67–24.51) and 11.71 (4.84–20.85) at ages 25 to 29 years to 862.44 (410.60–1388.98) and 699.50 (311.15–1230.14) at ages 95 years or above for males and females, respectively. The sharpest increase was noted for high sodium intake, from 1.16 (–0.30 to 5.43) to 389.34 (17.03–1276.42), along with notable increments in highly processed and red meat consumption. The risk of high SSB intake was pronounced in younger age groups, showing a minor increase from 1.20 (0.35–2.52) to 68.03 (28.18–126.63).

Significant sex-based differences were observed in 2021. For most dietary risk factors, males exhibited higher rates than females, with the disparity increasing with age. Males and females shared the leading dietary contributors: low fruit (41.71 [21.80–62.36]) and low vegetable (33.24 [16.0–54.28]) intake. The disparity in high sodium intake between the sexes was notable, at 26.17 (3.94–68.44) for males and 17.02 (1.54–51.06) for females. However, slightly higher rates of SSB and processed meat consumption were observed among females (Fig. S1, Supplemental Digital Content 4 and Tables S12–S18, Supplemental Digital Content 5).

3.5. Contributions of individual dietary components for CKD across SDI levels

In 2021, the age-standardized DALY rates attributable to dietary risks for total CKD significantly increased as the SDI levels decreased, across leading dietary risks, including low fruit (74.71 [37.79–115.10] per 1,00,000), vegetable (73.90 [35.84–120.92]), high sodium (20.45 [0.58–69.68]), and low whole grain (8.12 [1.96–15.45]) intake in low SDI regions, compared to high SDI regions (26.89 [14.04–39.51], 18.88 [9.22–30.83], 11.97 [0.84–36.25], and 5.53 [1.38–10.31], respectively). However, high SDI regions showed higher rates of highly processed meat, red meat, and SSB consumption at 14.91 (3.93–26.95), 6.59 (0.00–14.33), and 4.01 (1.86–6.73), respectively, than low SDI regions at 4.12 (1.04–7.83), 2.38 (0.00–5.66), and 0.72 (0.31–1.26), respectively.

Similar patterns were observed for CKD due to HTN, GN, and other unspecified causes. The burden from leading contributors, low fruit and vegetable intake and high sodium intake, increased, whereas that from highly processed/red meat and SSB intake decreased as the SDI level decreased. A greater dietary burden was seen for CKD due to HTN overall, with low fruit (54.23 [30.41–76.36]) and vegetable (55.99 [31.65–83.95]) consumption being pronounced in low SDI regions and high sodium intake (23.00 [3.66–55.75]) being pronounced in the middle SDI regions. Despite similar trends across SDIs, the leading contributors varied for CKD due to T2DM, with a high burden for low whole grain and highly processed/red meat intake, particularly in high SDI regions that showed an increased burden for highly processed meat intake (11.36 [3.00–19.38]; Fig. 4 and Tables S19–S23 Supplemental Digital Content 6).

Figure 4. — Heatmap of global age-standardized disability-adjusted life years rates attributable to risk factors for (A) total chronic kidney disease, (B) chronic kidney disease due to type 2 diabetes mellitus, (C) chronic kidney disease due to hypertension, (D) chronic kidney disease due to glomerulonephritis, and (E) chronic kidney disease due to other and unspecified causes in 2021.

3.6. Impact of individual dietary components on CKD across regions

Regionally, the total burden of diet-related CKD varied significantly in 2021. The highest burden was observed in central sub-Saharan Africa for low vegetable (121.22 [57.80–207.44] per 1,00,000) and fruit (110.24 [56.99–174.19]) intake. Sub-Saharan Africa regions, similar to southern sub-Saharan Africa (105.62 [53.24–162.12]), followed for low fruit consumption, and Latin America regions, similar to Central Latin America (98.84 [48.92–160.47]), followed for low vegetable consumption. Central sub-Saharan Africa also held the highest burden for low whole grain intake (14.84 [3.84–30.49]). High-income North America showed the highest burden for highly processed meat intake (32.81 [9.12–58.06]), leading to an inconsistent pattern of burden compared with other countries. The burden for high red meat intake was high in Latin American regions, with tropical Latin America showing a rate of 15.82 (0.00–35.33), while Central Latin America showed the highest (9.50 [4.05–17.62]) burden for high SSB intake, followed by high-income North America (7.82 [3.75–13.19]).

Similarly, for CKD due to T2DM, high-income North America showed the highest burden for highly processed meat intake (25.68 [7.08–42.83]). For CKD due to HTN, central sub-Saharan Africa showed the highest burden for low vegetable (99.74 [53.69–163.72]) and fruit (84.96 [46.09–129.79]) intake, with a high burden for high sodium intake, which peaked in Southeast Asia (48.22 [6.57–115.63] per 1,00,000). For CKD due to GN, eastern sub-Saharan Africa showed a high burden for low vegetable (13.80 [2.17–29.51]) and fruit (11.02 [1.86–23.84]) consumption (Fig. 4 and Tables S19–23, Supplemental Digital Content 6).

3.7. Percent change in diet-related CKD burden

Figure 5 shows the percentage change in total diet-related CKD from 1990 to 2021. Globally, an upward trend was observed across all dietary risks, with the highest increase in high SSB (81.07% [65.72–98.77]), followed by red meat (31.07% [−24.45 to 83.49]) intake. High SDI regions experienced sharp increments across all dietary risks, especially in high SSB (121.08% [93.19–155.94]) and processed meat (74.84% [58.50–101.13]) consumption. Low-middle and middle SDI regions showed upward trends in high SSB (108.41% [71.93–140.36], 111.16% [87.57–136.83]) and red meat (47.74% [15.76–372.31], 47.04% [24.33–416.65]) intake, while high-middle SDI regions showed a decrease in low vegetable (−36.75% [−42.77 to −30.58]) and fruit (−22.67% [−30.92 to −15.22]) intake.

These trends showed considerable variations across dietary risks and regions. The largest increase was observed in high-income North America for high sodium (188.63% [129.89–2562.83]), SSB (181.85% [128.82–256.74]), processed meat (157.87% [122.12–208.22]), and red meat (90.61% [−20.75 to 137.98] intake and low vegetable (119.28% [98.86–143.21]), whole grain (89.16% [67.26–120.36]), and fruit (79.98% [62.02–99.56]) intake. Latin American regions showed similar patterns, with increments in high red meat (69.72% [−59.84 to 216.60]), processed meat (69.56% [45.60–97.08]), and sodium consumption (58.09% [11.72–81.50]) in Central Latin America. In contrast, high-income Asia Pacific experienced reductions across all contributors, particularly for high sodium intake (−51.46% [−77.18 to -40.72]). East Asia experienced reductions in fruit (−43.31% [−54.74 to −31.01]) and vegetable intake (−88.34% [−92.70 to −82.54]; Fig. 5 and Table S24, Supplemental Digital Content 7).

4. Discussion

4.1. Key findings of this study

This study used estimates from GBD 2021 to determine the global, regional, and national burden of CKD attributable to dietary risks. Between 1990 and 2021, global age-standardized DALY rates attributable to dietary risks remained relatively stable across all 4 causes of CKD, with CKD due to T2DM consistently showing the highest rate but exhibiting regionally divergent trends across dietary risks. Globally, low fruit and vegetable intake and high sodium intake are the leading dietary factors, with additional emerging risks associated with meat and SSB intake. The burden increased notably with age and was higher in males. Low SDI regions were more affected by fruit and vegetable deficiencies, whereas high SDI regions exhibited a greater burden from processed/red meat and SSB intake, with sharp increments. Notable variations existed across regions and individual dietary risk factors, suggesting the need for tailored dietary guidelines.

4.2. Comparisons with previous studies

Several studies have suggested that dietary risks are significant contributors to the CKD burden.^[6] However, these studies focused on specific dietary components, such as low fruit and vegetable intake;^[11] overconsumption of ultra-processed foods,^[12] sodium,^[13] or protein;^[14] and single causes of CKD. Most existing studies are cohort studies with limited geographic or demographic scope,^[11,12] lacking a comprehensive global assessment. In contrast, this study evaluated the association between 7 dietary risks and CKD burden, simultaneously examining their impact across 4 causes of CKD over 30 years, using a global, regional, and national perspective informed by the GBD 2021. Similarly, a recent GBD 2021-based analysis reported comparable estimates of diet-related CKD mortality and rising ASMR trends over the same period, lending independent support to the magnitude and trajectory of the burden identified in the present study.^[15] Additionally, some studies have suggested global disparities in CKD but have focused on healthcare access, diagnostic capacity, and treatment availability rather than dietary factors.^[5] Hence, our stratified assessment of the CKD burden by region and socioeconomic level underscores the need for targeted dietary interventions to mitigate this burden.

4.3. Plausible mechanisms

Between 1990 and 2021, we observed stable age-standardized DALY rates attributable to dietary CKD across all 4 types. The global increase in the prevalence of T2DM and HTN attributable to increased obesity and physical inactivity supports the predominant burden of CKD due to T2DM and HTN over time.^[6] Improved diagnostic capacity,^[16] population aging,^[17] and an increase in CKD cases of unknown or multifactorial etiologies^[18] may also explain this stable trend.

Among the 7 dietary risks contributing to CKD burden, low fruit and vegetable intake was the leading contributor across most SDI and global regions. This trend aligns with the widespread global insufficiency of fruit and vegetable consumption, below the World Health Organization’s recommendation of 400 g per day,^[9] which is a risk factor for renal cell inflammation and kidney tissue damage.^[11] High sodium intake follows as the next dietary risk, with a global average consumption of 4.3 g/d, more than twice the World Health Organization recommendation of <2 g/d.^[9] Sodium elevates blood pressure, including intraglomerular pressure, and promotes proteinuria.^[13] High sodium intake contributes disproportionately to the burden on males, likely due to sex-specific preferences for sodium-rich diets.^[19] The increase in burden from high SSB intake is notable as it promotes insulin resistance by providing excess sugar,^[20] leading to kidney disease. This point reflects the rapid rise in global SSB consumption over the past few decades, particularly among younger age groups.^[21] The rising burden of meat intake is also pronounced, as processed and red meat, which are high in saturated fat and sodium, generate metabolic byproducts such as uric acid and ammonia that accumulate in the kidneys, exacerbating inflammation and fibrosis.^[14,22]

The CKD burden varies substantially according to SDI level and region. Regions with a low SDI experience a higher burden from insufficient intake of protective foods such as fruits, vegetables, and whole grains, along with high sodium consumption, resulting in a high burden from CKD due to HTN,^[22] as observed in sub-Saharan Africa. This result aligns with the trend of low-income countries being disproportionately affected by widespread food insecurity, characterized by high-energy-dense, high-sodium food consumption, as such food is more readily available and affordable than fruits and vegetables.^[23] Lack of health interventions and nutritional education, food supply chain disruptions, and limited healthy food options exacerbate these disparities.^[24] In contrast, regions with a high SDI are more affected by detrimental dietary components, including processed meat, red meat, and SSB, with a greater burden of CKD due to T2DM.^[14,22] Similarly, the elevated burden from highly processed meat, red meat, sodium, and SSB intake in high-income North America and the high burden from red meat and processed meat intake in Latin America^[25] reflect the global nutrition transition towards Westernized diets, where traditional diets are being replaced by ultra-processed, low-nutrient foods.^[26,27] The percentage change in dietary burden over time further supports this divergence, with high-SDI regions experiencing a substantial increase in burden from the overconsumption of harmful food.^[26] These findings emphasize the growing inequities in diet-related CKD risk and the dual challenges of undernutrition and overnutrition across SDI levels.^[28]

4.4. Policy and clinical implications

Although the broad importance of nutrition and population-level dietary interventions for kidney health is recognized by the Kidney Health Sustainable Development Goal reports,^[29] targeted policies and clinical implications are required to address region-tailored dietary interventions and regulatory strategies to effectively mitigate the CKD burden. To manage the burden of CKD due to T2DM attributable to the overnutrition of harmful dietary components in countries with high SDI,^[9,26] clinical efforts should prioritize reducing metabolic risks such as T2DM and obesity through multidisciplinary care,^[20] while public health policies should focus on food reformulation, front-of-package nutrition labeling, SSB taxation, and public campaigns. Regions such as high-income North America and Latin America require stringent regulation of ultra-processed foods and improved urban health planning, considering the successful sodium reduction endeavors in high-income Asia-Pacific regions.^[30] To address the CKD burden attributable to protective food deficiencies in countries with lower SDI^[9] such as sub-Saharan African regions, clinical care should focus on nutritional supplementation and community dietary programs, while policies should strengthen food security and food distribution efforts through price reductions, subsidies, and financial incentives.^[24] International support and ongoing monitoring are essential for improving the availability of nutrient-dense foods and curbing the escalation of diet-related CKD burden.

The persistent and regionally diverse burden of diet-related CKD highlights the urgent need for stratified clinical interventions and region-specific policy frameworks. Furthermore, the escalating burden of advancing age underscores the cumulative effect of dietary risks and the need for cost-effective dietary screening and counseling. As global aging accelerates and dietary transitions intensify, future guidelines should include culturally and demographically adapted dietary guidance tailored to age, sex, region, and socioeconomic contexts, as well as regulatory policies based on regional and socioeconomic conditions.

4.5. Strengths and limitations

To the best of our knowledge, this study is the first to present the global, regional, and national burdens of CKD attributable to dietary risks stratified by causes, using GBD 2021. However, this study has some limitations. First, given CKD’s long natural history and our reliance on recent or cross-sectional dietary data, unmodeled latency and potential reverse causation may bias dietary-attributable estimates in either direction, and results should be interpreted as associations.^[31,32] Such temporal disconnect is likely to result in nondifferential exposure misclassification and underestimation of the diet-attributable CKD burden. However, given the stability of population-level dietary patterns and the cohort-based nature of relative risks, this limitation is unlikely to alter the direction of associations or comparative trends. Despite efforts to quantify dietary impacts, a lack of consideration for potential interactions between dietary and non-dietary risk factors, such as physical inactivity, obesity, smoking, and medication use, likely results in underestimation of the true dietary contribution to CKD, as these risks frequently co-occur and share variance.^[33] Second, by focusing solely on dietary factors, the study may have overlooked other risks such as behavioral, genetic, and environmental determinants, potentially oversimplifying the complex pathophysiology of CKD.^[4,34] Third, as this study focused on analyzing diet-related CKD, CKD due to type 1 diabetes mellitus was not assessed, despite its distinct pathophysiology and epidemiological profile compared to T2DM.^[4] Fourth, early-stage CKD is often underdiagnosed because of its asymptomatic nature.^[35] This silent progression may have affected global estimates, leading to a substantial gap between the actual and reported burdens. Fifth, owing to limited access to diagnostic tools and healthcare infrastructure, which exacerbates underdiagnosis, CKD-related DALY rates in low SDI regions may have been underestimated.^[5,36] Furthermore, this analysis did not incorporate sociocultural and systemic factors influencing the diet-related CKD burden, such as cultural dietary patterns, food availability and affordability, and disparities in CKD detection and reporting due to healthcare infrastructure or screening procedures. These contextual differences may cause regional underestimation in low-SDI regions and amplification of specific risks in high-SDI regions, impacting the magnitude but not the direction of cross-country DALY comparisons. The GBD 2021 used advanced statistical models to compensate for data sparsity, which may have contributed to lower reliability in countries with poor surveillance or limited health system reporting, increasing the UIs and potentially underestimating the true CKD burden. Furthermore, regarding methodological constraints, the aggregated nature of GBD data precluded de novo sensitivity analyses, which require computationally infeasible refitting of component models. However, the reported UIs, derived from a draw-based propagation system, robustly capture the accumulated error across modeling layers, and the estimates’ reliability is underpinned by the GBD’s rigorous upstream validation, including out-of-sample predictive validity testing. Additionally, given the aggregated nature of GBD data, the application of advanced machine learning frameworks to model complex, nonlinear interactions was precluded, constraining the predictive scope of our analysis. Sixth, CKD-related deaths may have been misclassified as deaths due to cardiovascular disease or diabetes mellitus, leading to an underestimation of DALY rates, especially in low-resource settings with imprecise death reporting.^[4] Seventh, focusing on individual dietary factors without considering potential interactions between contributors may have oversimplified the combined dietary influences on CKD burden. Finally, although the direct association between dietary risks and GN is not well established, CKD due to GN was included in the analysis. This inclusion was based on the understanding that dietary factors may play an indirect role in the management and progression of CKD due to GN, given the focus of the study on the overall burden of CKD attributable to dietary risk.^[37] Therefore, the findings should be interpreted with caution.

While mechanistic mathematical models and machine learning frameworks offer powerful tools for predicting nonlinear interactions and individual disease trajectories,^[38–40] this study utilized the GBD Comparative Risk Assessment framework to focus on descriptive attribution at the population level. Given that GBD estimates are aggregated, calibrating the individual-level transition parameters required for mechanistic modeling was beyond the scope of this study; however, such predictive models would serve as valuable complementary tools for future research exploring intervention scenarios.

Despite these limitations, this study has several strengths. By utilizing the GBD 2021 estimates, this study provides a comprehensive assessment of CKD attributable to dietary risks, including CKD due to T2DM, HTN, GN, and other unspecified causes. It provides a multidimensional evaluation of the CKD burden stratified by age, sex, location, and SDI by examining age-standardized DALY rates. Furthermore, we propose SDI- and region-specific policy recommendations and targeted dietary guidelines for the prevention and mitigation of CKD worldwide.

5. Conclusions

This study highlights the persistent global burden of CKD attributable to dietary risk, with substantial disparities across age, sex, region, and SDI levels. Low fruit and vegetable intake and high sodium consumption remain the primary contributors, whereas risks due to the high consumption of red/processed meat and SSB are emerging. These findings underscore the urgent need for stratified dietary interventions and public health strategies to reduce the global burden of diet-related CKD.

Author contributions

Conceptualization: Jungmin Park, Seung Ha Hwang, Jiyeon Oh, Dong Keon Yon.

Methodology: Jungmin Park, Seung Ha Hwang, Jiyeon Oh, Dong Keon Yon.

Resources: Yuseon Kang, Damiano Pizzol, Lee Smith, Jinseok Lee, Hayeon Lee, Hyeon Seok Hwang, Dong Keon Yon.

Software: Jungmin Park, Seung Ha Hwang, Jiyeon Oh, Dong Keon Yon.

Supervision: Hyeon Seok Hwang, Dong Keon Yon.

Validation: Jungmin Park, Seung Ha Hwang, Jiyeon Oh, Dong Keon Yon.

Visualization: Seung Ha Hwang, Jiyeon Oh.

Writing – original draft: Jungmin Park, Seung Ha Hwang, Jiyeon Oh, Hyeon Seok Hwang, Dong Keon Yon.

Writing – review & editing: Jungmin Park, Seung Ha Hwang, Jiyeon Oh, Yuseon Kang, Damiano Pizzol, Lee Smith, Jinseok Lee, Hayeon Lee, Hyeon Seok Hwang, Dong Keon Yon.

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Abbreviations:

CKD: chronic kidney disease
DALY: disability-adjusted life year
eGFR: estimated glomerular filtration rate
GBD: global burden of disease
GN: glomerulonephritis
HTN: hypertension
SDI: Socio-demographic Index
SSB: sugar-sweetened beverage
T2DM: type 2 diabetes mellitus
UI: uncertainty interval

This research was supported by the Ministry of Science and ICT (RS-2026-25495984), the Ministry of Health & Welfare (RS-2025-02220492), Republic of Korea. This research was supported by the Patient-Centered Clinical Research Coordinating Center (PACEN) funded by the Ministry of Health & Welfare (RS-2025-25394255), Republic of Korea. The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors had full access to the study data and had final responsibility for the decision to submit for publication.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

Supplemental Digital Content is available in the online version of this article (http://dx.doi.org/10.1097/MD.0000000000050207).

How to cite this article: Park J, Hwang SH, Oh J, Kang Y, Pizzol D, Smith L, Lee J, Lee H, Hwang HS, Yon DK. Global, regional, and national burden of chronic kidney disease attributable to dietary risks, 1990 to 2021: A systematic analysis for the Global Burden of Disease Study 2021. Medicine 2026;105:33(e50207).

JP, SHH, and JO contributed to this article equally.

HSH and DKY contributed to this article equally.

Contributor Information

Jungmin Park, Email: ksw09213@khu.ac.kr.

Seung Ha Hwang, Email: hwanghsne@gmail.com.

Jiyeon Oh, Email: agnesjoh@khu.ac.kr.

Yuseon Kang, Email: kv0732002@khu.ac.kr.

Damiano Pizzol, Email: damianopizzol8@gmail.com.

Lee Smith, Email: Lee.Smith@aru.ac.uk.

Jinseok Lee, Email: wwhy28@khu.ac.kr.

Hayeon Lee, Email: wwhy28@khu.ac.kr.

Hyeon Seok Hwang, Email: hwanghsne@gmail.com.

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