The global burden of chronic kidney disease (CKD) attributable to high sodium intake: a comprehensive analysis of trends from 1990 to 2021 and burden prediction to 2040

The global burden of chronic kidney disease (CKD) attributable to high sodium intake: a comprehensive analysis of trends from 1990 to 2021 and burden prediction to 2040 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The global burden of chronic kidney disease (CKD) attributable to high sodium intake: a comprehensive analysis of trends from 1990 to 2021 and burden prediction to 2040 Yawen Lu, Jianfeng Ma, Yang Hu, Rumeng Zheng, Liping Liu, Kaili Lin, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6865321/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Chronic kidney disease (CKD) is a progressive condition affecting over 10% of the global population, with high sodium intake identified as a critical modifiable risk factor. This study investigated the global burden of CKD due to excessive sodium intake in 204 countries and territories from 1990 to 2021 and made the first future projections to 2040, addressing gaps in longitudinal analysis of sodium-related CKD trends and demographic differences. Method Data from the Global Burden of Disease (GBD) 2021 database were analyzed to quantify CKD-related deaths and disability-adjusted life years (DALYs) linked to high sodium intake. Age-standardized mortality rates (ASMR) and DALY rates (ASDR), alongside socio-demographic index (SDI), were used to assess regional and demographic variations. Statistical analyses in R included joinpoint regression to identify temporal inflection points and age-period-cohort (APC) modeling to disentangle age, period, and birth cohort effects. Future projections show that from 2021 to 2040, the global ASMR trend is stabilizing and ASDR is on the rise. And male ASMR and ASDR have been consistently higher than female ASMR. This gender difference is expected to continue for a long time, with men continuing to bear a greater burden of chronic kidney disease than women. Result Between 1990 and 2021, global CKD deaths attributed to high sodium intake surged 13.7-fold (2,607 to 69,954), while DALYs increased by 135% (741,197 to 1,705,325). ASMR and ASDR rose markedly in high-income regions (20.73% and 6.77%, respectively), with Latin America and the Caribbean reporting the highest burdens (ASMR: 1.49/100,000; ASDR: 33.21/100,000). Males exhibited consistently higher burdens than females, peaking in the 65–79 age group. Low SDI regions showed declining trends, contrasting with widening inequalities in medium SDI areas. Conclusion The global CKD burden attributable to high sodium intake has escalated dramatically over three decades, driven by aging populations, dietary shifts, and regional disparities. Urgent, targeted interventions—such as sodium reduction policies, gender-specific health strategies, and enhanced healthcare access—are critical to curbing this trend, particularly in high-risk demographics and high-income regions. chronic kidney disease high sodium intake global burden joinpoint regression analysis age-period-cohort model future projections Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Chronic kidney disease (CKD) has emerged as a growing global health crisis, currently impacting over 800 million people—more than 10% of the world's population [ 1 ] . Alarmingly, these numbers continue to climb year after year. Projections indicate that by 2040, CKD will rank as the fifth leading contributor to reduced life expectancy worldwide [ 2 ] . While the link between excessive sodium consumption and CKD progression has been established, the full scope of this relationship remains understudied on a global scale. Beyond its devastating health consequences, CKD presents unique clinical challenges, as affected individuals often require particularly complex care [ 3 ] . The economic toll of managing this condition continues to strain healthcare systems across the globe [ 4 ] . Sodium is a crucial mineral that's deeply involved in keeping our bodies running smoothly. Given its vital role, it's no surprise that sodium and health have a Goldilocks-type relationship – you need just the right amount. Too little or too much can lead to health problems [ 5 ] . Some research indicates that too much salt can throw a wrench into how well your kidneys function [ 6 ] . When you overload on sodium, your body kicks into high gear to maintain balance [ 5 ] . The kidneys work overtime to flush out the extra salt and water. But, this constant strain can eventually cause lasting damage, potentially leading to chronic kidney disease [ 7 ] . On the flip side, cutting back on sodium can actually protect your kidneys, even if it doesn't lower your blood pressure [ 8 ] . Experts suggest aiming for less than 2 grams of sodium or 5 grams of salt daily, which translates to about 90 mmol of sodium excreted in urine [ 9 ] . While CKD has been studied extensively, the connection between sodium intake and CKD hasn't received much attention. There's a real dearth of long-term studies tracking how sodium affects the progression of CKD. What's more, this study hasn't really dug deep into how these risk factors play out differently depending on age and gender. We've seen studies that look at individual risk factors – for example, how high blood pressure [ 3 ] affects CKD – but we still need a complete, soup-to-nuts investigation into the global burden of CKD. Considering how little we know about the shifting patterns, the global burden of CKD attributable to high sodium intake highlights the need for better ways to gauge and keep tabs on how the disease burden and health outcomes are changing over time in various groups. The purpose of this study is to comprehensively investigate the global burden of chronic kidney disease as determined by the 2021 Global Burden of Disease (GBD 2021) database, as well as the trends in high sodium intake across different categories from 1990 to 2021, including age, sex, social demographic index (SDI) quintiles, GBD subcontinental regions, and countries. We also conducted breakpoint regression analysis using an age-period-cohort model to examine the temporal patterns of the disease over the past three decades. Finally, we made the first future projections for chronic kidney disease caused by high sodium intake, extending these projections to 2041. These findings provide critical information for formulating policies and initiatives aimed at reducing global high sodium intake, preventing chronic kidney disease, and protecting high-risk populations. 2. Materials and Methods 2.1 Data source and definitions The research harnessed the GBD 2021 database to paint a picture of the global disease load connected to CKD linked to excessive sodium consumption. The GBD initiative draws on a variety of sources, including census figures, civil records, vital statistics, disease registries, household surveys, health service usage, and air pollution sensors, among other things [ 10 ] . CKD was defined as estimated glomerular filtration rate (eGFR) of < 60 mL/min per 1.73 m2 [ 11 ] . High sodium intake was defined as an average 24h urinary sodium excretion (in grams per day) greater than 1–5 g [ 12 ] . Previous studies [ 13 – 14 ] have detailed its assessment, including data selection and inputs. 2.2 Basic study variables We gathered estimates of the CKD burden, including disability-adjusted life years (DALYs) and mortality figures. DALYs are figured out by simply adding up Years Lived with Disability (YLDs) and Years of Life Lost (YLLs). For the GBD 2021 standard population, we crunched the numbers to get age-standardized rates (average rates per 100,000 folks) using the direct standardization method. All age-standardized mortality rates (ASMRs) and age-standardized DALY rates (ASDRs) were adjusted for age. The SDI, a composite measure, is made up of three things: the total fertility rate for the under−25s, the average education level of folks over 15, and the average per capita lagged-distributed income [ 15 ] . It's on a scale from 0 to 1 and gives you an idea of social and national progress; as the value goes up, so does development. This mountain of data gives us a solid base for digging into how much high sodium intake contributes to CKD around the world. 2.3 Study analysis All statistical analyses were conducted using R software, with a significance level of p<0.05 chosen for statistical significance. 2.3.1 Preliminary analysis To analyze evolving patterns in CKD prevalence over time, we employed least-squares regression modeling along with estimated annual percentage change (EAPC) metrics to quantify shifts in age-standardized incidence rates (ASIRs) during designated observation periods [ 16 ] . The EAPC serves as a key indicator for tracking ASR trajectories—when both its value and 95% confidence interval (CI) exceed zero, this signals a consistent annual uptick in ASIRs, whereas values below zero reflect a downward trend. For mapping the worldwide footprint of CKD cases linked to excessive sodium consumption, we leveraged R's ggplot2 and sf packages (version 4.3.3) to generate detailed cartographic representations, enabling cross-regional comparisons. Drawing from GBD datasets, we visualized geographic disparities in CKD burden attributable to high sodium intake. Our population-level investigation disaggregated male and female prevalence rates by age cohort. Additionally, we examined correlations between SDI and sodium-related CKD impacts. All data processing was performed using R's dplyr package, with ggplot2 facilitating graphical output. 2.3.2 Joinpoint regression analysis The Joinpoint regression model can identify one or more inflection points in time - series data, segmenting it into intervals. Each interval's trend is fitted with linear regression, to more accurately describe the characteristics of disease changes.The model can be used to investigate the impact of high sodium intake on CKD mortality, DALYs, YLDs, and YLLs.In the Joinpoint model, we start with the null hypothesis and fit a simple linear regression model. Changes in CKD-related variables are initially assumed to be linear. We then incrementally increase the number of joinpoints to improve the model fit. Finally, we conduct a permutation test to compare the goodness of fit between the null hypothesis (no joinpoints) and the alternative hypothesis (joinpoints exist). This process helps us select the best-fitting model. The annual percentage change (APC), the average annual percentage change (AAPC), and their 95% confidence interval (CI) are important outcome indicators. In the Joinpoint regression model, the annual percentage change (APC) describes the annual change rate of CKD within each trend segment. The average annual percentage change (AAPC) measures the average annual change rate of an indicator over a specific time span.Their 95% confidence interval (CI) can help determine if study results are clinically significant. If the lower CI limit exceeds the predefined clinical significance threshold, the intervention is deemed clinically valuable; otherwise, it may not be effective. When the P-value is below 0.05, the CI aids in further assessing result reliability. A narrow CI that is distant from zero indicates more reliable results, whereas a wider CI near zero suggests greater uncertainty. 2.3.3 Age-Period-Cohort (APC) model The Age-Period-Cohort (APC) model is a statistical model that can analyze phenomena over time.To conduct APC research, it is necessary to establish a multivariate regression model: $$\:{Y}_{t}={\beta\:}_{0}+{\beta\:}_{1}{\:Age\:}_{t}+{\beta\:}_{2}{\:Period\:}_{t}+{\beta\:}_{3}{\:Coℎort\:}_{t}+{ϵ}_{t}$$ In a multiple regression model, \(\:{\text{Y}}_{\text{t}}\) represents the dependent variable,while \(\:{\:\text{A}\text{g}\text{e}\:}_{\text{t}}\) , \(\:{\:\text{P}\text{e}\text{r}\text{i}\text{o}\text{d}\:}_{\text{t}}\:\text{a}\text{n}\text{d}{\:\text{C}\text{o}\text{ℎ}\text{o}\text{r}\text{t}\:}_{\text{t}}\) are independent variables that can predict it.. \(\:{\beta\:}_{0}\) is the intercept, indicating the expected value of Y when all independent variables are zero. \(\:{ϵ}_{t}\) is the error term, representing the influence of excluded factors on \(\:{\text{Y}}_{\text{t}}\) . In this study, age groups and observation periods were divided into 20 - year and 5 - year intervals [ 17 ] . Mortality and DALYs were examined independently, based on data from 1992 to 2021. We'll assess CKD burden trends across age groups, time periods, and birth cohorts in order to uncover the dynamic link between high - sodium intake and CKD. This approach not only enhances our understanding of how age, period, and cohort effects influence the relationship between high - sodium intake and CKD but also offers a scientific basis for targeted prevention and intervention strategies. 3. Results 3.1 Global distribution and trends in chronic kidney disease attributable to high sodium intake from 1990 to 2021 As the global obesity rate increased, 69954.35 deaths (95%UI:7889.2-198557.7) in 2021, up 1 3.7 times from 1990 (2,6072.12,95%UI: 4,2 4 6.9366,433.84). In 2021, global DALYs cases due to chronic kidney disease associated with high sodium intake reached 1,705,324.92 (95%UI 220:220,083.84,681,197.81), an increase of 135% over 1990. The global ASMR for chronic kidney disease due to high sodium intake was 0.84 per 100 000 in 2021 (95% UI: 0.092.39) and EAPC 14.74% from 1990 to 2021. Similarly, the ASDR increased by 6.8% during this period (EAPC: -29.0319.18) (Tables 1 and 2). Globally, the number of deaths and disability adjustment years associated with high Bmi-associated chronic kidney disease in the regional, gender and SDI quintiles surged across all GBD regions between 1990 and 2021.2021, Latin America and Caribbean ASMR(1.49,95%UI:0.07–4.42) and ASDR(33.21,95%UI:1.66–98.54) are both the highest. The ASMR for men increased from 0.98 (95% UI: 0.16,2.50) per 100,00 0 to 1.13 (95% UI: 0.14,3.05) in 2021, with an estimated annual percentage change (EAPC) of 0.15% (95% CI: -0.23,0.32). In contrast, the ASMR for women increased from 0.56 (95% UI: 0.07,1.53) in 1990 to 0.6 1 (95% UI: 0.05,1.87) in 2021, with a slight decrease in growth rate (EAPC: 0.10%, 95% CI: -0.40,0.29). It is noteworthy that the deaths and ASMR numbers remained consistently higher in men than in women, although the increasing trend in male health burden was more pronounced during this 30-year period. Similarly, ASDR followed ASMR and showed similar trends.Strikingly, ASMR and ASDR increased by 20.73% and 6.77% in high-income countries, while they decreased in low SDI regions (ASMR-1.7%, ASDR-6.27%), highlighting different trends across socioeconomic strata (Tables 1 and 2). Table 1. Deaths cases and ASMR per 100,00 population of chronic kidney disease attributable to high sodium intake in 1990 and 2021. Characteristics 1990 2021 1990–2021 Deaths.Cases.No.(95% UI) ASMR per 100,000 No.(95% UI) Deaths.Cases.No.(95% UI) ASMR per 100,000 No.(95% UI) EAPC(%) in ASMR No.(95% CI) Global 26072.12 (4246.93,66433.84) 0.73 (0.11,1.92) 69954.35 (7889.2,198557.7) 0.84 (0.09,2.39) 14.74 (-26.06,28.7) Central Europe, Eastern Europe,and Central Asia 2032.16 (457.42,4648.64) 0.45 (0.1,1.04) 3459.64 (609.06,8371.28) 0.53 (0.09,1.28) 16.69 (-11.59,32.18) High-income 5086.65 (492.96,15301.53) 0.42 (0.04,1.26) 13795.9 (530.98,45520.79) 0.52 (0.02,1.69) 24.17 (-50.06,40.01) Latin America and Caribbean 2166.3 (120.11,6280.93) 1.15 (0.06,3.35) 8975.91 (432.14,26627.22) 1.49 (0.07,4.42) 29.68 (0.19,47.61) North Africa and Middle East 383.49 (0.1,2057.91) 0.27 (0,1.48) 1304.98 (0.28,7337.82) 0.33 (0,1.88) 24.02 (-56.11,126.57) South Asia 2139.39 (67.32,7034.18) 0.43 (0.01,1.43) 7738.56 (300.38,25010.64) 0.57 (0.02,1.89) 34.28 (5.85,136.67) Southeast Asia, East Asia, and Oceania 12324.44 (2826.15,26697.38) 1.31 (0.27,2.94) 30331.54 (5754.85,71416.33) 1.14 (0.2,2.73) -13.36 (-40.46,3.38) Sub-Saharan Africa 1939.7 (99.99,6362.54) 1.13 (0.06,3.74) 4347.83 (116.86,15343.49) 1.22 (0.03,4.32) 8.13 (-50.15,26.31) Gender Male 14888.73(2718.36, 36728.29) 0.98(0.16, 2.50) 41258.41(5491.44, 110313.72) 1.13(0.14, 3.05) 0.15(-0.23, 0.32) Female 11183.39(1469.36, 30401.28) 0.56(0.07, 1.53) 28695.95(2127.65, 87893.53) 0.61(0.05, 1.87) 0.10(-0.40, 0.29) Socio-demographic Index (SDI) Low SDI 1795.34 (94.44,5692.36) 1 (0.05,3.18) 3831.27 (113.47,13219.56) 0.98 (0.03,3.36) -1.7 (-51.36,13.45) Low-middle SDI 3760.47 (394.39,10649.52) 0.71 (0.07,2.08) 11649.55 (785.27,35184.71) 0.91 (0.06,2.8) 27.35 (-25.96,47.95) Middle SDI 10017.04 (1894.02,23472.38) 1.16 (0.2,2.8) 28460.22 (3690.07,74413.73) 1.15 (0.14,3.05) -1.07 (-37.62,14.69) High-middle SDI 5330.06 (1064.78,13039.03) 0.59 (0.11,1.5) 12246.3 (2072.28,30636.39) 0.63 (0.1,1.59) 5.6 (-19.85,22.66) High SDI 5128.37 (688.25,14172.24) 0.46 (0.06,1.28) 13688.92 (777.44,43392.05) 0.55 (0.03,1.73) 20.73 (-44.25,39.98) Table 2. DALYs cases and ASDR per 100,00 population of chronic kidney disease attributable to high sodium intake in 1990 and 2021. Characteristics 1990 2021 1990–2021 DALYs.Cases.No.(95% UI) ASDR per 100,000 No.(95% UI) DALYs.Cases.No.(95% UI) ASDR per 100,000 No.(95% UI) EAPC(%) in ASDR No.(95% CI) Global 725260.87 (124311.77,1838069.14) 18.55 (3.12,47.36) 1705324.92 (220083.8,4681197.81) 19.81 (2.51,54.57) 6.8 (-29.03,19.18) Central Europe, Eastern Europe,and Central Asia 65799.96 (13432.03,159239.35) 14.25 (2.91,34.69) 88958.78 (14460.29,222349.27) 13.95 (2.28,35.25) -2.12 (-27.23,9.73) High-income 120428.85 (14165.91,352239.05) 10.03 (1.18,29.2) 253445.48 (10326.44,806963.15) 11.05 (0.47,34.65) 10.16 (-58.85,24.95) Latin America and Caribbean 54968.69 (3168.11,158913.12) 25.72 (1.5,74.5) 205635.13 (10343.06,611879.08) 33.21 (1.66,98.54) 29.12 (0.45,46.41) North Africa and Middle East 9828.95 (4.66,50759.79) 5.91 (0,31.37) 32148.55 (15.14,176203.28) 7.13 (0,39.56) 20.72 (-46.63,121.33) South Asia 73703.12 (2775.13,236530.83) 12.56 (0.4,41.31) 247605.03 (12358.69,781215.27) 16.45 (0.74,52.26) 30.99 (7.41,119.63) Southeast Asia, East Asia, and Oceania 353455.3 (88499.01,753579.54) 32 (7.43,68.93) 776640.56 (156206.38,1775603.94) 27.1 (5.43,62.61) -15.31 (-40.08,0) Sub-Saharan Africa 47076.01 (2332.78,150716.3) 23.63 (1.16,77.34) 100891.39 (2526.95,363466.49) 23.57 (0.63,83.44) -0.25 (-55.86,17.33) Gender Male 423298.08(82118.91, 1038086.63) 23.85(4.46, 59.34) 1036223.17(155928.51, 2709714.31) 26.08(3.79, 69.13) 0.09(-0.24, 0.25) Female 301962.79(44927.55, 805882.05) 14.34(2.08, 38.36) 669101.75(60366.41, 2007776.58) 14.46(1.31, 43.24) 0.01(-0.43, 0.19) Socio-demographic Index (SDI) Low SDI 46201.71 (2264.17,147578.86) 21.82 (1.07,69.87) 95543.58 (2646.35,323953.38) 20.45 (0.58,69.68) -6.27 (-52.63,8.07) Low-middle SDI 112167.91 (12388.38,320782.14) 18.5 (1.91,53.75) 321546.95 (23277.87,965256.55) 22.46 (1.55,67.31) 21.43 (-26.95,39.34) Middle SDI 284540.08 (57141.7,667749.92) 28.14 (5.33,66.75) 719210.46 (105004.97,1847364.2) 26.85 (3.78,68.88) -4.6 (-39.35,11.02) High-middle SDI 157146.73 (34246.21,370847.35) 16.07 (3.37,38.64) 306152.43 (58076.91,741209.29) 15.48 (2.94,37.86) -3.67 (-23.3,10.4) High SDI 124125.49 (18641.66,340284.61) 11.21 (1.69,30.63) 261115.11 (17114.56,799429.17) 11.97 (0.84,36.25) 6.77 (-50.87,23.56) 3.2 Global burden analysis The global distribution of ASMR and ASDR for CKD due to high sodium intake in 2021 shows significant geographical and national differences. Both ASMR and ASDR burden are mainly concentrated in South Latin America, Central America, Central Europe, Africa, East Asia and Southeast Asia, with the highest regions (> 1.2115 per 100,000 people) including Mexico, Venezuela, Bolivia, Hungary, Central Africa, Central Africa, Tanzania, Lao Guo, Malaysia, Philippines and other countries (Fig. 1A and B). The EAPC trends from 1990 to 2021 highlight the dynamic changes. ASMR and ASDR in North America, Northern Europe, Northern Europe, Eastern Europe, North Africa and Central Asia, including Norway, Sweden, Germany, Netherlands, Belarus, Ukraine, Libya, Egypt, Ghana, Pakistan, Nepal and other countries; China, Mongolia, Japan, Germany, Ethiopia and Ethiopia have significantly lower burden (Fig. 1A and B). It is worth noting that Italy and Russia have been showing a low burden. 3.3 Analysis of regional differences and health inequalities The relationship between SDI and ASMR and ASDR of chronic kidney disease is non-linear both globally and throughout the 21 GBD regions. When SDI was below 0.75, the burden increased significantly with increasing SDI. However, further increases were associated with a decrease in SCI burden. High sdi regions, such as high income South Latin America, Central Europe, high income North America, and Australasia, are clustered at the upper end of the ASMR and ASDR ranges. In contrast, regions with low SDI, including eastern Sub-Saharan Africa, western Sub-Saharan Africa, central sub-Saharan Africa, Oceania, South Asia and West Asia, exhibited lower disease burden. It is worth noting that despite the high-income SDI region, ASMR and ASDR values are much lower than other high SDI regions (Fig. 2A and B). Globally, the burden of kidney cancer due to high salt intake was concentrated in low SDI regions in 1990 and 2021, and inequality worsened among people with different SDI rankings. The health inequality index remained negative from 1990 to 2021 and decreased with rising SDI, indicating a greater burden of kidney cancer in areas with low SDI. The health inequality index for mortality and DALYs decreased from 1990 to 2021 (Deaths: -0.68 to-1.49; DALYs: -17.180 to-33.76), indicating worsening inequality status (Fig. 3A and B). The deaths and DALYs for 1990 and 2021 were consistently large and slightly improved (Deaths: 0.742 to 0.705; DALYs: 0.751 to 0.707) (Fig. 3C and D). China and India have a large population base, and even a reduced change in proportion can have a significant impact on health equality issues. From 1990 to 2021, the proportion of mortality and disability adjusted life years decreased in China, while mortality and disability adjusted life years decreased in India. 3.4 Age and sex differences and temporal trends in chronic kidney disease attributable to high sodium intake The global burden of chronic kidney disease caused by high sodium intake exhibits different demographic and temporal patterns. Male disease burden than women, but continued to grow throughout 1990–2021. Death and DALY were consistently higher in men than in women in almost all age groups, and this difference was more pronounced in the 55–79 year age group (Fig. 4A, B). In 2021, peak deaths associated with high BMI occurred in individuals aged 65–79 years, reflecting the high vulnerability of this age group (Fig. 4A). However, the loss of disability caused by chronic kidney disease was more concentrated in the age group of 65–69 years, and the lower the age group (Fig. 4B). From 1990 to 2021, the number of ckd-related deaths and the disability-adjusted life-adjusted years continued to increase, with steady growth rates, both reaching an unprecedented peak in 2021 (Fig. 4C and D). The burden of disease increased with age in both men and women, but women were more affected by changes in age. The mortality and disability rates in men and women gradually increased from 50 to 54 years, increased significantly from 80 to 84 years and peaked in the 95 + years age group. Note a clear gender imbalance in the disease burden of men and women after the age of 80 years (Fig. 5A and B). The number of deaths and disability loss years increased with age and showed a downward trend in later ages. Male deaths were mainly concentrated in the 65–89 age group and reached a peak of 3,681 cases in the 75–79 year age group. Female deaths were mainly concentrated in the 65–79 age group and reached a peak of 6,247 cases in the 70–74 year age group (Fig. 5C). Year of disability loss was more influenced by age than deaths and peaked in the 65–69 age group for both males and females (male: 109514; female: 174994) (Fig. 5D). 3.5 Combined point regression analysis Combined point regression analysis showed that ASMR and ASDR due to high sodium intake in global CKD in 1990–2021. Overall, ASMR and ASDR showed an increasing trend, but showed a nonsignificant downward trend in 2004–2007. The trend of disease burden was roughly similar in men and women, but the burden was significantly greater in men, and the difference between men and women was more pronounced in ASDR. Specifically, the ASMR of CKD increased overall from 1990 to 2021 (AAPC = 0.48%; 95%CI: 0.37–0.58%; P 0.05) (Fig. 6A and Supplementary Table 1). ASDR also showed a global upward trend (AAPC = 0.21%; 95%CI: 0.13–0.29%; P < 0.001) with the fastest growth from 1997 to 2004 (APC = 0.71%; 95%CI: 0.62–0.81%; P 0.05) (Fig. 6B and Supplementary Table 2). Similarly, YLLs of CKD continued to rise from 1990 to 2021 (AAPC = 0.29%; 95%CI: 0.19–0.40%; P 0.05) (Fig. 6C and Supplementary Table 3). In contrast, YLDs were more complex and volatile, decreasing against YLLs in 1990–1996 and 2015–2019, and increasing the most in 2019–2021 (APC = 1.82%; 95%CI: 1.38–2.27%; P < 0.001) (Fig. 6D and Supplementary Table 4). 3.6 Results of the Age-Period-Cohort analysis For Deaths, local drifts showed an annual percentage increase with age, with the most pronounced drift observed in the 80's population (Fig. 8A). The The longitudinal age curve and cross-sectional age patterns showed that mortality was positively associated with age and rose sharply after the age of 80 years (Fig. 8B and 7C). The period effect showed a progressive increase in rates overall, indicating that the effect of high sodium intake on CKD mortality was amplified over time. However, the 2010 ratio was significantly lower than that in 2005 (Fig. 8D). Cohort analysis found that the risk gradually increased in the birth cohort since 1900, and gradually decreased after reaching its peak in 1950 (Fig. 8E). DALYs showed a similar trend to Deaths in the results of Age-Period-Cohort Analysis (Fig. 8F, G, H, I and J). Notably, the disability loss rate on the longitudinal age curve and cross-sectional age patterns increases dates mortality from age 50. 3.6.1 Future projections From 2021 to 2040, global ASMR trends are stabilizing, but the differences between men and women are significant. Female ASMR is on the rise, while male ASMR is declining. In terms of ASDR, both globally, among women, and among men, ASDR is increasing. From 1990 to 2021, male ASMR and DALY rates have consistently been higher than those of females. This gender disparity is expected to persist for a long time, with men continuing to bear a greater burden of CKD compared to women (Fig. 8A and B). 4. Discussion The global burden of CKD skyrocketed due to excessive sodium consumption between 1990 and 2021. Deaths related to CKD surged 13.7-fold, while DALYs shot up by 135%. Despite these substantial jumps, the increases in adjusted deaths and DALYs were relatively moderate. This pattern aligns with what we observed in the 1990–2019 study [ 18 ] , and it's only gotten more intense. The reasons for this include shifts in eating habits, population growth, and an aging demographic. The CKD burden from high sodium levels varies widely across countries and regions, and it's intricately linked to SDI levels. The most severe cases are found in Latin America, Sub-Saharan Africa, and Southeast Asia, where SDI is low. On the other hand, North America and Northern Europe have seen the fastest growth in CKD rates, correlating with higher SDI levels. Over the past 30 years, the burden of disease has been higher in men than in women before the age of 90, and the increasing trend is more obvious. The above findings suggest the need for targeted public health policies and interventions to mitigate the rising burden of CKD associated with high sodium intake factors. In 2021, the burden of CKD associated with high sodium intake was globally poor and the variable trends vary across countries. Instead, the low burden areas rose, and the high burden areas showed a low annual growth rate, especially in China, Mongolia, Japan and other countries, the burden was significantly reduced. Since 2007, China has really taken the bull by the horns when it comes to lowering sodium consumption. They've done things like fast-tracking changes to nutrition labels on packaged foods to make them more in line with current nutritional science, and they've been working hard to get the word out about healthy eating habits. Just to keep your body running smoothly, you really only need about 200 to 500 mg of sodium each day – anything more than that is often overkill [ 19 ] . Therefore, the decrease in the average daily sodium intake of Chinese residents from 5.9 grams in 2002 to 5.2 grams in 2012 [ 20 ] may have a positive impact on reducing the disease burden. The burden in Italy has been low, reflecting adherence to the Mediterranean diet pattern and focus on salt intake [ 21 – 22 ] . The low burden in Russia is mainly due to the early diagnosis methods. Researchers at Qiuming National Medical University (TyumSMU), a member of the Russian Ministry of Health, proposed a method using positron emission and computed tomography (PET / CT) to diagnose chronic kidney disease before kidney function problems [ 23 ] . This approach evaluates the molecular cell viability of tissues by observing the fixation of labeled glucose molecules in the kidney cells, thereby identifying early chronic kidney disease beyond any existing diagnostic method. Studies [ 24 ] indicates that people typically hang onto their cultural or ethnic heritage through their dietary habits, and this, in turn, shapes their food choices. It's also crucial to realize that where people get their salt from differs drastically from country to country, meaning that different approaches are needed. For instance, in nations where processed foods are the main culprit for high salt intake, the focus should be on creating and enforcing regulations for the food industry. On the other hand, in countries where home cooking is the primary source of salt, we need to put more effort into encouraging individuals to change their habits, as consumer decisions still hold significant sway. By the same token, while policy interventions are always important, we might need to put more stock in strategies that motivate and enable individuals to cut back on salt when they're cooking. These points underscore just how complex and varied attitudes toward sodium reduction can be, and highlight the need for sodium reduction strategies that are customized to a wide range of cultural and socio-demographic factors [ 25 ] . SDI presents a nonlinear complex relationship with the disease burden of chronic kidney disease, mainly in low SDI areas, and inequality has worsened among populations with different SDI rankings. Compared with low SDI areas, residents of high SDI areas have easy access to advanced education and better health care, as well as coordinated prevention efforts, so the burden is reduced. The Asia-Pacific region is clearly different from other high SDI regions with a low disease burden, indicating the effectiveness of targeted sodium reduction policies. The autonomy of sodium reduction can be improved by improving the overall health awareness of the population or by paying wider attention to nutrition [ 26 ] . While the ASMR and ASDR in the medium SDI regions have exceeded the moderately low SDI and low SDI regions. This region lies between low and high levels of SDI, facing unique challenges related to healthcare infrastructure and disease patterns, such as the transition from infectious diseases to chronic diseases [ 27 ] . It may also be related to insufficient case reporting and delayed sodium reduction policy in low and medium SDI and low SDI areas. China and India have a large population base, and even a reduced change in proportion can have a significant impact on health equality issues. From 1990 to 2021, mortality and disability-adjusted life year ratios in China declined, while mortality and disability-adjusted life year values in India decreased. It is noteworthy that despite reduced age-standardized rates, China and India remain key factors in the absolute CKD burden due to their large population and continuous high sodium intake. This underscores the dual challenges of addressing population growth and cultural dietary practices. Men tend to experience a higher disease burden compared to women, partly due to their greater urinary sodium excretion (247 mmol/day versus women’s 218 mmol/day) [ 28 ] . This physiological difference suggests that men may face increased exposure to high sodium levels, potentially exacerbating health risks. Additionally, men are more prone to engaging in harmful habits like smoking and excessive drinking [ 29 ] , both of which are well-established contributors to chronic non-communicable diseases. These behavioral patterns further compound the health disparities between genders. Previously, a study showed that smokers preferred salty foods, leading to the occurrence of CKD with high salt intake. A study with 49,558 participants (50.3% women, 49.7% men) [ 30 ] showed that men were more likely to die from CKD than women, which is in line with our study results. In addition, the negative impact of exposure to high salt intake was significantly lower in women [ 31 ] . It is worth noting that, although access to living donor kidneys seems equal between men and women, women reduce the chance of being transplanted by the donor of the deceased [ 32 ] , so they cannot relax concerns about women's health issues. This study also showed that the burden of chronic kidney disease associated with high salt intake was also positively associated with age, reflecting the decline in renal function and vascular compliance [ 33 ] , highlighting the need for targeted intervention and age, especially for the aging population. Early on in life, managing sodium intake can shape one's taste bud acuity and influence their desire and adaptability to salt as they grow older. Thus, it's vital to prioritize the avoidance of excessive salt consumption among the youth. Between 1990 and 2021, there's been a noticeable uptick in ASMR and ASDR cases tied to high sodium consumption, which mirrors the growth and greying of the global population. It's a given that the incidence of CKD and its risk factors will mirror this aging demographic. We're talking about a scenario where the prevalence of CKD in categories G3-G5 could soar past 10% by 2050, leading to massive health and economic repercussions, especially for low-income nations [ 34 ] . Head over to the WHO's online portal—it's the source where they released their groundbreaking "Global Sodium Reduction" report. It's no secret that we're falling short of our goal to slash sodium intake by 30% by 2025. The WHO is urging member states to act fast and put policies in place to tackle this issue head-on, mitigating the damaging impacts of too much salt. However, it's only nine countries—Brazil, Chile, the Czech Republic, Lithuania, Malaysia, Mexico, Saudi Arabia, Spain, and Uruguay—that have put forth a full range of sodium reduction strategies, making it clear how critical it is to tackle these risk factors. Note that the disease burden showed a clear decreasing trend between 2004–2007. Although kidney disease is commonly classified as a non-communicable disease, infection is also an important etiology, directly through kidney involvement (e.g.,leptospirosis or HIV infection) or indirectly through infection-associated glomerulonephritis, hemodynamic mechanisms, or systemic inflammatory responses [ 35 – 36 ] . Leptospires seropositivity was a risk factor in Central America in areas with high burden of chronic kidney disease in this study [ 37 ] , potentially contributing to unidentified CKD variants or heightened vulnerability to triggers like heat exposure [ 38 ] . So it is reasonable to speculate that the decline in ASMR and ASDR of chronic kidney disease associated with high salt intake is indirectly affected by the first global public health emergency of the 21st century —— the severe acute respiratory syndrome (SARS) virus epidemic in 2003, increasing global awareness of protection and reducing the risk of infection. We also note that the temporal trend of YLLs, although consistent with the DALYs trend, the changes in YLDs are more complex. Post−2019 YLD s. soared, highlighting the long-term disability burden and the need for early life intervention to mitigate lifetime kidney damage. By exposing the differential effects of aging, temporal patterns, and risk in specific birth cohorts, the age-cycle cohort analysis provides important insights into the temporal dynamics of renal cancer burden associated with high sodium intake. Mortality and disability loss from kidney cancer associated with high sodium intake increased significantly after age 50 and increased sharply after age 80, emphasizing aging as a key driver of the burden of disease burden. The period effect showed a progressive increase in rates overall, indicating that the effect of high sodium intake on CKD mortality was amplified over time. Excessive sodium intake will increase the metabolic burden of the kidneys, leading to sodium and water retention, oedema, and have a negative effect on kidney function [ 39 ] and the rehabilitation of kidney patients.However, the ratio showed a clear downward trend between 2005 and 2007, consistent with the results of the combined point regression analysis in this study and speculated to be due to the indirect effects of the SARS virus epidemic and the global economic crisis. Rising food prices since 2005 [ 40 ] , and the 2008 financial crisis caused reduced revenues and higher food costs, have further weakened the ability to prioritize food quality and reduced salt intake [ 41 ] . The cohort trends showed a decreased risk in the cohort after 1950, reflecting an increased health awareness of CKD due to high sodium intake. Study [ 42 ] has shown that, the number of salt reduction programs around the world has increased, and more and more countries are choosing structural or regulatory approaches. However, urgent accelerated and repeated efforts in other countries, especially in low SDI areas, must require stricter monitoring and evaluation of strategies to achieve salt reduction targets. Looking ahead to 2040, forecasts regarding CKD linked to high sodium consumption reveal some striking differences between men and women, alongside shifting disease patterns. While the overall death rate appears to be plateauing, the diverging trends—an uptick in mortality for women coupled with a dip for men—suggest underlying variations in biology, lifestyle, and dietary habits. Simultaneously, the rising disability rate in both sexes paints a picture of an increasing burden, despite strides made in managing high blood pressure and CKD. This persistent rise in female mortality might well be chalked up to how differently women respond to salt. It's known that women, particularly after menopause, tend to see their blood pressure spike more readily with high sodium intake, likely due to the drop in estrogen, which normally helps the kidneys flush out sodium [ 43 ] . Men continue to exhibit higher ASMR for chronic kidney disease compared to women, though this gap is gradually narrowing. This downward trend coincides with growing health consciousness among male populations [ 44 ] , prompting more men to proactively monitor their wellbeing for early disease detection and timely intervention. Since 1990, men have made significant strides in preventive healthcare—advancements that have ultimately created distinct mortality trajectories between the sexes. Meanwhile, rising ASDR reveal a troubling paradox: while mortality figures stabilize, excessive sodium consumption continues to inflict cumulative renal damage. Loading up on salt really throws a wrench into kidney function by worsening protein in the urine and overworking the kidney's filters [ 45 ] . This pushes kidney disease forward, even if blood pressure is under control. It just goes to show how bad dietary habits can lead to long-term health problems, even if they don't seem immediately life-threatening. Popping RAAS inhibitors might buy some time before end-stage renal disease kicks in, but it could also mean a longer period of being disabled [ 46 ] . Plus, salt-related vascular problems, like an enlarged heart [ 47 ] , add insult to injury when it comes to chronic kidney disease. If we're serious about tackling the growing problem of chronic kidney disease, we've got to get serious about public health initiatives aimed at cutting down on sodium consumption. In China's countryside, a primary focus should be placed on trimming the salt shaker in home-cooked meals. Across the pond in Japan, the UK, and the States, cutting down on the sodium in packaged goods is a no-brainer [ 48 ] . And let's not forget the gender gap—specific health services need to be beefed up, from boosting cancer screenings for the fairer sex to spreading the health gospel among women and upping men's health game. Men and women face different health hazards, so it's crucial to have shrewd policies and better medical service frameworks to lessen the diseases' toll on our health and lift the health bar for everyone on the planet. Moreover, the aftermath of diseases, like disabilities, needs a close look. We should fine-tune the full spectrum of disease prevention, treatment, and rehab care. Internationally, we've got to beef up community rehab centers, train rehab professionals to a higher standard, guarantee that patients get quick and efficient rehab after their recovery, and slash the number of years cut short by disabilities. While this study provides a comprehensive analysis of the global burden of CKD caused by high sodium intake and makes future projections for the first time, some limitations should be acknowledged. The study relies on data from GBD 2021 database. Variations in data quality and reporting standards across countries may affect the accuracy of our estimates, particularly in low- and middle-income countries where data collection systems may be less robust. The study assumes a direct causal relationship between high sodium intake and CKD. However, sodium intake is often estimated based on dietary surveys, which may not fully capture individual consumption patterns. Additionally, other dietary and lifestyle factors (e.g., potassium intake, physical activity) that could influence CKD risk were not accounted for in this analysis. While the study identifies trends over a 30-year period, it does not account for potential confounding factors such as changes in healthcare access, advancements in medical treatments, or shifts in dietary patterns over time. These factors could influence the observed trends in CKD burden. The findings are based on global and regional aggregates, which may not fully reflect the heterogeneity within individual countries or subpopulations. Localized studies are needed to tailor interventions to specific contexts. Conclusion The study highlights a significant global rise in CKD burden due to high sodium intake, with marked disparities across regions and demographics. Targeted interventions, such as sodium reduction policies and public health campaigns, are essential to mitigate this growing health challenge, particularly in high-risk populations. Addressing dietary habits and improving healthcare access can help reduce the future impact of CKD. Declarations Data availability statement The original contributions presented in the study are included in the article and Supplementary material, further inquiries can be directed to the corresponding author. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Acknowledgements The authors would like to extend our thanks to the GBD team for allowing us to access their free, comprehensive data base and appreciate the works by the Global Burden of Disease Study 2021 collaborators. Author contributions Yawen Lu and Yongfeng Wang worked on conceptualization and design of this study. Yawen Lu, Jianfeng Ma, Yang Hu and Rumeng Zheng conducted all data collection and analysis and compiled charts. Yongfeng Wang, Yawen Lu, Jianfeng Ma, Yang Hu, Rumeng Zheng, Liping Liu, Kaili Lin and Kun Zhangwrote the first draft of the manuscript. Yongfeng Wang and Sheng Li managed publications process, had full access to all the data in the study, and had final responsibility for the decision. All authors read and approved the final manuscript. Funding This study was funded by the Natural Science Foundation of Gansu Province (24JRRA1201), the Special Project of People's Livelihood Science and Technology for Social Development (2021-004-SF0-0079), the Health and Health Industry Research Project of Lanzhou City (A2024010), and Lanzhou University College Students Innovation and Entrepreneurship Training Program (20250060108). Conflicts of interest The authors declared no conflict of interest in the publication of this paper. References Kovesdy CP. Epidemiology of chronic kidney disease: an update 2022. Kidney international supplements. 2022 Apr 1;12(1):7-11. Yin L, Kuai M, Liu Z, Zou B, Wu P. Global burden of chronic kidney disease due to dietary factors. Frontiers in Nutrition. 2025 Jan 15;11:1522555. Bellasi A, Di Lullo L, Di Iorio B. Chronic kidney disease: the silent epidemy. Journal of Clinical Medicine. 2019 Oct 26;8(11):1795. Lu J, Zhao X, Gan S. 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Journal of the American Dietetic Association. 2010 May 1;110(5):736-45. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6865321","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477489092,"identity":"3dd2bd45-087e-4276-b3cf-7ca882180296","order_by":0,"name":"Yawen Lu","email":"","orcid":"","institution":"The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yawen","middleName":"","lastName":"Lu","suffix":""},{"id":477489093,"identity":"db67d215-35cd-4aab-9de2-36a44cce2a14","order_by":1,"name":"Jianfeng Ma","email":"","orcid":"","institution":"The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jianfeng","middleName":"","lastName":"Ma","suffix":""},{"id":477489094,"identity":"158e8745-0448-489e-b68c-e5aabaca055b","order_by":2,"name":"Yang Hu","email":"","orcid":"","institution":"The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Hu","suffix":""},{"id":477489095,"identity":"ef7fb4f8-f9d3-43b6-9ee9-017478325b08","order_by":3,"name":"Rumeng Zheng","email":"","orcid":"","institution":"The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Rumeng","middleName":"","lastName":"Zheng","suffix":""},{"id":477489096,"identity":"5c85dc86-b0e6-46b1-92cb-104a07fe21c1","order_by":4,"name":"Liping Liu","email":"","orcid":"","institution":"The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Liping","middleName":"","lastName":"Liu","suffix":""},{"id":477489097,"identity":"b4ac8890-bcea-4b7d-bb96-ecfa4f859e86","order_by":5,"name":"Kaili Lin","email":"","orcid":"","institution":"The First People's Hospital of Lanzhou City","correspondingAuthor":false,"prefix":"","firstName":"Kaili","middleName":"","lastName":"Lin","suffix":""},{"id":477489098,"identity":"90abf062-c52e-4d5e-8d07-08e6def7bb9e","order_by":6,"name":"Kun Zhang","email":"","orcid":"","institution":"School of Stomatology Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Zhang","suffix":""},{"id":477489099,"identity":"baca78ff-e3be-4250-8cd8-e48618d9ab71","order_by":7,"name":"Yongfeng Wang","email":"","orcid":"","institution":"General Surgery Clinical Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Yongfeng","middleName":"","lastName":"Wang","suffix":""},{"id":477489100,"identity":"c2ae92a5-6609-46d9-bbaa-76cec56c75f3","order_by":8,"name":"Sheng Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYDCCA2DSgodBAkRXSMjJE6GFsYGBQQKq5YyFsWEDkVrAiIGxrSIRai9uwHe8+fmDj3skZORnNx97+HWeRAJjA/PDRzfwaJE8c8ywccYzCR6DO8fSjWW3SeSxM7AZG+fg0WJwI4exmecAUItEjpm05DaJYsYGHjZporTIz8j/Ji05RyKx4QCxWhhu5LBJfmwgQgvILzNngBx2I81MmuGYhLFhMwG/AEPswYcPB2zs5WckP5P8UVMnJ8/e/PAxPi0ogJkHTBKrHAQYf5CiehSMglEwCkYMAAA6zUrT8HMAxAAAAABJRU5ErkJggg==","orcid":"","institution":"The First Hospital of Lanzhou University","correspondingAuthor":true,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-06-10 17:08:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6865321/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6865321/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85790946,"identity":"6187dca0-3d0b-4349-bfcb-82ad57165ab7","added_by":"auto","created_at":"2025-07-01 17:27:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9805262,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal distribution of ASR of chronic kidney disease Deaths and DALYs attributable to high sodium intake. (A) ASMR in 2021. (B) ASDR in 2021. (C) The EAPC in ASMR, 1990–2021. (D) The EAPC in ASDR, 1990–2021.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6865321/v1/a8f62a589ffbe545237835b2.png"},{"id":85790948,"identity":"a7db5bb0-cf94-43ba-8492-3505f96d8591","added_by":"auto","created_at":"2025-07-01 17:27:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":7642663,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between ASMR, ASDR and SDI in chronic kidney disease attributable to high sodium intake for 204 countries and 21 regions, 1990–2021. The above points show estimates for each country and region. The correlation between ASMR (A) or ASDR (B) and SDI in 21 GBD regions. The association between ASMR (C) or ASDR (D) and SDI in 204 countries.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6865321/v1/8780d42500d797ddda383a5c.png"},{"id":85790950,"identity":"16e5f83a-defb-49e9-8795-e8253aaf9cc8","added_by":"auto","created_at":"2025-07-01 17:27:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4891634,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of death and DALY inequality in chronic kidney disease attributable to high sodium intake in 1990 and 2021. The 1990 and 2021 statistics are displayed in blue and red, respectively. (A) Crude deaths rate per 100,000 by SDI, 1990 and 2021, by relative rank. (B) Crude DALY rate per 100,000 by SDI relative rank, 1990 and 2021. (C) Cumulative deaths rate by cumulative population percentage, ordered by SDI in 1990 and 2021. (D) Cumulative fraction of DALY relative to cumulative fraction of population, 1990 and 2021, sorted by SDI.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6865321/v1/d518b6863ed4741061d7d604.png"},{"id":85790945,"identity":"3a729935-8970-4a4c-99b6-4617b375a373","added_by":"auto","created_at":"2025-07-01 17:27:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2610506,"visible":true,"origin":"","legend":"\u003cp\u003eAge-sex characteristics and temporal patterns of the global burden of chronic kidney disease attributable to high sodium intake. (A) Deaths in all age groups, 2021. (B) DALYs in all age groups, 2021. (C) Deaths during various years, from 1990 to 2021. (D) DALYs during various years. Disability-adjusted life years, or DALYs, from 1990 to 2021.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6865321/v1/272629c21fa2a26fdf284491.png"},{"id":85791162,"identity":"87e359aa-68dd-49b5-8b9d-30510c42fbfd","added_by":"auto","created_at":"2025-07-01 17:35:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1719605,"visible":true,"origin":"","legend":"\u003cp\u003eAge and sex differences in chronic kidney disease attributable to high sodium intake in 2021. (A) Deaths rates for female and male in all age groups. (B) DALYs rates for female and male in all age groups. (C) Deaths cases for female and male in all age groups. (D) DALYs cases for female and male in all age groups.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6865321/v1/964c212b08f70e849da0158b.png"},{"id":85791164,"identity":"1ad4c438-b798-4bb6-aed6-82160940fd82","added_by":"auto","created_at":"2025-07-01 17:35:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5902429,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint regression analysis of chronic kidney disease attributable to high sodium intake burden temporal trends, 1990–2021. (A) ASMR; (B) ASDR; (C) YLLs; (D) YLDs.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6865321/v1/61cc5f7b1bad70773437b000.png"},{"id":85791880,"identity":"053b1d43-e84a-494d-90da-e5ce6e499eed","added_by":"auto","created_at":"2025-07-01 17:51:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1832672,"visible":true,"origin":"","legend":"\u003cp\u003eAge-Period-Cohort Analysis Results for Deaths and DALYs. (A) Local Drifts with Net Drifts in Deaths; (B) Longitudinal Age Curve in Deaths; (C) Cross-Sectional Age Curve in Deaths; (D) Period RR in Deaths. (E) Cohort RR in Deaths; (F) Local Drifts with Net Drifts in DALYs; (G) Longitudinal Age Curve in DALYs; (H) Cross-Sectional Age Curve in DALYs;. (I) Period RR in DALYs; (J) Cohort RR in DALYs.\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6865321/v1/ea5a49b4ab690a54d2e0b3e2.png"},{"id":85790966,"identity":"b7cc076c-e6b1-483e-a2ab-7f19e5118cab","added_by":"auto","created_at":"2025-07-01 17:27:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2317125,"visible":true,"origin":"","legend":"\u003cp\u003eFuture prediction by 2040 for chronic kidney disease attributable to high sodium intake, both globally and by gender. (A) ASMR；(B) ASDR. Observed data are shown as solid lines showing past data. The predicted data are represented as dashed lines showing future trend predictions. The shaded area represents the range of uncertainty in the forecast.\u003c/p\u003e","description":"","filename":"figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6865321/v1/4bcef524a55dfe2ccfcb0297.png"},{"id":100363401,"identity":"c1d8f58c-d55c-4bd3-8b5f-42e57dff1b23","added_by":"auto","created_at":"2026-01-16 07:49:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":34065402,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6865321/v1/6d41fdd8-f129-469a-a151-49a03614a511.pdf"},{"id":85790944,"identity":"37775480-634d-409e-945d-4487599a815b","added_by":"auto","created_at":"2025-07-01 17:27:31","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3529,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.zip","url":"https://assets-eu.researchsquare.com/files/rs-6865321/v1/8b181c7f727c6fd4b655b1eb.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"The global burden of chronic kidney disease (CKD) attributable to high sodium intake: a comprehensive analysis of trends from 1990 to 2021 and burden prediction to 2040","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) has emerged as a growing global health crisis, currently impacting over 800\u0026nbsp;million people\u0026mdash;more than 10% of the world's population\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Alarmingly, these numbers continue to climb year after year. Projections indicate that by 2040, CKD will rank as the fifth leading contributor to reduced life expectancy worldwide\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. While the link between excessive sodium consumption and CKD progression has been established, the full scope of this relationship remains understudied on a global scale. Beyond its devastating health consequences, CKD presents unique clinical challenges, as affected individuals often require particularly complex care\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The economic toll of managing this condition continues to strain healthcare systems across the globe\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSodium is a crucial mineral that's deeply involved in keeping our bodies running smoothly. Given its vital role, it's no surprise that sodium and health have a Goldilocks-type relationship \u0026ndash; you need just the right amount. Too little or too much can lead to health problems\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Some research indicates that too much salt can throw a wrench into how well your kidneys function\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. When you overload on sodium, your body kicks into high gear to maintain balance\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. The kidneys work overtime to flush out the extra salt and water. But, this constant strain can eventually cause lasting damage, potentially leading to chronic kidney disease\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. On the flip side, cutting back on sodium can actually protect your kidneys, even if it doesn't lower your blood pressure\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Experts suggest aiming for less than 2 grams of sodium or 5 grams of salt daily, which translates to about 90 mmol of sodium excreted in urine\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile CKD has been studied extensively, the connection between sodium intake and CKD hasn't received much attention. There's a real dearth of long-term studies tracking how sodium affects the progression of CKD. What's more, this study hasn't really dug deep into how these risk factors play out differently depending on age and gender. We've seen studies that look at individual risk factors \u0026ndash; for example, how high blood pressure\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e affects CKD \u0026ndash; but we still need a complete, soup-to-nuts investigation into the global burden of CKD. Considering how little we know about the shifting patterns, the global burden of CKD attributable to high sodium intake highlights the need for better ways to gauge and keep tabs on how the disease burden and health outcomes are changing over time in various groups.\u003c/p\u003e \u003cp\u003eThe purpose of this study is to comprehensively investigate the global burden of chronic kidney disease as determined by the 2021 Global Burden of Disease (GBD 2021) database, as well as the trends in high sodium intake across different categories from 1990 to 2021, including age, sex, social demographic index (SDI) quintiles, GBD subcontinental regions, and countries. We also conducted breakpoint regression analysis using an age-period-cohort model to examine the temporal patterns of the disease over the past three decades. Finally, we made the first future projections for chronic kidney disease caused by high sodium intake, extending these projections to 2041. These findings provide critical information for formulating policies and initiatives aimed at reducing global high sodium intake, preventing chronic kidney disease, and protecting high-risk populations.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source and definitions\u003c/h2\u003e \u003cp\u003eThe research harnessed the GBD 2021 database to paint a picture of the global disease load connected to CKD linked to excessive sodium consumption. The GBD initiative draws on a variety of sources, including census figures, civil records, vital statistics, disease registries, household surveys, health service usage, and air pollution sensors, among other things\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. CKD was defined as estimated glomerular filtration rate (eGFR) of \u0026lt;\u0026thinsp;60 mL/min per 1.73 m2\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. High sodium intake was defined as an average 24h urinary sodium excretion (in grams per day) greater than 1\u0026ndash;5 g\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Previous studies\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e have detailed its assessment, including data selection and inputs.\u003c/p\u003e \u003cp\u003e2.2 Basic study variables\u003c/p\u003e \u003cp\u003eWe gathered estimates of the CKD burden, including disability-adjusted life years (DALYs) and mortality figures. DALYs are figured out by simply adding up Years Lived with Disability (YLDs) and Years of Life Lost (YLLs). For the GBD 2021 standard population, we crunched the numbers to get age-standardized rates (average rates per 100,000 folks) using the direct standardization method. All age-standardized mortality rates (ASMRs) and age-standardized DALY rates (ASDRs) were adjusted for age. The SDI, a composite measure, is made up of three things: the total fertility rate for the under\u0026minus;25s, the average education level of folks over 15, and the average per capita lagged-distributed income\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. It's on a scale from 0 to 1 and gives you an idea of social and national progress; as the value goes up, so does development. This mountain of data gives us a solid base for digging into how much high sodium intake contributes to CKD around the world.\u003c/p\u003e \u003cp\u003e2.3 Study analysis\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using R software, with a significance level of p\u0026lt;0.05 chosen for statistical significance.\u003c/p\u003e \u003cp\u003e2.3.1 Preliminary analysis\u003c/p\u003e \u003cp\u003eTo analyze evolving patterns in CKD prevalence over time, we employed least-squares regression modeling along with estimated annual percentage change (EAPC) metrics to quantify shifts in age-standardized incidence rates (ASIRs) during designated observation periods\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. The EAPC serves as a key indicator for tracking ASR trajectories\u0026mdash;when both its value and 95% confidence interval (CI) exceed zero, this signals a consistent annual uptick in ASIRs, whereas values below zero reflect a downward trend.\u003c/p\u003e \u003cp\u003eFor mapping the worldwide footprint of CKD cases linked to excessive sodium consumption, we leveraged R's ggplot2 and sf packages (version 4.3.3) to generate detailed cartographic representations, enabling cross-regional comparisons. Drawing from GBD datasets, we visualized geographic disparities in CKD burden attributable to high sodium intake. Our population-level investigation disaggregated male and female prevalence rates by age cohort. Additionally, we examined correlations between SDI and sodium-related CKD impacts. All data processing was performed using R's dplyr package, with ggplot2 facilitating graphical output.\u003c/p\u003e \u003cp\u003e2.3.2 Joinpoint regression analysis\u003c/p\u003e \u003cp\u003eThe Joinpoint regression model can identify one or more inflection points in time - series data, segmenting it into intervals. Each interval's trend is fitted with linear regression, to more accurately describe the characteristics of disease changes.The model can be used to investigate the impact of high sodium intake on CKD mortality, DALYs, YLDs, and YLLs.In the Joinpoint model, we start with the null hypothesis and fit a simple linear regression model. Changes in CKD-related variables are initially assumed to be linear. We then incrementally increase the number of joinpoints to improve the model fit. Finally, we conduct a permutation test to compare the goodness of fit between the null hypothesis (no joinpoints) and the alternative hypothesis (joinpoints exist). This process helps us select the best-fitting model.\u003c/p\u003e \u003cp\u003eThe annual percentage change (APC), the average annual percentage change (AAPC), and their 95% confidence interval (CI) are important outcome indicators. In the Joinpoint regression model, the annual percentage change (APC) describes the annual change rate of CKD within each trend segment. The average annual percentage change (AAPC) measures the average annual change rate of an indicator over a specific time span.Their 95% confidence interval (CI) can help determine if study results are clinically significant. If the lower CI limit exceeds the predefined clinical significance threshold, the intervention is deemed clinically valuable; otherwise, it may not be effective. When the P-value is below 0.05, the CI aids in further assessing result reliability. A narrow CI that is distant from zero indicates more reliable results, whereas a wider CI near zero suggests greater uncertainty.\u003c/p\u003e \u003cp\u003e2.3.3 Age-Period-Cohort (APC) model\u003c/p\u003e \u003cp\u003eThe Age-Period-Cohort (APC) model is a statistical model that can analyze phenomena over time.To conduct APC research, it is necessary to establish a multivariate regression model:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{t}={\\beta\\:}_{0}+{\\beta\\:}_{1}{\\:Age\\:}_{t}+{\\beta\\:}_{2}{\\:Period\\:}_{t}+{\\beta\\:}_{3}{\\:Coℎort\\:}_{t}+{ϵ}_{t}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn a multiple regression model, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Y}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e represents the dependent variable,while\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:\\text{A}\\text{g}\\text{e}\\:}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:\\text{P}\\text{e}\\text{r}\\text{i}\\text{o}\\text{d}\\:}_{\\text{t}}\\:\\text{a}\\text{n}\\text{d}{\\:\\text{C}\\text{o}\\text{ℎ}\\text{o}\\text{r}\\text{t}\\:}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003eare independent variables that can predict it.. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the intercept, indicating the expected value of Y when all independent variables are zero. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ϵ}_{t}\\)\u003c/span\u003e\u003c/span\u003e is the error term, representing the influence of excluded factors on \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Y}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn this study, age groups and observation periods were divided into 20 - year and 5 - year intervals\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Mortality and DALYs were examined independently, based on data from 1992 to 2021. We'll assess CKD burden trends across age groups, time periods, and birth cohorts in order to uncover the dynamic link between high - sodium intake and CKD. This approach not only enhances our understanding of how age, period, and cohort effects influence the relationship between high - sodium intake and CKD but also offers a scientific basis for targeted prevention and intervention strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Global distribution and trends in chronic kidney disease attributable to high sodium intake from 1990 to 2021\u003c/p\u003e\n\u003cp\u003eAs the global obesity rate increased, 69954.35 deaths (95%UI:7889.2-198557.7) in 2021, up 1 3.7 times from 1990 (2,6072.12,95%UI: 4,2 4 6.9366,433.84). In 2021, global DALYs cases due to chronic kidney disease associated with high sodium intake reached 1,705,324.92 (95%UI 220:220,083.84,681,197.81), an increase of 135% over 1990. The global ASMR for chronic kidney disease due to high sodium intake was 0.84 per 100 000 in 2021 (95% UI: 0.092.39) and EAPC 14.74% from 1990 to 2021. Similarly, the ASDR increased by 6.8% during this period (EAPC: -29.0319.18) (Tables\u0026nbsp;1 and 2).\u003c/p\u003e\n\u003cp\u003eGlobally, the number of deaths and disability adjustment years associated with high Bmi-associated chronic kidney disease in the regional, gender and SDI quintiles surged across all GBD regions between 1990 and 2021.2021, Latin America and Caribbean ASMR(1.49,95%UI:0.07\u0026ndash;4.42) and ASDR(33.21,95%UI:1.66\u0026ndash;98.54) are both the highest. The ASMR for men increased from 0.98 (95% UI: 0.16,2.50) per 100,00 0 to 1.13 (95% UI: 0.14,3.05) in 2021, with an estimated annual percentage change (EAPC) of 0.15% (95% CI: -0.23,0.32). In contrast, the ASMR for women increased from 0.56 (95% UI: 0.07,1.53) in 1990 to 0.6 1 (95% UI: 0.05,1.87) in 2021, with a slight decrease in growth rate (EAPC: 0.10%, 95% CI: -0.40,0.29). It is noteworthy that the deaths and ASMR numbers remained consistently higher in men than in women, although the increasing trend in male health burden was more pronounced during this 30-year period. Similarly, ASDR followed ASMR and showed similar trends.Strikingly, ASMR and ASDR increased by 20.73% and 6.77% in high-income countries, while they decreased in low SDI regions (ASMR-1.7%, ASDR-6.27%), highlighting different trends across socioeconomic strata (Tables\u0026nbsp;1 and 2).\u003c/p\u003e\n\u003cp\u003eTable 1. Deaths cases and ASMR per 100,00 population of chronic kidney disease attributable to high sodium intake in 1990 and 2021.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDeaths.Cases.No.(95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASMR per 100,000 No.(95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDeaths.Cases.No.(95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASMR per 100,000 No.(95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEAPC(%) in ASMR No.(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26072.12 (4246.93,66433.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73 (0.11,1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69954.35 (7889.2,198557.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84 (0.09,2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.74 (-26.06,28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Europe, Eastern Europe,and Central Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2032.16 (457.42,4648.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45 (0.1,1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3459.64 (609.06,8371.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53 (0.09,1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.69 (-11.59,32.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5086.65 (492.96,15301.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42 (0.04,1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13795.9 (530.98,45520.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52 (0.02,1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.17 (-50.06,40.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLatin America and Caribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2166.3 (120.11,6280.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15 (0.06,3.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8975.91 (432.14,26627.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.49 (0.07,4.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.68 (0.19,47.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e383.49 (0.1,2057.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27 (0,1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1304.98 (0.28,7337.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33 (0,1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.02 (-56.11,126.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2139.39 (67.32,7034.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43 (0.01,1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7738.56 (300.38,25010.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57 (0.02,1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.28 (5.85,136.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoutheast Asia, East Asia, and Oceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12324.44 (2826.15,26697.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.31 (0.27,2.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30331.54 (5754.85,71416.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14 (0.2,2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.36 (-40.46,3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1939.7 (99.99,6362.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.13 (0.06,3.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4347.83 (116.86,15343.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.22 (0.03,4.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.13 (-50.15,26.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14888.73(2718.36, 36728.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98(0.16, 2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41258.41(5491.44, 110313.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.13(0.14, 3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15(-0.23, 0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11183.39(1469.36, 30401.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56(0.07, 1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28695.95(2127.65, 87893.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61(0.05, 1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10(-0.40, 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocio-demographic Index (SDI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1795.34 (94.44,5692.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (0.05,3.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3831.27 (113.47,13219.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98 (0.03,3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.7 (-51.36,13.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3760.47 (394.39,10649.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71 (0.07,2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11649.55 (785.27,35184.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91 (0.06,2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.35 (-25.96,47.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10017.04 (1894.02,23472.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.16 (0.2,2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28460.22 (3690.07,74413.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15 (0.14,3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.07 (-37.62,14.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5330.06 (1064.78,13039.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59 (0.11,1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12246.3 (2072.28,30636.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63 (0.1,1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6 (-19.85,22.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5128.37 (688.25,14172.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46 (0.06,1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13688.92 (777.44,43392.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55 (0.03,1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.73 (-44.25,39.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 2. DALYs cases and ASDR per 100,00 population of chronic kidney disease attributable to high sodium intake in 1990 and 2021.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDALYs.Cases.No.(95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASDR per 100,000 No.(95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDALYs.Cases.No.(95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASDR per 100,000 No.(95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEAPC(%) in ASDR No.(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e725260.87 (124311.77,1838069.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.55 (3.12,47.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1705324.92 (220083.8,4681197.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.81 (2.51,54.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8 (-29.03,19.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Europe, Eastern Europe,and Central Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65799.96 (13432.03,159239.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.25 (2.91,34.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88958.78 (14460.29,222349.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.95 (2.28,35.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.12 (-27.23,9.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120428.85 (14165.91,352239.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.03 (1.18,29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e253445.48 (10326.44,806963.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.05 (0.47,34.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.16 (-58.85,24.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLatin America and Caribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54968.69 (3168.11,158913.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.72 (1.5,74.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205635.13 (10343.06,611879.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.21 (1.66,98.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.12 (0.45,46.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9828.95 (4.66,50759.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.91 (0,31.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32148.55 (15.14,176203.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.13 (0,39.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.72 (-46.63,121.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73703.12 (2775.13,236530.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.56 (0.4,41.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247605.03 (12358.69,781215.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.45 (0.74,52.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.99 (7.41,119.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoutheast Asia, East Asia, and Oceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e353455.3 (88499.01,753579.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (7.43,68.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e776640.56 (156206.38,1775603.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.1 (5.43,62.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.31 (-40.08,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47076.01 (2332.78,150716.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.63 (1.16,77.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100891.39 (2526.95,363466.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.57 (0.63,83.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.25 (-55.86,17.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e423298.08(82118.91, 1038086.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.85(4.46, 59.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1036223.17(155928.51, 2709714.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.08(3.79, 69.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09(-0.24, 0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e301962.79(44927.55, 805882.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.34(2.08, 38.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e669101.75(60366.41, 2007776.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.46(1.31, 43.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01(-0.43, 0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocio-demographic Index (SDI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46201.71 (2264.17,147578.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.82 (1.07,69.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95543.58 (2646.35,323953.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.45 (0.58,69.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.27 (-52.63,8.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112167.91 (12388.38,320782.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5 (1.91,53.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321546.95 (23277.87,965256.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.46 (1.55,67.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.43 (-26.95,39.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e284540.08 (57141.7,667749.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.14 (5.33,66.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e719210.46 (105004.97,1847364.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.85 (3.78,68.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.6 (-39.35,11.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157146.73 (34246.21,370847.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.07 (3.37,38.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e306152.43 (58076.91,741209.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.48 (2.94,37.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.67 (-23.3,10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124125.49 (18641.66,340284.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.21 (1.69,30.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261115.11 (17114.56,799429.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.97 (0.84,36.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.77 (-50.87,23.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Global burden analysis\u003c/h2\u003e\n \u003cp\u003eThe global distribution of ASMR and ASDR for CKD due to high sodium intake in 2021 shows significant geographical and national differences. Both ASMR and ASDR burden are mainly concentrated in South Latin America, Central America, Central Europe, Africa, East Asia and Southeast Asia, with the highest regions (\u0026gt;\u0026thinsp;1.2115 per 100,000 people) including Mexico, Venezuela, Bolivia, Hungary, Central Africa, Central Africa, Tanzania, Lao Guo, Malaysia, Philippines and other countries (Fig.\u0026nbsp;1A and B). The EAPC trends from 1990 to 2021 highlight the dynamic changes. ASMR and ASDR in North America, Northern Europe, Northern Europe, Eastern Europe, North Africa and Central Asia, including Norway, Sweden, Germany, Netherlands, Belarus, Ukraine, Libya, Egypt, Ghana, Pakistan, Nepal and other countries; China, Mongolia, Japan, Germany, Ethiopia and Ethiopia have significantly lower burden (Fig.\u0026nbsp;1A and B). It is worth noting that Italy and Russia have been showing a low burden.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Analysis of regional differences and health inequalities\u003c/h2\u003e\n \u003cp\u003eThe relationship between SDI and ASMR and ASDR of chronic kidney disease is non-linear both globally and throughout the 21 GBD regions. When SDI was below 0.75, the burden increased significantly with increasing SDI. However, further increases were associated with a decrease in SCI burden. High sdi regions, such as high income South Latin America, Central Europe, high income North America, and Australasia, are clustered at the upper end of the ASMR and ASDR ranges. In contrast, regions with low SDI, including eastern Sub-Saharan Africa, western Sub-Saharan Africa, central sub-Saharan Africa, Oceania, South Asia and West Asia, exhibited lower disease burden. It is worth noting that despite the high-income SDI region, ASMR and ASDR values are much lower than other high SDI regions (Fig. 2A and B).\u003c/p\u003e\n \u003cp\u003eGlobally, the burden of kidney cancer due to high salt intake was concentrated in low SDI regions in 1990 and 2021, and inequality worsened among people with different SDI rankings. The health inequality index remained negative from 1990 to 2021 and decreased with rising SDI, indicating a greater burden of kidney cancer in areas with low SDI. The health inequality index for mortality and DALYs decreased from 1990 to 2021 (Deaths: -0.68 to-1.49; DALYs: -17.180 to-33.76), indicating worsening inequality status (Fig. 3A and B). The deaths and DALYs for 1990 and 2021 were consistently large and slightly improved (Deaths: 0.742 to 0.705; DALYs: 0.751 to 0.707) (Fig. 3C and D). China and India have a large population base, and even a reduced change in proportion can have a significant impact on health equality issues. From 1990 to 2021, the proportion of mortality and disability adjusted life years decreased in China, while mortality and disability adjusted life years decreased in India.\u003c/p\u003e\n \u003cp\u003e3.4 Age and sex differences and temporal trends in chronic kidney disease attributable to high sodium intake\u003c/p\u003e\n \u003cp\u003eThe global burden of chronic kidney disease caused by high sodium intake exhibits different demographic and temporal patterns. Male disease burden than women, but continued to grow throughout 1990\u0026ndash;2021. Death and DALY were consistently higher in men than in women in almost all age groups, and this difference was more pronounced in the 55\u0026ndash;79 year age group (Fig. 4A, B). In 2021, peak deaths associated with high BMI occurred in individuals aged 65\u0026ndash;79 years, reflecting the high vulnerability of this age group (Fig. 4A). However, the loss of disability caused by chronic kidney disease was more concentrated in the age group of 65\u0026ndash;69 years, and the lower the age group (Fig. 4B). From 1990 to 2021, the number of ckd-related deaths and the disability-adjusted life-adjusted years continued to increase, with steady growth rates, both reaching an unprecedented peak in 2021 (Fig. 4C and D).\u003c/p\u003e\n \u003cp\u003eThe burden of disease increased with age in both men and women, but women were more affected by changes in age. The mortality and disability rates in men and women gradually increased from 50 to 54 years, increased significantly from 80 to 84 years and peaked in the 95\u0026thinsp;+\u0026thinsp;years age group. Note a clear gender imbalance in the disease burden of men and women after the age of 80 years (Fig.\u0026nbsp;5A and B). The number of deaths and disability loss years increased with age and showed a downward trend in later ages. Male deaths were mainly concentrated in the 65\u0026ndash;89 age group and reached a peak of 3,681 cases in the 75\u0026ndash;79 year age group. Female deaths were mainly concentrated in the 65\u0026ndash;79 age group and reached a peak of 6,247 cases in the 70\u0026ndash;74 year age group (Fig.\u0026nbsp;5C). Year of disability loss was more influenced by age than deaths and peaked in the 65\u0026ndash;69 age group for both males and females (male: 109514; female: 174994) (Fig.\u0026nbsp;5D).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Combined point regression analysis\u003c/h2\u003e\n \u003cp\u003eCombined point regression analysis showed that ASMR and ASDR due to high sodium intake in global CKD in 1990\u0026ndash;2021. Overall, ASMR and ASDR showed an increasing trend, but showed a nonsignificant downward trend in 2004\u0026ndash;2007. The trend of disease burden was roughly similar in men and women, but the burden was significantly greater in men, and the difference between men and women was more pronounced in ASDR. Specifically, the ASMR of CKD increased overall from 1990 to 2021 (AAPC\u0026thinsp;=\u0026thinsp;0.48%; 95%CI: 0.37\u0026ndash;0.58%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but the annual growth rate became negative between 2003 and 2007 (APC = -0.18%; 95%CI: -0.81\u0026ndash;0.45%; P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;6A and Supplementary Table\u0026nbsp;1). ASDR also showed a global upward trend (AAPC\u0026thinsp;=\u0026thinsp;0.21%; 95%CI: 0.13\u0026ndash;0.29%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with the fastest growth from 1997 to 2004 (APC\u0026thinsp;=\u0026thinsp;0.71%; 95%CI: 0.62\u0026ndash;0.81%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which turned negative during 2004\u0026ndash;2007 (APC = -0.50%; 95%CI: -1.08\u0026ndash;0.07%; P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;6B and Supplementary Table\u0026nbsp;2). Similarly, YLLs of CKD continued to rise from 1990 to 2021 (AAPC\u0026thinsp;=\u0026thinsp;0.29%; 95%CI: 0.19\u0026ndash;0.40%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but also negative during 2016\u0026ndash;2021 (APC = -0.77%; 95%CI: -1.71\u0026ndash;0.18%; P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;6C and Supplementary Table\u0026nbsp;3). In contrast, YLDs were more complex and volatile, decreasing against YLLs in 1990\u0026ndash;1996 and 2015\u0026ndash;2019, and increasing the most in 2019\u0026ndash;2021 (APC\u0026thinsp;=\u0026thinsp;1.82%; 95%CI: 1.38\u0026ndash;2.27%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;6D and Supplementary Table\u0026nbsp;4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Results of the Age-Period-Cohort analysis\u003c/h2\u003e\n \u003cp\u003eFor Deaths, local drifts showed an annual percentage increase with age, with the most pronounced drift observed in the 80\u0026apos;s population (Fig.\u0026nbsp;8A). The The longitudinal age curve and cross-sectional age patterns showed that mortality was positively associated with age and rose sharply after the age of 80 years (Fig.\u0026nbsp;8B and 7C). The period effect showed a progressive increase in rates overall, indicating that the effect of high sodium intake on CKD mortality was amplified over time. However, the 2010 ratio was significantly lower than that in 2005 (Fig.\u0026nbsp;8D). Cohort analysis found that the risk gradually increased in the birth cohort since 1900, and gradually decreased after reaching its peak in 1950 (Fig.\u0026nbsp;8E). DALYs showed a similar trend to Deaths in the results of Age-Period-Cohort Analysis (Fig.\u0026nbsp;8F, G, H, I and J). Notably, the disability loss rate on the longitudinal age curve and cross-sectional age patterns increases dates mortality from age 50.\u003c/p\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e3.6.1 Future projections\u003c/h2\u003e\n \u003cp\u003eFrom 2021 to 2040, global ASMR trends are stabilizing, but the differences between men and women are significant. Female ASMR is on the rise, while male ASMR is declining. In terms of ASDR, both globally, among women, and among men, ASDR is increasing. From 1990 to 2021, male ASMR and DALY rates have consistently been higher than those of females. This gender disparity is expected to persist for a long time, with men continuing to bear a greater burden of CKD compared to women (Fig.\u0026nbsp;8A and B).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe global burden of CKD skyrocketed due to excessive sodium consumption between 1990 and 2021. Deaths related to CKD surged 13.7-fold, while DALYs shot up by 135%. Despite these substantial jumps, the increases in adjusted deaths and DALYs were relatively moderate. This pattern aligns with what we observed in the 1990–2019 study\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, and it's only gotten more intense. The reasons for this include shifts in eating habits, population growth, and an aging demographic. The CKD burden from high sodium levels varies widely across countries and regions, and it's intricately linked to SDI levels. The most severe cases are found in Latin America, Sub-Saharan Africa, and Southeast Asia, where SDI is low. On the other hand, North America and Northern Europe have seen the fastest growth in CKD rates, correlating with higher SDI levels. Over the past 30 years, the burden of disease has been higher in men than in women before the age of 90, and the increasing trend is more obvious. The above findings suggest the need for targeted public health policies and interventions to mitigate the rising burden of CKD associated with high sodium intake factors.\u003c/p\u003e \u003cp\u003eIn 2021, the burden of CKD associated with high sodium intake was globally poor and the variable trends vary across countries. Instead, the low burden areas rose, and the high burden areas showed a low annual growth rate, especially in China, Mongolia, Japan and other countries, the burden was significantly reduced. Since 2007, China has really taken the bull by the horns when it comes to lowering sodium consumption. They've done things like fast-tracking changes to nutrition labels on packaged foods to make them more in line with current nutritional science, and they've been working hard to get the word out about healthy eating habits. Just to keep your body running smoothly, you really only need about 200 to 500 mg of sodium each day – anything more than that is often overkill\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Therefore, the decrease in the average daily sodium intake of Chinese residents from 5.9 grams in 2002 to 5.2 grams in 2012\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e may have a positive impact on reducing the disease burden. The burden in Italy has been low, reflecting adherence to the Mediterranean diet pattern and focus on salt intake\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The low burden in Russia is mainly due to the early diagnosis methods. Researchers at Qiuming National Medical University (TyumSMU), a member of the Russian Ministry of Health, proposed a method using positron emission and computed tomography (PET / CT) to diagnose chronic kidney disease before kidney function problems\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. This approach evaluates the molecular cell viability of tissues by observing the fixation of labeled glucose molecules in the kidney cells, thereby identifying early chronic kidney disease beyond any existing diagnostic method. Studies\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e indicates that people typically hang onto their cultural or ethnic heritage through their dietary habits, and this, in turn, shapes their food choices. It's also crucial to realize that where people get their salt from differs drastically from country to country, meaning that different approaches are needed. For instance, in nations where processed foods are the main culprit for high salt intake, the focus should be on creating and enforcing regulations for the food industry. On the other hand, in countries where home cooking is the primary source of salt, we need to put more effort into encouraging individuals to change their habits, as consumer decisions still hold significant sway. By the same token, while policy interventions are always important, we might need to put more stock in strategies that motivate and enable individuals to cut back on salt when they're cooking. These points underscore just how complex and varied attitudes toward sodium reduction can be, and highlight the need for sodium reduction strategies that are customized to a wide range of cultural and socio-demographic factors\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSDI presents a nonlinear complex relationship with the disease burden of chronic kidney disease, mainly in low SDI areas, and inequality has worsened among populations with different SDI rankings. Compared with low SDI areas, residents of high SDI areas have easy access to advanced education and better health care, as well as coordinated prevention efforts, so the burden is reduced. The Asia-Pacific region is clearly different from other high SDI regions with a low disease burden, indicating the effectiveness of targeted sodium reduction policies. The autonomy of sodium reduction can be improved by improving the overall health awareness of the population or by paying wider attention to nutrition\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. While the ASMR and ASDR in the medium SDI regions have exceeded the moderately low SDI and low SDI regions. This region lies between low and high levels of SDI, facing unique challenges related to healthcare infrastructure and disease patterns, such as the transition from infectious diseases to chronic diseases\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. It may also be related to insufficient case reporting and delayed sodium reduction policy in low and medium SDI and low SDI areas. China and India have a large population base, and even a reduced change in proportion can have a significant impact on health equality issues. From 1990 to 2021, mortality and disability-adjusted life year ratios in China declined, while mortality and disability-adjusted life year values in India decreased. It is noteworthy that despite reduced age-standardized rates, China and India remain key factors in the absolute CKD burden due to their large population and continuous high sodium intake. This underscores the dual challenges of addressing population growth and cultural dietary practices.\u003c/p\u003e \u003cp\u003eMen tend to experience a higher disease burden compared to women, partly due to their greater urinary sodium excretion (247 mmol/day versus women’s 218 mmol/day)\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. This physiological difference suggests that men may face increased exposure to high sodium levels, potentially exacerbating health risks. Additionally, men are more prone to engaging in harmful habits like smoking and excessive drinking\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, both of which are well-established contributors to chronic non-communicable diseases. These behavioral patterns further compound the health disparities between genders. Previously, a study showed that smokers preferred salty foods, leading to the occurrence of CKD with high salt intake. A study with 49,558 participants (50.3% women, 49.7% men) \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e showed that men were more likely to die from CKD than women, which is in line with our study results. In addition, the negative impact of exposure to high salt intake was significantly lower in women \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. It is worth noting that, although access to living donor kidneys seems equal between men and women, women reduce the chance of being transplanted by the donor of the deceased \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, so they cannot relax concerns about women's health issues. This study also showed that the burden of chronic kidney disease associated with high salt intake was also positively associated with age, reflecting the decline in renal function and vascular compliance\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, highlighting the need for targeted intervention and age, especially for the aging population. Early on in life, managing sodium intake can shape one's taste bud acuity and influence their desire and adaptability to salt as they grow older. Thus, it's vital to prioritize the avoidance of excessive salt consumption among the youth.\u003c/p\u003e \u003cp\u003eBetween 1990 and 2021, there's been a noticeable uptick in ASMR and ASDR cases tied to high sodium consumption, which mirrors the growth and greying of the global population. It's a given that the incidence of CKD and its risk factors will mirror this aging demographic. We're talking about a scenario where the prevalence of CKD in categories G3-G5 could soar past 10% by 2050, leading to massive health and economic repercussions, especially for low-income nations\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Head over to the WHO's online portal—it's the source where they released their groundbreaking \"Global Sodium Reduction\" report. It's no secret that we're falling short of our goal to slash sodium intake by 30% by 2025. The WHO is urging member states to act fast and put policies in place to tackle this issue head-on, mitigating the damaging impacts of too much salt. However, it's only nine countries—Brazil, Chile, the Czech Republic, Lithuania, Malaysia, Mexico, Saudi Arabia, Spain, and Uruguay—that have put forth a full range of sodium reduction strategies, making it clear how critical it is to tackle these risk factors. Note that the disease burden showed a clear decreasing trend between 2004–2007. Although kidney disease is commonly classified as a non-communicable disease, infection is also an important etiology, directly through kidney involvement (e.g.,leptospirosis or HIV infection) or indirectly through infection-associated glomerulonephritis, hemodynamic mechanisms, or systemic inflammatory responses\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e–\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Leptospires seropositivity was a risk factor in Central America in areas with high burden of chronic kidney disease in this study\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, potentially contributing to unidentified CKD variants or heightened vulnerability to triggers like heat exposure\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. So it is reasonable to speculate that the decline in ASMR and ASDR of chronic kidney disease associated with high salt intake is indirectly affected by the first global public health emergency of the 21st century —— the severe acute respiratory syndrome (SARS) virus epidemic in 2003, increasing global awareness of protection and reducing the risk of infection. We also note that the temporal trend of YLLs, although consistent with the DALYs trend, the changes in YLDs are more complex. Post−2019 YLD s. soared, highlighting the long-term disability burden and the need for early life intervention to mitigate lifetime kidney damage.\u003c/p\u003e \u003cp\u003eBy exposing the differential effects of aging, temporal patterns, and risk in specific birth cohorts, the age-cycle cohort analysis provides important insights into the temporal dynamics of renal cancer burden associated with high sodium intake. Mortality and disability loss from kidney cancer associated with high sodium intake increased significantly after age 50 and increased sharply after age 80, emphasizing aging as a key driver of the burden of disease burden. The period effect showed a progressive increase in rates overall, indicating that the effect of high sodium intake on CKD mortality was amplified over time. Excessive sodium intake will increase the metabolic burden of the kidneys, leading to sodium and water retention, oedema, and have a negative effect on kidney function\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e and the rehabilitation of kidney patients.However, the ratio showed a clear downward trend between 2005 and 2007, consistent with the results of the combined point regression analysis in this study and speculated to be due to the indirect effects of the SARS virus epidemic and the global economic crisis. Rising food prices since 2005\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e, and the 2008 financial crisis caused reduced revenues and higher food costs, have further weakened the ability to prioritize food quality and reduced salt intake\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. The cohort trends showed a decreased risk in the cohort after 1950, reflecting an increased health awareness of CKD due to high sodium intake. Study\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e has shown that, the number of salt reduction programs around the world has increased, and more and more countries are choosing structural or regulatory approaches. However, urgent accelerated and repeated efforts in other countries, especially in low SDI areas, must require stricter monitoring and evaluation of strategies to achieve salt reduction targets.\u003c/p\u003e \u003cp\u003eLooking ahead to 2040, forecasts regarding CKD linked to high sodium consumption reveal some striking differences between men and women, alongside shifting disease patterns. While the overall death rate appears to be plateauing, the diverging trends—an uptick in mortality for women coupled with a dip for men—suggest underlying variations in biology, lifestyle, and dietary habits. Simultaneously, the rising disability rate in both sexes paints a picture of an increasing burden, despite strides made in managing high blood pressure and CKD. This persistent rise in female mortality might well be chalked up to how differently women respond to salt. It's known that women, particularly after menopause, tend to see their blood pressure spike more readily with high sodium intake, likely due to the drop in estrogen, which normally helps the kidneys flush out sodium\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Men continue to exhibit higher ASMR for chronic kidney disease compared to women, though this gap is gradually narrowing. This downward trend coincides with growing health consciousness among male populations\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e, prompting more men to proactively monitor their wellbeing for early disease detection and timely intervention. Since 1990, men have made significant strides in preventive healthcare—advancements that have ultimately created distinct mortality trajectories between the sexes. Meanwhile, rising ASDR reveal a troubling paradox: while mortality figures stabilize, excessive sodium consumption continues to inflict cumulative renal damage. Loading up on salt really throws a wrench into kidney function by worsening protein in the urine and overworking the kidney's filters\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. This pushes kidney disease forward, even if blood pressure is under control. It just goes to show how bad dietary habits can lead to long-term health problems, even if they don't seem immediately life-threatening. Popping RAAS inhibitors might buy some time before end-stage renal disease kicks in, but it could also mean a longer period of being disabled\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Plus, salt-related vascular problems, like an enlarged heart\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e, add insult to injury when it comes to chronic kidney disease. If we're serious about tackling the growing problem of chronic kidney disease, we've got to get serious about public health initiatives aimed at cutting down on sodium consumption. In China's countryside, a primary focus should be placed on trimming the salt shaker in home-cooked meals. Across the pond in Japan, the UK, and the States, cutting down on the sodium in packaged goods is a no-brainer\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. And let's not forget the gender gap—specific health services need to be beefed up, from boosting cancer screenings for the fairer sex to spreading the health gospel among women and upping men's health game. Men and women face different health hazards, so it's crucial to have shrewd policies and better medical service frameworks to lessen the diseases' toll on our health and lift the health bar for everyone on the planet. Moreover, the aftermath of diseases, like disabilities, needs a close look. We should fine-tune the full spectrum of disease prevention, treatment, and rehab care. Internationally, we've got to beef up community rehab centers, train rehab professionals to a higher standard, guarantee that patients get quick and efficient rehab after their recovery, and slash the number of years cut short by disabilities.\u003c/p\u003e \u003cp\u003eWhile this study provides a comprehensive analysis of the global burden of CKD caused by high sodium intake and makes future projections for the first time, some limitations should be acknowledged. The study relies on data from GBD 2021 database. Variations in data quality and reporting standards across countries may affect the accuracy of our estimates, particularly in low- and middle-income countries where data collection systems may be less robust. The study assumes a direct causal relationship between high sodium intake and CKD. However, sodium intake is often estimated based on dietary surveys, which may not fully capture individual consumption patterns. Additionally, other dietary and lifestyle factors (e.g., potassium intake, physical activity) that could influence CKD risk were not accounted for in this analysis. While the study identifies trends over a 30-year period, it does not account for potential confounding factors such as changes in healthcare access, advancements in medical treatments, or shifts in dietary patterns over time. These factors could influence the observed trends in CKD burden. The findings are based on global and regional aggregates, which may not fully reflect the heterogeneity within individual countries or subpopulations. Localized studies are needed to tailor interventions to specific contexts.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThe study highlights a significant global rise in CKD burden due to high sodium intake, with marked disparities across regions and demographics. Targeted interventions, such as sodium reduction policies and public health campaigns, are essential to mitigate this growing health challenge, particularly in high-risk populations. Addressing dietary habits and improving healthcare access can help reduce the future impact of CKD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article and Supplementary material, further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to extend our thanks to the GBD team for allowing us to access their free, comprehensive data base and appreciate the works by the Global Burden of Disease Study 2021 collaborators.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYawen Lu and Yongfeng Wang worked on conceptualization and design of this study. Yawen Lu, Jianfeng Ma, Yang Hu and Rumeng Zheng conducted all data collection and analysis and compiled charts. Yongfeng Wang, Yawen Lu, Jianfeng Ma, Yang Hu, Rumeng Zheng, Liping Liu, Kaili Lin and Kun Zhangwrote the first draft of the manuscript. Yongfeng Wang and Sheng Li managed publications process, had full access to all the data in the study, and had final responsibility for the decision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Natural Science Foundation of Gansu Province (24JRRA1201), the Special Project of People's Livelihood Science and Technology for Social Development (2021-004-SF0-0079), the Health and Health Industry Research Project of Lanzhou City (A2024010), and Lanzhou University College Students Innovation and Entrepreneurship Training Program (20250060108).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no conflict of interest in the publication of this paper.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKovesdy CP. Epidemiology of chronic kidney disease: an update 2022. 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Journal of human hypertension. 2021 Jun;35(6):499-509. \u003c/li\u003e\n\u003cli\u003eMarketou ME, Maragkoudakis S, Anastasiou I, Nakou H, Plataki M, Vardas PE, Parthenakis FI. Salt‐induced effects on microvascular function: a critical factor in hypertension mediated organ damage. The Journal of Clinical Hypertension. 2019 Jun;21(6):749-57. \u003c/li\u003e\n\u003cli\u003eAnderson CA, Appel LJ, Okuda N, Brown IJ, Chan Q, Zhao L, Ueshima H, Kesteloot H, Miura K, Curb JD, Yoshita K. Dietary sources of sodium in China, Japan, the United Kingdom, and the United States, women and men aged 40 to 59 years: the INTERMAP study. Journal of the American Dietetic Association. 2010 May 1;110(5):736-45.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"chronic kidney disease, high sodium intake, global burden, joinpoint regression analysis, age-period-cohort model, future projections","lastPublishedDoi":"10.21203/rs.3.rs-6865321/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6865321/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eChronic kidney disease (CKD) is a progressive condition affecting over 10% of the global population, with high sodium intake identified as a critical modifiable risk factor. This study investigated the global burden of CKD due to excessive sodium intake in 204 countries and territories from 1990 to 2021 and made the first future projections to 2040, addressing gaps in longitudinal analysis of sodium-related CKD trends and demographic differences.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eData from the Global Burden of Disease (GBD) 2021 database were analyzed to quantify CKD-related deaths and disability-adjusted life years (DALYs) linked to high sodium intake. Age-standardized mortality rates (ASMR) and DALY rates (ASDR), alongside socio-demographic index (SDI), were used to assess regional and demographic variations. Statistical analyses in R included joinpoint regression to identify temporal inflection points and age-period-cohort (APC) modeling to disentangle age, period, and birth cohort effects. Future projections show that from 2021 to 2040, the global ASMR trend is stabilizing and ASDR is on the rise. And male ASMR and ASDR have been consistently higher than female ASMR. This gender difference is expected to continue for a long time, with men continuing to bear a greater burden of chronic kidney disease than women.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eBetween 1990 and 2021, global CKD deaths attributed to high sodium intake surged 13.7-fold (2,607 to 69,954), while DALYs increased by 135% (741,197 to 1,705,325). ASMR and ASDR rose markedly in high-income regions (20.73% and 6.77%, respectively), with Latin America and the Caribbean reporting the highest burdens (ASMR: 1.49/100,000; ASDR: 33.21/100,000). Males exhibited consistently higher burdens than females, peaking in the 65\u0026ndash;79 age group. Low SDI regions showed declining trends, contrasting with widening inequalities in medium SDI areas.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe global CKD burden attributable to high sodium intake has escalated dramatically over three decades, driven by aging populations, dietary shifts, and regional disparities. Urgent, targeted interventions\u0026mdash;such as sodium reduction policies, gender-specific health strategies, and enhanced healthcare access\u0026mdash;are critical to curbing this trend, particularly in high-risk demographics and high-income regions.\u003c/p\u003e","manuscriptTitle":"The global burden of chronic kidney disease (CKD) attributable to high sodium intake: a comprehensive analysis of trends from 1990 to 2021 and burden prediction to 2040","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 17:27:26","doi":"10.21203/rs.3.rs-6865321/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"28aa7550-51b5-4571-a032-3cde487c34dd","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T09:09:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-01 17:27:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6865321","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6865321","identity":"rs-6865321","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}