- Review
- Open access
- Published:
Association between dietary inflammatory index and risk of chronic kidney disease and low glomerular filtration rate; a systematic review and meta-analysis of observational studies
Journal of Health, Population and Nutrition volume 44, Article number: 120 (2025)
Abstract
Objective
Earlier studies on the association between the dietary inflammatory index (DII) and the risk of chronic kidney disease (CKD) and low estimated glomerular filtration rate (low-eGFR) have provided uncertain findings. Therefore, this study aimed to summarize the existing literature on the association between DII and CKD and low-eGFR.
Methods
In April 2024, PubMed, Scopus, and Web of Science were searched for observational studies, along with manual inclusion of Google Scholar and Embase. The review was submitted to PROSPERO (CRD42024536756) and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines. Studies which reported risk for CKD or low-eGFR were included.
Results
The random-effects model was used for statistical analysis and pooled effect sizes were reported as odds ratios (ORs) and their corresponding 95% confidence intervals (CIs). A total of 13 studies, all with a cross-sectional design, were identified eligible for inclusion in the meta-analysis. The results revealed that higher DII scores were associated with significantly higher odds of CKD (OR: 1.36, 95% CI: 1.20–1.56, p < 0.001) and low-eGFR (OR: 1.58, 95% CI: 1.26-2.00, p = 0.001).
Conclusion
This study found a significant positive association between the DII and the odds of CKD and low-eGFR, suggesting a higher likelihood of CKD in individuals who adhere to a pro-inflammatory diet. Large-scale prospective cohort studies are required to confirm these findings, particularly by assessing different indicators of kidney function.
Introduction
Chronic kidney disease (CKD) is a progressive condition that affects more than 800 million individuals worldwide. In contrast to cardiovascular and respiratory diseases, CKD mortality has been rising and reported as the third fastest-growing cause of death globally [1, 2]. It has been projected that by 2040, CKD will be the 5th highest cause of years of life lost globally [3]. Previous studies have suggested inflammatory processes as the pathogenesis of the majority of kidney diseases either in the development or progression [4,5,6]. Systemic or intrarenal inflammation may disrupt microvascular response to regulatory factors and promotes the production of various tubular toxins, such as reactive oxygen species. This process causes tubular injury, nephron dropout, and ultimately leads to the onset of CKD [7]. Additionally, elevated levels of systemic inflammatory markers are associated with a reduction in glomerular filtration rate (GFR) and an increase in the levels of urinary protein [8]. Therefore, reducing inflammation seems to be a potential strategy to prevent or reduce the progression of CKD [9, 10].
Among various risk factors proposed for CKD, dietary habits have been extensively explored in relation to renal function [11, 12]. Studies have suggested that anti-inflammatory dietary patterns such as Mediterranean diet are associated with lower inflammatory status [13,14,15,16]. These dietary patterns are mostly rich in plant-derived foods including vegetables, fruits, whole grains, nuts and seeds [17]. On the other hand, pro-inflammatory dietary patterns such as Westerns dietary pattern or high-glycemic index diets are associated with higher systemic inflammation [18,19,20]. Therefore, identifying a dietary pattern that holds the potential to reduce the risk of systemic inflammation might contribute to a more comprehensive prevention and treatment strategy for kidney diseases.
The dietary inflammatory index (DII) as a literature-derived population-based index for assessing the potential inflammatory effects of diets [21], has been investigated in relation to the risk of CKD or low estimated GFR (low-eGFR) by several studies [22,23,24,25]. While several of these studies have reported a significant increase in the risk of CKD and low-eGFR in participants who adhered to a pro-inflammatory diet (high DII score) [24, 26, 27], the reported effect sizes varied, with differences in the strength of associations and the specific odds ratios presented.
Therefore, since no previous systematic review or meta-analysis has comprehensively evaluated the relationship between DII and risk of CKD or low-eGFR, this study aimed to summarize the existing literature and clarify the association between DII and the risk of CKD and low-eGFR, as well as its relationship with serum biomarkers of kidney function. It is important to note that while individuals with low-eGFR may be classified as CKD patients, CKD is identified by the presence of albuminuria and/or low-eGFR. This distinction indicates that the risk of CKD in a specific population is not necessarily the same as the risk of low-eGFR [28]. Consequently, our study aimed to evaluate the risk of CKD and low-eGFR separately.
Methods
Study design
We systematically reviewed the relationship between DII and risk of CKD or low-eGFR as well as its correlation with uric acid, blood urea nitrogen (BUN), and creatinine. The review was submitted to PROSPERO (CRD42024536756) and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines.
Search strategy
In April 2024, a comprehensive search was conducted using Medical Subject Heading (MeSH) terms and text words related to DII, CKD, and low-eGFR combined with relevant renal function parameters (Supplementary Table 1). This search was updated weekly until August 2024. PubMed, Scopus, and Web of Science Core Collection databases were searched without any restrictions on publication date or language. Additionally, a manual search was conducted to identify any remaining articles; however, no additional records were included through this method. Figure 1 shows the search strategy and the total number of studies evaluated and selected.
Selection criteria
Two independent reviewers (AAK and FK) screened titles and abstracts to find relevant articles. No relevant articles in languages other than English were identified. Disagreements were resolved with the assistance of FH. Based on the full text article and the inclusion and exclusion criteria, eligible studies were identified. The studies were identified as eligible if they met the following criteria: [1] original studies on adult population (aged 18 or older) [2], an observational design (cross-sectional, prospective cohort, or case-control) [3], reporting risk assessed by odds ratio (OR) or relative risk (RR) or hazard ratio (HR) with their corresponding 95% confidence intervals (CIs) for low-eGFR (defined as eGFR below 60%) or CKD (defined as presence of albuminuria and/or low-eGFR) [84,85,86] (Table 1) [4] or reporting any type of association between DII and serum biomarkers of renal function or eGFR [28, 29]. Studies were excluded if they were not original research, were in vitro or animal model, conducted on children, or did not have outcomes of interest.
Data extraction
If there were several articles related to a single study, we prioritized the latest publication. The following data were extracted from the studies: first author’s name, publication year, country, design, dietary assessment method, participant’s health status, sample sizes, outcomes, final results, and adjusted variables.
Quality assessment
The quality of studies was assessed by two authors (AAK and FK) independently, utilizing the Newcastle-Ottawa scale for cross-sectional and cohort studies (Supplementary Table 3) [30]. Additionally, the Risk of Bias in Non-Randomized Studies of Environmental Exposures (ROBINS-E) tool was used to assess the risk of bias and the quality of the included cohort studies (Supplementary Table 4) [31]. Disagreements were resolved with the assistance of FH.
Certainty assessment
The overall certainty of evidence across the studies was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) guidelines. The quality of evidence was classified into four categories, based off the corresponding evaluation criteria: high, moderate, low, and very low.
Statistical analysis
The articles which reported the association between high DII and risk of CKD or low-eGFR in comparison with low DII using OR, RR, and HR were included in the current meta-analysis. If RRs or HRs were reported, they were treated as equivalent to ORs when the prevalence of frailty in the study population was ≥ 20% [32]. To summarize the association between high DII and CKD or low-eGFR, risk estimates extracted from each study were calculated using the average of the natural logarithm ORs. We used the random-effects model and the inverse-variance method to calculate the pooled effect size. Heterogeneity was evaluated using the I2 statistic [33]. To explore potential sources of heterogeneity, we conducted subgroup analyses based on dietary assessment tool (24-hour recall/Food Frequency Questionnaire), the methods used to calculate the DII (DII/E-DII), the participants sex (both sexes/females only), and geographical regions (United States/Asia). Publication bias was examined using visual inspection of a funnel plot, Egger’s, and Begg’s tests. When bias was detected, a trim-and-fill analysis was performed to assess its impact on the overall effect. Additionally, a sensitivity analysis was conducted to determine the impact of each study on the pooled effect by removing any specific study. All analyses were conducted using Stata 11.0 software (StataCorp, College Station, TX), and significance was set at p values < 0.05.
Results
Study selection and characteristics
The search strategy (Supplementary Table 1) yielded 537 records. After removing 242 duplicates, an initial screening of 300 studies based on their titles and abstracts resulted in the exclusion of 268 records. After conducting eligibility assessments on 32 full texts, 27 studies met the criteria to be included in the systematic review. Among them, 24 were cross-sectional studies [16, 22,23,24,25,26,27, 34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49], one was case-control study [50], and two were cohort studies [51, 52], one of which included both cross-sectional and cohort data [52]. Furthermore, among the included studies, 13 were eligible for the quantitative synthesis (meta-analysis). The included studies in the systematic review were published between 2015 and 2024, with a sample size ranging from 150 to 66,978. In total, the included studies involved 290,890 participants. Detailed characteristics of the included studies are shown in Table 2. The review included studies conducted in various countries: 11 in North America [16, 24, 36,37,38,39, 46,47,48,49, 53], 9 in Asia [23, 26, 27, 35, 40, 42, 43, 45], 7 in Europe [22, 25, 41, 44, 50, 52], and one in Australia [52]. Twelve articles examined the association between DII and CKD or diabetes kidney disease (DKD) [16, 26, 27, 34, 36, 37, 39, 42, 46, 48, 49, 53], while 14 studies reported the association between DII and GFR [16, 24,25,26, 35,36,37,38,39,40,41,42, 47, 52]. Furthermore, 4 studies reported the association between DII and serum uric acid [33, 38,39,40], 8 studies reported creatinine [22, 23, 40, 42,43,44,45, 50], and 3 studies reported BUN [23, 42, 43].
Quality assessment
Based on the Newcastle-Ottawa Scale, the current systematic review included two cohort studies by Li et al. and Bondonno et al., which received scores of 6 and 7 out of nine, respectively [51, 52], as well as one case-control study that received a score of 6 [50]. However, the scores of the cross-sectional studies varied more widely (Supplementary Table 3). The quality evaluation results of the included articles ranged from 3 [45] to 7 out of 9 [26, 27, 35, 38, 39, 41, 48, 52]. Most studies did not report non-response rates and were not scored on this criterion. Nevertheless, the majority of the studies received moderate to good quality assessments. Furthermore, based on the ROBINS-E tool, the two included cohort studies had a moderate to serious risk of bias (Supplementary Table 4) [51, 52].
Certainty assessment
To assess the quality of the evidence for main outcomes in this systematic review and meta-analysis, the GRADE framework was performed (Supplementary Table 5). The results indicated an overall low certainty regarding the association between DII and the risk of CKD and low eGFR, as well as the correlation between DII and eGFR. Additionally, very low certainty was found for the relationships between DII and creatinine, uric acid, and BUN.
Findings
Table 2 summarizes the systematic review results, providing details on study design, setting, population, outcomes, and findings.
Meta-analysis findings
DII and CKD risk
A total of nine studies, comprising 169,346 participants, investigated the association between DII and the risk of CKD, with all reporting a significantly higher risk of CKD in participants with the highest DII scores [16, 26, 27, 34, 37, 46, 49, 51, 53]. The results of our meta-analysis revealed 36% higher odds of CKD in participants with the highest DII scores compared to those with the lowest (95% CI: 1.20–1.56, p < 0.001) (Fig. 2). Significant heterogeneity was observed between studies (I2 = 90.4%; P < 0.001). When studies were stratified by the dietary assessment tool, the methods used to calculate the DII, and the geographical region where studies conducted, results in the subgroups remained consistent with the overall estimate, and heterogeneity did not disappear (Table 3). There was evidence of publication bias, as suggested by an asymmetry in the funnel plot and the Egger test (P < 0.001). Additionally, the trim and fill algorithm indicated an adjusted value, showing a direct association between the DII and the odds of CKD (OR: 1.363, 95% CI: 1.208 to 1.537). The slight difference between the adjusted value (1.363) and the original estimate (1.360) suggests a minor influence of the study effect on the original results. Sensitivity analysis consistently supported a positive association between DII and CKD risk, indicating the robustness of this relationship.
DII and risk of low e-GFR
In total, 7 studies with 84,000 participants investigated the association between DII and risk of low-eGFR [16, 24, 26, 35,36,37,38]. All of these studies reported a significantly higher odds of low-eGFR in participants with the highest adherence to DII, compared to those with the lowest adherence. Our meta-analysis revealed that high DII scores were associated with a 58% increase in the odds of low eGFR (95% CI: 1.26-2.00, p = 0.001). (Fig. 3). The studies showed significant heterogeneity (I2 = 70%; P = 0.001). When studies were stratified by the dietary assessment tool, the methods used to calculate the DII, participants sex, and the study geographical region, results in the subgroups remained consistent with the overall estimate and in some cases, the heterogeneity level decreased considerably (Table 4). Specifically, heterogeneity decreased significantly in studies that used food frequency questionnaires (FFQ) as the dietary assessment tool (I2 = 20.7%; P = 0.283), were conducted in Asia (I2 = 20.7%; P = 0.283), used the energy-adjusted DII (E-DII) (I2 = 38.5%; P = 0.181), and enrolled only women (I² = 20.7%; P = 0.283). An asymmetry in the funnel plot and the Egger test (P = 0.002) suggested possible publication bias. Additionally, according to the trim and fill algorithm, the adjusted value indicated a direct association between the DII and the odds of low-eGFR (OR: 1.704, 95% CI: 0.878 to 2.530). Comparing the adjusted value (1.704) with the original estimate (1.58) suggests a small contribution of the study effect to the original results. Sensitivity analysis revealed a consistent positive link between DII and the odds of low-eGFR by excluding each individual study, indicating the robustness of our results.
Narrative review
DII and risk of CKD progression
In total, 2 studies used a cross-sectional design to investigate the relationship between DII and the odds of CKD progression [39, 42]. These studies reported the OR for being in the higher stages of CKD, which reflects disease progression rather than the overall odds of CKD. Consequently, we excluded these studies from the meta-analysis. Both studies reported an increased odds of CKD progression in participants with highest DII scores. Rouhani et al. reported an increased odds of being in the higher stages of CKD among those in the top tertiles of DII compared to those in tertile 1 (OR = 2.12, 95% CI: 1.05–4.26, P = 0.03) [42]. Xu et al. also found a positive association between DII and the odds of higher CKD stages (Q4 vs. Q1, OR = 2.29, 95% CI: 1.42–3.71, P for trend = 0.0007) [39].
DII and risk of DKD
Two studies explored the association between DII and diabetic kidney disease (DKD), yielding inconsistent results [36, 48]. Due to differences in the definitions of DKD and CKD, these studies were not included in the meta-analysis [28, 54]. Wang’s study suggested that higher quartiles of DII were linked to an increased odds of DKD (Q4 to Q1, OR = 1.64, 95% CI: 1.24–2.17, p < 0.05) [36]. However, Rui’s study did not show significant association between DII and DKD in the fully adjusted model [48].
Correlation between DII and eGFR
Ten studies examined the correlation between DII and eGFR [16, 24, 25, 35, 39,40,41,42, 47, 52]. Except for the Bondonno et al. cohort study [52], all the studies had a cross-sectional design. Seven studies indicated that higher DII was linked to a decrease in eGFR [16, 24, 35, 39, 41, 47, 52], while 3 studies did not observe any significant correlation [25, 40, 42].
DII and creatinine
Eight studies were conducted to evaluate the link between DII and creatinine [22, 23, 40, 42,43,44,45, 50]. All of the studies were cross-sectional, except for one, which was a case-control study [50]. While the majority of studies found no significant correlation between DII and creatinine [22, 40, 43, 45, 50], 2 studies reported significant associations [23, 44]. Carrasco-Marín et al. reported that individuals with a pro-inflammatory diet had significantly higher levels of creatinine (β = 0.27, 95% CI 0.26–0.29, p-value < 0.0001) [44]. Similarly, a cross-sectional study by Farhangi et al. indicated that DII was linked to increased creatinine levels, specifically in men [23].
DII and uric acid
Four studies explored the link between DII and uric acid levels [22, 25, 44, 50]. All the studies employed a cross-sectional design except for a case-control study [50]. While 3 studies found no significant association between DII and uric acid [22, 25, 50], the study by Carrasco-Marín et al. indicated that individuals with a pro-inflammatory diet exhibited elevated uric acid levels (β = 0.21, 95% CI 0.29 − 0.23, p-value < 0.0001) [44].
DII and BUN
Three studies, all with a cross-sectional design, investigated the association between DII and BUN [42, 43]. The majority of studies did not find a significant association between DII and BUN [23, 42, 43]. The only study that found a significant association between DII and BUN was the study by Farhangi et al., which reported a direct correlation between them (Q4 compared to Q1, β = 1.04, 95% CI 1.01–1.08, p < 0.05) [23].
Discussion
The results of the present meta-analysis suggest that individuals with higher DII scores had 36% and 58% higher odds of CKD and low-eGFR, respectively. These associations were independent of the dietary assessment tool, the methods used to calculate the DII, participants sex, and the geographical region. Additionally, although the systematic review found a negative correlation between DII and eGFR, the correlation between DII and serum biomarkers of kidney function were inconsistent.
The association between pro-inflammatory dietary patterns and risk of developing different health condition has been extensively explored [55,56,57]. In 2021 Marx et al. conducted an umbrella review exploring the association between DII and different health conditions [57]. The study included 15 meta-analyses with a total population of 4,360,111 participants reporting 38 chronic disease-related outcomes. Marx et al. reported a significant positive association between adherence to a pro-inflammatory dietary pattern and 27 (71%) health outcomes such as myocardial infraction, all-cause mortality, and overall risk of cancer incidence [57]. However, due to the lack of any meta-analysis on DII and renal dysfunction at that time, Marx et al. failed to provide any information on CKD in their umbrella review [57].
In agreement with our findings, a recent meta-analysis reported a significant association between elevated risk of CKD and high DII scores [58]. However, the study had some limitations [87]. Firstly, Chen et al. included 3 cohort studies which examined the relationship between DII and mortality but not CKD development. Secondly, their search strategy has not been updated since March 2023 up until August 2024, leading to missing 7 relevant studies [26, 27, 37, 46, 49, 51, 53]. Thirdly, they combined the risk of CKD progression with other studies examining the risk of CKD [42]. For example, the study by Rouhani et al. was conducted on CKD patients and reported the OR for being in the higher stages of chronic kidney disease, which should not be interpreted as the risk of CKD development [42]. Finally, Chen et al. only explored the relationship between DII and CKD, while in the current study, the correlation between DII and different measures of kidney function, like GFR, creatinine, and BUN and risk of low-eGFR, has been investigated.
In line with our findings suggesting a positive association between DII and the risk of both CKD and low-eGFR, previous studies have reported an increased risk of CKD in individuals who adhered to pro-inflammatory dietary patterns, such as Western dietary pattern and diets high in ultra-processed foods [18, 59]. Furthermore, several studies have reported a negative association between anti-inflammatory dietary patterns and risk of CKD [15, 60]. For example, a meta-analysis by Hansrivijit et al. explored the association between CKD and the adherence to the Mediterranean Diet (MD), which has anti-inflammatory properties, and demonstrated that each 1-point increment in the MD score was associated with a 10% reduction in the risk of CKD [15, 61].
While our analysis found a positive link between DII and low-eGFR risk, studies investigating the relationship between DII and creatinine, uric acid, and BUN yielded inconsistent results. This may be due to the close relationship between eGFR and kidney function, especially in the early stages of CKD. In contrast, other indicators of kidney function are more likely to be influenced by factors such as diet, sex, ethnicity, and muscle mass [62]. Additionally, some indicators of kidney function tend to show changes only at advanced stages of CKD such as creatinine which is influenced when renal function decreases by 50% [62].
Although earlier studies have suggested a link between DII, inflammation and CKD, the exact mechanisms remain unclear. Proposed mechanisms for the connection between DII, inflammation and CKD are mainly focus on high energy, fat, sugar, and protein intake. High-calorie and high-fat diets are known contributors to obesity, which in turn can initiate chronic low-grade inflammation, marked by elevated serum C-reactive protein (CRP) levels [63]. Additionally, adipose tissue secretes various lipid mediators and cytokines, including tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), which have been linked to CKD [26, 64,65,66]. These inflammatory factors are closely linked to the nuclear factor-κB (NF-κB), and TGF-β/Smad3 signaling pathways [67,68,69]. These pathways lead to increased expression of pro-inflammatory and fibrotic genes, promoting renal inflammation, accumulation of extracellular matrix, and fibrosis [67, 70, 71]. Over time, the persistent activation of these pathways contributes to glomerulosclerosis, tubular atrophy, and progressive kidney dysfunction, ultimately driving the development and progression of chronic kidney disease. Furthermore, a prolonged high-sugar diet leads to hyperglycemia, which produces advanced glycation end-products (AGEs). Diets rich in protein, particularly those high in meat cooked at high temperatures, also contain high amounts of AGEs [72]. These AGEs trigger inflammation, promote insulin resistance, and induce kidney damage at both the glomerular and tubular levels [73,74,75].
However, it is important to note that dietary patterns alone are unlikely to cause kidney damage in most individuals. It is possible that the association between diet and the onset of CKD may be primarily mediated by insulin resistance and development of the metabolic syndrome, diabetes, and hypertension [76,77,78]. In fact, these conditions are known to create an environment of chronic inflammation, oxidative stress, and impaired vascular function, which collectively contribute to kidney damage [79,80,81,82]. Insulin resistance, for example, can lead to hyperglycemia and increased AGE formation, while hypertension directly strains the renal blood vessels, promoting glomerular injury [82, 83]. Thus, the interplay between diet, metabolic disorders, and kidney health highlights the importance of managing metabolic risk factors in the prevention of CKD.
The present study has several strengths. To our knowledge, this is the first comprehensive systematic review and meta-analysis on the relationship between DII and kidney function. In comparison to previous meta-analysis on the association between DII and CKD, this study included more relevant studies [58]. We conducted a comprehensive search strategy, allowing us to assess various kidney function indicators. To consider the effect of various confounders and potential sources of heterogeneity, only the fully adjusted models were enrolled in the analysis. However, this study has some limitations which should be taken into consideration when interpreting the results. Firstly, dietary intakes were collected by self-reported questionaries which are prone to recall bias. Furthermore, there is a possibility of measurement errors and misclassification of participants in the results due to variations in dietary intake questionnaires which consequently can affect the results. Secondly, while the analysis utilized the most adjusted estimates available, it is important to note that due to the observational nature of the included studies, the possibility of residual and unknown confounders influencing the results cannot be entirely eliminated. Furthermore, a notable limitation of this meta-analysis is the presence of significant publication bias. However, even after conducting the trim and fill analysis, the results did not change considerably. Finally, since the findings are primarily based on cross-sectional studies, establishing a causal relationship between the DII and the outcomes is not possible. Moreover, as only one cohort study was included in the meta-analysis, we were unable to perform a subgroup analysis based on study design. This highlights the need for further prospective cohort studies to better assess these relationships.
In conclusion, this meta-analysis demonstrated a significant positive association between DII and the odds of CKD and low-eGFR. Furthermore, the majority of studies suggest a negative correlation between DII and eGFR. However, the findings regarding the correlation between DII and serum biomarkers of kidney function are inconclusive. Large-scale prospective cohort studies are required to confirm these findings, particularly by assessing different indicators of kidney function.
Data availability
No datasets were generated or analysed during the current study.
Abbreviations
- DII:
-
Dietary inflammatory index
- CKD:
-
Chronic kidney disease
- GFR:
-
Glomerular filtration rate
- low-eGFR:
-
Low estimated glomerular filtration rate
- PRISMA:
-
Preferred reporting items for systematic reviews and meta-analyses
- OR:
-
Odds ratio
- CL:
-
Confidence intervals
- BUN:
-
Blood urea nitrogen (BUN)
- RR:
-
Relative risk
- HR:
-
Hazard ratio
- FFQ:
-
Food frequency questionnaires
- MD:
-
Mediterranean diet
- CRP:
-
C-reactive protein
- TNF-α:
-
Tumor necrosis factor-alpha
- IL-6:
-
Interleukin-6
- NF-κB:
-
Nuclear factor-κB
References
Francis A, Harhay MN, Ong ACM, Tummalapalli SL, Ortiz A, Fogo AB, et al. Chronic kidney disease and the global public health agenda: an international consensus. Nat Rev Nephrol. 2024;20(7):473–85.
Wang H, Naghavi M, Allen C, Barber RM, Bhutta ZA, Carter A, et al. Global, regional,and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet. 2016;388(10053):1459– 544.
Foreman KJ, Marquez N, Dolgert A, Fukutaki K, Fullman N, McGaughey M, et al. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories. Lancet. 2018;392(10159):2052–90.
Andrade-Oliveira V, Foresto-Neto O, Watanabe IKM, Zatz R, Câmara NOS. Inflammation in renal diseases: new and old players. Front Pharmacol. 2019;10:1192.
Feng YL, Yang Y, Chen H. Small molecules as a source for acute kidney injury therapy. Pharmacol Ther. 2022;237:108169.
Fu Y, Xiang Y, Li H, Chen A, Dong Z. Inflammation in kidney repair: mechanism and therapeutic potential. Pharmacol Ther. 2022;237:108240.
Mihai S, Codrici E, Popescu ID, Enciu AM, Albulescu L, Necula LG, et al. Inflammation-Related mechanisms in chronic kidney disease prediction, progression, and outcome. J Immunol Res. 2018;2018:2180373.
Gupta J, Mitra N, Kanetsky PA, Devaney J, Wing MR, Reilly M, et al. Association between albuminuria, kidney function, and inflammatory biomarker profile in CKD in CRIC. Clin J Am Soc Nephrology: CJASN. 2012;7(12):1938–46.
Rapa SF, Di Iorio BR, Campiglia P, Heidland A, Marzocco S. Inflammation and oxidative stress in chronic kidney Disease-Potential therapeutic role of minerals, vitamins and Plant-Derived metabolites. Int J Mol Sci. 2019;21(1).
Kadatane SP, Satariano M, Massey M, Mongan K, Raina R. The role of inflammation in CKD. Cells [Internet]. 2023;12(12).
Kazancioğlu R. Risk factors for chronic kidney disease: an update. Kidney Int Supplements. 2013;3(4):368–71.
van Westing AC, Küpers LK, Geleijnse JM. Diet and kidney function: a literature review. Curr Hypertens Rep. 2020;22(2):14.
Sánchez-Rosales AI, Guadarrama-López AL, Gaona-Valle LS, Martínez-Carrillo BE, Valdés-Ramos R. The effect of dietary patterns on inflammatory biomarkers in adults with type 2 diabetes mellitus: A systematic review and Meta-Analysis of randomized controlled trials. Nutrients [Internet]. 2022;14(21).
Hart MJ, Torres SJ, McNaughton SA, Milte CM. Dietary patterns and associations with biomarkers of inflammation in adults: a systematic review of observational studies. Nutr J. 2021;20(1):24.
Hansrivijit P, Oli S, Khanal R, Ghahramani N, Thongprayoon C, Cheungpasitporn W. Mediterranean diet and the risk of chronic kidney disease: A systematic review and meta-analysis. Nephrol (Carlton). 2020;25(12):913–8.
Mazidi M, Shivappa N, Wirth MD, Hebert JR, Kengne AP. Greater dietary inflammatory index score is associated with higher likelihood of chronic kidney disease. Br J Nutr. 2018;120(2):204–9.
Naqvi SA, Taylor LM, Panaccione R, Ghosh S, Barkema HW, Hotte N, et al. Dietary patterns, food groups and nutrients in Crohn’s disease: associations with gut and systemic inflammation. Sci Rep. 2021;11(1):1674.
Hariharan D, Vellanki K, Kramer H. The Western diet and chronic kidney disease. Curr Hypertens Rep. 2015;17(3):16.
Dror E, Dalmas E, Meier DT, Wueest S, Thévenet J, Thienel C, et al. Postprandial macrophage-derived IL-1β stimulates insulin, and both synergistically promote glucose disposal and inflammation. Nat Immunol. 2017;18(3):283–92.
Atkinson FS, Brand-Miller JC, Foster-Powell K, Buyken AE, Goletzke J. International tables of glycemic index and glycemic load values 2021: a systematic review. Am J Clin Nutr. 2021;114(5):1625–32.
Shivappa N, Steck SE, Hurley TG, Hussey JR, Hébert JR. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr. 2014;17(8):1689–96.
Alkerwi A, Vernier C, Crichton GE, Sauvageot N, Shivappa N, Hébert JR. Cross-comparison of diet quality indices for predicting chronic disease risk: findings from the observation of cardiovascular risk factors in Luxembourg (ORISCAV-LUX) study. Br J Nutr. 2015;113(2):259–69.
Farhangi MA, Najafi M. Dietary inflammatory index: a potent association with cardiovascular risk factors among patients candidate for coronary artery bypass grafting (CABG) surgery. Nutr J. 2018;17(1):20.
Huang J, Li H, Yang X, Qian C, Wei Y, Sun M. The relationship between dietary inflammatory index (DII) and early renal injury in population with/without hypertension: analysis of the National health and nutrition examination survey 2001–2002. Ren Fail. 2024;46(1):2294155.
Vahid F, Hoge A, Hébert JR, Bohn T. Association of diet quality indices with serum and metabolic biomarkers in participants of the ORISCAV-LUX-2 study. Eur J Nutr. 2023;62(5):2063–85.
Guo M, Lei Y, Liu X, Li X, Xu Y, Zheng D. Association between dietary inflammatory index and chronic kidney disease in middle-aged and elderly populations. Front Nutr. 2024;11:1335074.
Guo C, Lin Y, Wu S, Li H, Wu M, Wang F. Association of the dietary inflammation index (DII) with the prevalence of chronic kidney disease in patients with type-2 diabetes mellitus. Ren Fail. 2023;45(2):2277828.
Whaley-Connell AT, Sowers JR, Stevens LA, McFarlane SI, Shlipak MG, Norris KC, et al. CKD in the united States: kidney early evaluation program (KEEP) and National health and nutrition examination survey (NHANES) 1999–2004. Am J Kidney Dis. 2008;51(4 Suppl 2):S13–20.
Inker LA, Astor BC, Fox CH, Isakova T, Lash JP, Peralta CA, et al. KDOQI US commentary on the 2012 KDIGO clinical practice guideline for the evaluation and management of CKD. Am J Kidney Dis. 2014;63(5):713–35.
Wells GA, Shea B, O’Connell D, Peterson J, Welch V, Losos M et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. 2000.
Higgins JPT, Morgan RL, Rooney AA, Taylor KW, Thayer KA, Silva RA, et al. A tool to assess risk of bias in non-randomized follow-up studies of exposure effects (ROBINS-E). Environ Int. 2024;186:108602.
Zhang J, Yu KF. What’s the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA. 1998;280(19):1690–1.
Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60.
Moludi J, Fateh HL, Pasdar Y, Moradinazar M, Sheikhi L, Saber A, et al. Association of dietary inflammatory index with chronic kidney disease and kidney stones in Iranian adults: A cross-sectional study within the Ravansar non-communicable diseases cohort. Front Nutr. 2022;9:955562.
Lin M, Shivappa N, Hébert JR, Huang H, Cai L, Liang J, et al. Dietary inflammatory index and cardiorenal function in women with diabetes and prediabetes. Nutr Metabolism Cardiovasc Dis. 2021;31(8):2319–27.
Wang Y-J, Du Y, Chen G-Q, Cheng Z-Q, Liu X-M, Lian Y. Dose–response relationship between dietary inflammatory index and diabetic kidney disease in US adults. Public Health Nutr. 2023;26(3):611–9.
Qu S, Fang J, Zhao S, Wang Y, Gao W, Li Z et al. Associations of dietary inflammatory index with low estimated glomerular filtration rate, albuminuria and chronic kidney disease in US adults: results from the NHANES 2011–2018. Nutrition, metabolism and cardiovascular diseases. 2024;34(4):1036–45.
Zeng S, Qi L, Sun Y, Zhuang G. Association of chronic kidney disease with dietary inflammatory index in adults aged 50 years and older: dose-response analysis of a nationally representative population-based study. J Ren Nutr. 2024;34(3):216–22.
Xu Z, Li L, Jiang L, Zhai Y, Tang Y, Liu D, et al. Association of dietary inflammatory index with CKD progression and estimated glomerular filtration rate in the American CKD population: A cross-sectional study. PLoS ONE. 2024;19(2):e0297916.
Tajik S, Eimeri S, Mansouri S, Rahimi-Foroushani A, Shab-Bidar S. Association between dietary inflammatory index and kidney function in elderly population: results from a cross-sectional study. Nutr Food Sci. 2019;49(3):491–503.
Xu H, Sjögren P, Ärnlöv J, Banerjee T, Cederholm T, Risérus U, et al. A Proinflammatory diet is associated with systemic inflammation and reduced kidney function in elderly adults. J Nutr. 2015;145(4):729–35.
Rouhani MH, Najafabadi MM, Surkan PJ, Esmaillzadeh A, Feizi A, Azadbakht L. Dietary inflammatory index and its association with renal function and progression of chronic kidney disease. Clin Nutr ESPEN. 2019;29:237–41.
Behbahani HB, Bazyar H, Aghamohammadi V, Ahangarpour A, Shivappa N, Hebert JR, et al. The dietary inflammatory index is positively associated with cardiometabolic risk parameters in atherosclerosis patients. Nutr Res. 2022;107:26–36.
Carrasco-Marín F, Zhao L, Hébert JR, Wirth MD, Petermann-Rocha F, Phillips N, et al. Association of a dietary inflammatory index with cardiometabolic, endocrine, liver, renal and bones biomarkers: cross-sectional analysis of the UK biobank study. Nutr Metabolism Cardiovasc Dis. 2024;34(7):1731–40.
Kizil M, Tengilimoglu-Metin MM, Gumus D, Sevim S, Turkoglu İ, Mandiroglu F. Dietary inflammatory index is associated with serum C-reactive protein and protein energy wasting in Hemodialysis patients: A cross-sectional study. Nutr Res Pract. 2016;10(4):404–10.
Lu X, Zhou S, Liu S, Shi Y. Association of the dietary inflammation index DII with the prevalence of chronic kidney disease in patients with hypertension. Ren Fail. 2024;46(2):2373279.
Rivera-Paredez B, Argoty-Pantoja AD, Velázquez-Cruz R, Salmerón J, Jiménez-Corona A, González-Villalpando C, et al. Dietary inflammatory index and lower glomerular filtration rate in Mexican adults. Nutr Res. 2024;127:53–62.
Rui Y, Zhang X, Xie H, Qi H, Liu R, Zeng N. Association of the dietary inflammatory index with complicated diabetic kidney disease in people with diabetes mellitus: evidence from NHANES 2009-2018. Acta Diabetol. 2024;61(11):1375–84.
Guo L, Zhao P, Zhu Z. Higher dietary inflammatory index and systemic Immune-Inflammation index score are associated with higher risk of chronic kidney disease: analysis of the National health and nutrition examination survey from 1999 to 2018. J Ren Nutr. 2024.
Rodgers AL, Arzoz-Fabregas M, Roca-Antonio J, Dolade-Botias M, Shivappa N, Hébert JR. Correlation research demonstrates that an inflammatory diet is a risk factor for calcium oxalate renal stone formation. Clin Nutr ESPEN. 2024;60:320–6.
Li Z, Xu Z, Xuan C, Xu H. Association between waist triglyceride index, body mass index, dietary inflammatory index, and triglyceride- glucose index with chronic kidney disease: the 1999–2018 cohort study from NHANES. Front Endocrinol. 2024;15.
Bondonno NP, Blekkenhorst LC, Bird AL, Lewis JR, Hodgson JM, Shivappa N, et al. Dietary inflammatory index and the aging kidney in older women: a 10-year prospective cohort study. Eur J Nutr. 2020;59(7):3201–11.
Huang Y, Xu S, Wan T, Wang X, Jiang S, Shi W et al. The combined effects of the most important dietary patterns on the incidence and prevalence of chronic renal failure: results from the US National health and nutrition examination survey and Mendelian analyses. Nutrients. 2024;16(14).
de Boer IH, Rue TC, Hall YN, Heagerty PJ, Weiss NS, Himmelfarb J. Temporal trends in the prevalence of diabetic kidney disease in the united States. JAMA. 2011;305(24):2532–9.
Namazi N, Larijani B, Azadbakht L. Dietary inflammatory index and its association with the risk of cardiovascular diseases, metabolic syndrome, and mortality: A systematic review and Meta-Analysis. Horm Metab Res. 2018;50(5):345–58.
Shivappa N, Godos J, Hébert JR, Wirth MD, Piuri G, Speciani AF et al. Dietary inflammatory index and cardiovascular risk and Mortality—A Meta-Analysis. Nutrients [Internet]. 2018;10(2).
Marx W, Veronese N, Kelly JT, Smith L, Hockey M, Collins S, et al. The dietary inflammatory index and human health: an umbrella review of Meta-Analyses of observational studies. Adv Nutr. 2021;12(5):1681–90.
Chen Q, Ou L. Meta-analysis of the association between the dietary inflammatory index and risk of chronic kidney disease. Eur J Clin Nutr. 2025;79(1):7–14.
Xiao B, Huang J, Chen L, Lin Y, Luo J, Chen H, et al. Ultra-processed food consumption and the risk of incident chronic kidney disease: a systematic review and meta-analysis of cohort studies. Ren Fail. 2024;46(1):2306224.
Song Y, Lobene AJ, Wang Y, Hill Gallant KM. The DASH diet and cardiometabolic health and chronic kidney disease: A narrative review of the evidence in East Asian countries. Nutrients [Internet]. 2021;13(3).
Estruch R. Anti-inflammatory effects of the mediterranean diet: the experience of the PREDIMED study. Proc Nutr Soc. 2010;69(3):333–40.
Gounden VBH, Jialal I. Renal Function Tests. [Updated 2023 Jul 17]: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing. 2023. [Available from: https://www.ncbi.nlm.nih.gov/books/NBK507821/
Visser M, Bouter LM, McQuillan GM, Wener MH, Harris TB. Elevated C-reactive protein levels in overweight and obese adults. JAMA. 1999;282(22):2131–5.
Kern L, Mittenbühler MJ, Vesting AJ, Ostermann AL, Wunderlich CM, Wunderlich FT. Obesity-Induced TNFα and IL-6 signaling: the missing link between obesity and Inflammation-Driven liver and colorectal cancers. Cancers (Basel). 2018;11(1).
Fox ER, Benjamin EJ, Sarpong DF, Nagarajarao H, Taylor JK, Steffes MW, et al. The relation of C - reactive protein to chronic kidney disease in African Americans: the Jackson heart study. BMC Nephrol. 2010;11(1):1.
Shankar A, Sun L, Klein BEK, Lee KE, Muntner P, Nieto Javier F, et al. Markers of inflammation predict the long-term risk of developing chronic kidney disease: a population-based cohort study. Kidney Int. 2011;80(11):1231–8.
Wu W, Wang X, Yu X, Lan HY. Smad3 signatures in renal inflammation and fibrosis. Int J Biol Sci. 2022;18(7):2795–806.
Liu T, Zhang L, Joo D, Sun S-C. NF-κB signaling in inflammation. Signal Transduct Target Therapy. 2017;2(1):17023.
Brasier AR. The nuclear factor-kappaB-interleukin-6 signalling pathway mediating vascular inflammation. Cardiovasc Res. 2010;86(2):211–8.
Akhtar M, Guo S, Guo Y-f, Zahoor A, Shaukat A, Chen Y, et al. Upregulated-gene expression of pro-inflammatory cytokines (TNF-α, IL-1β and IL-6) via TLRs following NF-κB and MAPKs in bovine mastitis. Acta Trop. 2020;207:105458.
Wight TN, Potter-Perigo S. The extracellular matrix: an active or passive player in fibrosis? Am J Physiol Gastrointest Liver Physiol. 2011;301(6):G950–5.
Goldberg T, Cai W, Peppa M, Dardaine V, Baliga BS, Uribarri J, et al. Advanced glycoxidation end products in commonly consumed foods. J Am Diet Assoc. 2004;104(8):1287–91.
Pal R, Bhadada SK. AGEs accumulation with vascular complications, glycemic control and metabolic syndrome: A narrative review. Bone. 2023;176:116884.
Meek RL, LeBoeuf RC, Saha SA, Alpers CE, Hudkins KL, Cooney SK, et al. Glomerular cell death and inflammation with high-protein diet and diabetes. Nephrol Dial Transpl. 2013;28(7):1711–20.
Dozio E, Caldiroli L, Molinari P, Castellano G, Delfrate NW, Romanelli MMC et al. Accelerated ageing: the impact of advanced glycation end products on the prognosis of chronic kidney disease. Antioxid (Basel). 2023;12(3).
Thomas G, Sehgal AR, Kashyap SR, Srinivas TR, Kirwan JP, Navaneethan SD. Metabolic syndrome and kidney disease: a systematic review and meta-analysis. Clin J Am Soc Nephrol. 2011;6(10):2364–73.
Kramer H. Diet and chronic kidney disease. Adv Nutr. 2019;10(Suppl4):S367–79.
Hao XM, Liu Y, Hailaiti D, Gong Y, Zhang XD, Yue BN, et al. Mechanisms of inflammation modulation by different immune cells in hypertensive nephropathy. Front Immunol. 2024;15:1333170.
Reddy P, Lent-Schochet D, Ramakrishnan N, McLaughlin M, Jialal I. Metabolic syndrome is an inflammatory disorder: A conspiracy between adipose tissue and phagocytes. Clin Chim Acta. 2019;496:35–44.
Scurt FG, Ganz MJ, Herzog C, Bose K, Mertens PR, Chatzikyrkou C. Association of metabolic syndrome and chronic kidney disease. Obes Rev. 2024;25(1):e13649.
Kumar M, Dev S, Khalid MU, Siddenthi SM, Noman M, John C, et al. The bidirectional link between diabetes and kidney disease: mechanisms and management. Cureus. 2023;15(9):e45615.
Ameer OZ. Hypertension in chronic kidney disease: what Lies behind the scene. Front Pharmacol. 2022;13:949260.
Khalid M, Petroianu G, Adem A. Advanced glycation end products and diabetes mellitus: mechanisms and perspectives. Biomolecules [Internet]. 2022;12(4).
Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006;145(4):247–54.
Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–12.
Chapter 1: Definition and classification of CKD. Kidney Int Suppl. 2011. 2013;3(1):19–62.
Ataei Kachouei A, Kamrani F, Haghighatdoost F. Comment on “Meta-analysis of the association between the dietary inflammatory index and risk of chronic kidney disease” by Chen et al. 2024. European Journal of Clinical Nutrition. 2025.
Acknowledgements
None.
Funding
None.
Author information
Authors and Affiliations
Contributions
A.A.K. and F.H. conceived and designed the study. F.H. conducted the statistical analyses. A.A.K. and F.K. interpreted the results and drafted the manuscript. N.S.A. and F.H. provided scientific and language editing of the manuscript. All authors reviewed and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Ataei Kachouei, A., Kamrani, F., Akhavan, N.S. et al. Association between dietary inflammatory index and risk of chronic kidney disease and low glomerular filtration rate; a systematic review and meta-analysis of observational studies. J Health Popul Nutr 44, 120 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41043-025-00872-9
Received:
Accepted:
Published:
DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41043-025-00872-9