Abstract
Background
Unhealthy diets contribute to the onset and progression of chronic kidney disease (CKD), with poor dietary habits identified as significant lifestyle factors that elevate CKD risk.
Methods
Data from the UK Biobank cohort, which included over 500,000 participants aged 40–69 from diverse regions of England, Wales, and Scotland, were analyzed. Participants, who completed at least one online 24-hour dietary recall assessment, were included in the study. The baseline for analysis was the first 24-hour dietary recall in 2011, with follow-up extending until the earliest occurrence of CKD diagnosis, death, or the end of the study period.
Results
A total of 207,268 British individuals who completed at least one online 24-hour dietary recall assessment were included in this study. Four healthy dietary pattern scores were evaluated: the Healthy Plant-based Diet Index (hPDI), the Healthy Eating Index (HEI)-2015, the Mediterranean Diet (MED) Score, and the Alternative Mediterranean Diet (AMED) Score. These scores assessed the association between dietary patterns and the incidence of CKD. Relative to the lowest dietary scores, the HR for CKD was 0.79 (95% CI, 0.73–0.87) for the hPDI, 0.80 (95% CI, 0.73–0.87) for the HEI-2015, 0.86 (95% CI, 0.81–0.93) for the MED, and 0.84 (95% CI, 0.78–0.90) for the AMED (all p < 0.001).
Conclusion
This study provides robust evidence linking healthy dietary patterns to a reduced risk of CKD. Further clinical trials are needed to confirm whether adherence to such diets can lower the risk of CKD.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-025-21652-4.
Keywords: Chronic kidney disease, Dietary pattern, Dietary score, Incidence
Introduction
Chronic kidney disease (CKD) is a progressive disorder that can culminate in kidney failure and other severe health complications, such as cardiovascular diseases, contributing significantly to morbidity and mortality rates [1, 2]. In 2017, global cases of all-stage CKD reached 697.5 million, with 1.2 million deaths attributed to the condition [3]. The rising incidence of diabetes mellitus, obesity, hypertension, and an aging population are driving the increasing global burden of CKD [4]. Research suggests that genetic differences across regions may also contribute to the geographical variation in CKD prevalence [5, 6]. Key risk factors for CKD include diabetes, hypertension, cardiovascular disease and heart failure, obesity, advanced age, family history of CKD or kidney failure, prior acute kidney injury, and smoking [7, 8].
Unhealthy or poor-quality diets contribute to the onset of several chronic conditions, including diabetes mellitus, obesity, hypertension, and CKD [9–11]. Adhering to a healthy dietary pattern plays a crucial role in preventing and slowing the progression of chronic kidney disease (CKD) [12, 13]. Modifying dietary patterns in adults not only improves overall health but also mitigates the risk of chronic diseases’ onset and progression [14]. A dietary pattern encompasses a variety of foods that interact in ways that can either complement or counterbalance one another. It reflects the intricate relationships between different foods or nutrients, thereby capturing an individual’s actual eating habits and offering a more comprehensive perspective on diet-disease associations [15]. Recent research has increasingly focused on understanding how diet quality influences the onset of CKD [16–18]. Multiple diet quality indices have been developed to assess overall dietary quality [19]. Collectively, studies consistently suggest that greater adherence to a healthy dietary pattern is inversely associated with CKD risk [12–14].
Studies have established a link between dietary interventions and the progression of chronic kidney disease (CKD) as well as the management of associated complications [16, 20, 21]. Diets focused on whole, unprocessed, plant-based foods highlight the benefits of such nutritional patterns for individuals with CKD [16]. Therefore, understanding the association between dietary patterns and CKD risk can help provide dietary guidance for patients and the general population.
The objective of this study was to assess the relative effectiveness of different dietary patterns in mitigating CKD risk using data from the UK Biobank cohort. Four dietary patterns were examined: the Healthy Plant-Based Diet Index (hPDI) [22], Healthy Eating Index (HEI)-2015 [23], Mediterranean Diet (MED) Score [24], and Alternative Mediterranean Diet (AMED) Score [25].
Methods
Study population
This prospective study draws on data from the ongoing UK Biobank cohort (Application number: 51671; approved in August 2019), which includes over 500,000 participants aged 40–69 years, recruited across various regions of England, Wales, and Scotland between 2006 and 2010. Detailed information on the study design and assessment protocols of the UK Biobank cohort has been previously published [26]. Ethical approval was granted by the National Information Governance Board for Health and Social Care in England and Wales, and the Community Health Index Advisory Group in Scotland. Written informed consent was obtained from all participants prior to data collection.
Participants with missing 24-hour recall data or implausible daily energy intake (total energy intake < 2508 or > 14630 kj/day in women, and < 3344 or > 17556 kj/day in men) [27] were excluded. Ultimately, 210,950 participants were included in the analysis. The baseline was defined as the date of the first 24-hour dietary recall assessment in 2011, continuing until the earliest of the following events: the first CKD diagnosis, death, or the end of the follow-up period. Participants who had died prior to the baseline or had a history of CKD (International Classification of Diseases, Tenth Revision [ICD-10] code N18) or CKD-related conditions (ICD-10 codes I13, N02, N03, N04, N05, N06, N07, N08, and N15) before the baseline (n = 3,653) were excluded. Additionally, participants lost to follow-up (n = 29) were also excluded. Consequently, the final analysis included 207,268 participants (92,884 men and 114,384 women).
Assessment of outcomes
The definition of CKD events in this study included the initial diagnosis of CKD (ICD-10 code N18), an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m² at any point during follow-up, or a urine albumin-creatinine ratio ≥ 30 mg/g at any time during the follow-up period.
Assessment of the dietary scores
At the recruitment assessment center, participants were invited by email to complete an online 24-hour dietary recall assessment. This assessment collected data on the consumption of 206 food items and 32 beverage types over the preceding 24 h, along with estimates of energy and nutrient intake. Participants were also required to report portion sizes for each food item, specifying the number of servings consumed. Standard portion sizes were provided for most items, including one apple, one sausage, one rasher of bacon, or one slice of ham. For foods like pasta or rice, a “serving” was defined, and participants were asked to indicate the number of servings consumed.
The associations between four distinct healthy dietary patterns and CKD risk were analyzed, including the hPDI, HEI-2015, MED Score, and AMED Score. Detailed descriptions of the components and scoring criteria for each dietary score were provided in Supplemental Tables S1-S4. The hPDI included 18 food groups, categorized into quintiles of consumption, with each quintile assigned a score from 1 to 5, either positive or reverse, yielding a total score ranging from 18 to 90. The HEI-2015 consisted of 13 components, with a total score range of 0 to 100. Both the MED Score and AMED Score encompassed nine components, each with a total score range of 0 to 9. Higher scores indicated greater adherence to the respective dietary patterns.
CKD prediction model
The prediction model developed by Nelson et al. was used to estimate the risk of CKD incidence [28]. Given that variables such as albuminuria and HbA1c frequently differ based on diabetes status, separate models were constructed for participants with and without diabetes. The primary model for predicting 5-year risk of reduced eGFR included demographic factors (age, sex, and race/ethnicity), eGFR (modeled using linear splines with a knot at 90 mL/min/1.73 m2), history of cardiovascular disease, smoking, hypertension, body mass index (BMI), and albuminuria. For diabetic participants, the model further accounted for diabetes medications, HbA1c levels, and their interaction. The prediction equation was provided in Supplemental Table S9, with stratification into quartiles for analysis.
Covariates
Covariates were obtained through participant interviews conducted during the recruitment phase. These included age (years), sex (female or male), assessment center, ethnic background (white or non-white), index of multiple deprivation (IMD), BMI category (low, normal, overweight, or obese), smoking status (never, former, or current), alcohol consumption (special occasions/never, 1–3 times/month, 1–4 times/week, or daily/almost daily), physical activity (metabolic equivalent hours/week), sleep duration (< 8 h, 8 h, 8–9 h, or > 9 h), history of hypertension (yes or no), history of diabetes (yes or no), general health status (self-reported long-standing illness), use of dietary supplements (no, yes, unknown, or prefer not to answer), and drug use (yes or no), including nonsteroidal anti-inflammatory drugs (NSAIDs), angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), and beta-blockers.
Statistical analysis
Descriptive statistics were used to assess the baseline characteristics of participants across different dietary patterns. Person-years were calculated from the date of the initial 24-hour dietary recall to the earliest of the following events: CKD diagnosis, death, or end of the follow-up period. Normally distributed continuous variables were presented as mean (SD), and categorical variables as number (percentages). Cox proportional hazards models were applied to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between healthy dietary scores and CKD risk. Multivariable logistic regression models were employed to examine the relationship between incident CKD and each quantile of the hPDI, HEI-2015, MED Score, and AMED Score, calculating HRs and corresponding 95% CIs. The first quintile (Q1) for the hPDI and HEI-2015, and the lowest score range (0–2) for the MED Score and AMED Score were designated as reference categories. The basic model adjusted for age, sex, and assessment center, while the multivariable model further accounted for ethnic background, IMD, BMI, smoking and drinking status, physical activity, sleep duration, use of dietary supplements (vitamins and minerals), drug use (NSAIDs, ACEIs, ARBs, and beta-blockers), history of hypertension and diabetes, general health status (self-reported long-standing illness) and total energy intake. CKD risk per standard deviation increase in the dietary scores was estimated by treating the scores as continuous variables. To reduce the impact of reverse causality, one-year lag analyses were performed (Fig. 1).
Fig. 1.
Flow of participants through study
Sensitivity analyses were conducted to assess the robustness of the study. To mitigate reverse causation from pre-existing conditions, 2-year and 4-year lag analyses were implemented. Participants diagnosed with diabetes mellitus or hypertension at baseline were excluded prior to analysis. Sensitivity analysis was conducted before(Model 1) and after (Model 2) including total energy intake as a correction variable. Subgroup analyses were performed, and statistical interactions evaluated based on potential risk factors, including sex, baseline age (2006), BMI, physical activity, smoking status, drinking habits, and health status. Two-sided statistical tests were used, with p < 0.05 considered statistically significant. An interaction term was incorporated into the regression model to examine the relationship between dietary scores and various covariates. The analysis adhered to the STROBE cohort reporting guidelines [29].
Results
Baseline characteristics of the study population
Participants who completed at least one 24-hour online dietary recall and had plausible energy intake values were included in the study (n = 210,950). Exclusions were made for individuals diagnosed with CKD or CKD-related conditions at baseline (n = 3,653), as well as those lost to follow-up (n = 29). The final cohort comprised 207,268 individuals of British descent (92,884 men and 114,384 women) from the UK Biobank. Baseline characteristics based on the four dietary scores were presented in Table 1. Participants with higher scores were generally older and more likely to be female. They also exhibited lower IMD and BMI, higher physical activity levels, and a reduced prevalence of diabetes mellitus. Moreover, the use of medications such as beta-blockers, NSAIDs, and ACEIs was less common among those with higher scores compared to individuals with lower scores (Table 1).
Table 1.
Baseline characteristics of participants in the UK Biobank cohort stratified by healthy dietary pattern scores
| Variable a | Overall | hPDI | HEI-2015 | MED Score | AMED Score | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Q1 | Q3 | Q5 | Q1 | Q3 | Q5 | 0–2 | 3–4 | 5–9 | 3–4 | 5–9 | ||
| (N = 207,268) | (N = 35,493) | (N = 47,364) | (N = 44,780) | (N = 41,441) | (N = 41,468) | (N = 41,496) | (N = 58,115) | (N = 81,810) | (N = 67,343) | (N = 80,854) | (N = 70,930) | |
| Age, Mean (SD), year | 56.0 (7.95) | 55.0 (8.12) | 56.1 (7.95) | 56.7 (7.70) | 54.8 (8.21) | 56.1 (7.93) | 57.1 (7.54) | 55.7 (8.10) | 55.9 (7.97) | 56.4 (7.77) | 56.0 (7.97) | 56.4 (7.78) |
| Male, N (%) | 92,884 (44.8) | 19,367 (54.6) | 21,613 (45.6) | 15,891 (35.5) | 21,306 (51.4) | 19,013 (45.8) | 14,865 (35.8) | 29,363 (50.5) | 36,472 (44.6) | 27,049 (40.2) | 36,664 (45.3) | 29,650 (41.8) |
| White, N (%) | 197,729 (95.4) | 34,023 (95.9) | 45,271 (95.6) | 42,430 (94.8) | 38,929 (93.9) | 39,693 (95.7) | 39,920 (96.2) | 55,334 (95.2) | 77,858 (95.2) | 64,537 (95.8) | 76,814 (95.0) | 68,286 (96.3) |
| IMD, Mean (SD) | 15.4 (12.1) | 15.8 (12.4) | 15.4 (12.1) | 15.1 (11.8) | 17.2 (13.3) | 15.1 (11.9) | 14.4 (11.2) | 16.1 (12.7) | 15.5 (12.2) | 14.7 (11.5) | 15.5 (12.2) | 14.5 (11.4) |
| BMI, mean (SD), kg/m2 | 26.9 (4.63) | 27.8 (4.98) | 27.0 (4.60) | 26.2 (4.38) | 27.5 (4.85) | 26.9 (4.57) | 26.4 (4.48) | 27.4 (4.66) | 27.1 (4.67) | 26.4 (4.51) | 27.0 (4.62) | 26.4 (4.49) |
| Total energy, mean (SD), kj/day | 8,640 (2540) |
9580 (2540) |
8680 (2450) |
7870 (2420) |
8680 (2990) |
8780 (2470) |
8260 (2130) |
8380 (2700) |
8620 (2600) |
8900 (2280) |
8610 (2580) |
8780 (2240) |
| Physical activity, mean (SD), MET hours/week | 42.0 (37.9) | 39.6 (37.2) | 41.8 (37.9) | 44.5 (38.7) | 42.3 (40.2) | 41.0 (36.6) | 43.4 (37.5) | 41.8 (38.8) | 41.9 (37.9) | 42.4 (37.0) | 41.8 (37.8) | 42.3 (37.1) |
| Never-smoker, N (%) | 117,662 (56.8) | 20,501 (57.8) | 26,823 (56.6) | 25,368 (56.7) | 22,304 (53.8) | 23,616 (57.0) | 24,310 (58.6) | 31,632 (54.4) | 46,375 (56.7) | 39,655 (58.9) | 45,557 (56.3) | 42,146 (59.4) |
| Never-drinker, N (%) | 33,609 (16.2) | 5,864 (16.5) | 7,323 (15.5) | 7,723 (17.2) | 7,921 (19.1) | 6,327 (15.3) | 6,483 (15.6) | 9,847 (16.9) | 13,836 (16.9) | 9,926 (14.7) | 13,536 (16.7) | 10,267 (14.5) |
| Hypertension, N (%) | 114,541 (55.3) | 20,429 (57.6) | 26,256 (55.4) | 23,659 (52.8) | 22,647 (54.6) | 22,928 (55.3) | 22,867 (55.1) | 32,717 (56.3) | 45,526 (55.6) | 36,298 (53.9) | 44,850 (55.5) | 38,356 (54.1) |
| Diabetes mellitus, N (%) | 9,780 (4.7) | 1,804 (5.1) | 2,292 (4.8) | 1,965 (4.4) | 2,143 (5.2) | 1,960 (4.7) | 1,710 (4.1) | 3,027 (5.2) | 4,037 (4.9) | 2,716 (4.0) | 4,036 (5.0) | 2,738 (3.9) |
| Long-standing illness, N (%) | 59,906 (28.9) | 11,042 (31.1) | 13,645 (28.8) | 12,293 (27.5) | 12,703 (30.7) | 11,987 (28.9) | 11,625 (28.0) | 17,210 (29.6) | 23,932 (29.3) | 18,764 (27.9) | 23,489 (29.1) | 19,725 (27.8) |
| Medication use, N (%) | ||||||||||||
| Beta-blockers | 10,520 (5.1) | 1,979 (5.6) | 2,486 (5.2) | 1,932 (4.3) | 2,209 (5.3) | 2,067 (5.0) | 2,003 (4.8) | 3,186 (5.5) | 4,312 (5.3) | 3,022 (4.5) | 4,172 (5.2) | 3,242 (4.6) |
| NSAIDs | 34,353 (16.6) | 6,158 (17.3) | 7,853 (16.6) | 7,051 (15.7) | 7,534 (18.2) | 6,802 (16.4) | 6,355 (15.3) | 10,145 (17.5) | 13,736 (16.8) | 10,472 (15.6) | 13,495 (16.7) | 11,007 (15.5) |
| ARBs | 4,738 (2.3) | 875 (2.5) | 1,058 (2.2) | 975 (2.2) | 884 (2.1) | 977 (2.4) | 939 (2.3) | 1,342 (2.3) | 1,911 (2.3) | 1,485 (2.2) | 1,908 (2.4) | 1,564 (2.2) |
| ACEIs | 17,210 (8.3) | 3,337 (9.4) | 3,978 (8.4) | 3,239 (7.2) | 3,595 (8.7) | 3,378 (8.1) | 3,264 (7.9) | 5,171 (8.9) | 6,971 (8.5) | 5,068 (7.5) | 6,904 (8.5) | 5,323 (7.5) |
Abbreviations: IMD, index of multiple deprivation; BMI, body mass index; NSAIDs, nonsteroidal anti-inflammatory drugs; ARBs, angiotonin receptor blockers; ACEIs, angiotensin-converting enzyme inhibitors
a Normally distributed continuous variables were presented as mean (SD), and categorical variables as number (percentages)
Association between healthy dietary scores and CKD incidence
To minimize the influence of reverse causation due to pre-existing conditions, one-year lag analyses were conducted. During a follow-up period totaling 41,921 person-years, 5,865 CKD cases were recorded. After rigorous adjustment for potential confounders, multivariable analysis revealed a statistically significant inverse relationship between the four dietary scores and CKD risk. In comparisons between the highest and lowest score ranges, the multivariable-adjusted (Model 1) HRs for CKD were 0.79 (95% CI, 0.73–0.87) for the hPDI, 0.80 (95% CI, 0.73–0.87) for the HEI-2015, 0.86 (95% CI, 0.81–0.93) for the MED Score, and 0.84 (95% CI, 0.78–0.90) for the AMED Score (all p < 0.001) (Table 2). Additionally, the associations were predominantly linear, with a 6–8% reduction in CKD risk per standard deviation increase in score for hPDI (HR, 0.93; 95% CI, 0.90–0.95), HEI-2015 (HR, 0.92; 95% CI, 0.89–0.94), MED Score (HR, 0.94; 95% CI, 0.92–0.97), and AMED Score (HR, 0.94; 95% CI, 0.91–0.96) (Table 2).
Table 2.
HRs (95% CI) for the association between the four dietary scores and risk of chronic kidney disease
| Variable | Quantile of dietary score | HR (95%CI) per SD | P for trend | ||||
|---|---|---|---|---|---|---|---|
| hPDI | Q1 | Q2 | Q3 | Q4 | Q5 | ||
| Median score | 42 | 47 | 51 | 54 | 60 | ||
| Cases | 1,149 | 1,058 | 1,439 | 1,164 | 1,055 | ||
| Person-years | 8,380 | 7,468 | 10,299 | 8,207 | 7,567 | ||
| Incidence rate a | 3.32 | 3.01 | 2.96 | 2.62 | 2.29 | ||
| Age, sex and center-stratified HR (95% CI) b | 1.00 (Ref) | 0.91 (0.83–0.99) | 0.89 (0.82–0.97) | 0.82 (0.75–0.89) | 0.68 (0.62–0.74) | 0.87 (0.85–0.90) | < 0.001 |
| Multivariable-adjusted HR (95% CI) c | 1.00 (Ref) | 0.97 (0.89–1.05) | 0.97 (0.90–1.05) | 0.92 (0.84-1.00) | 0.79 (0.73–0.86) | 0.92 (0.90–0.95) | < 0.001 |
| HEI-2015 | Q1 | Q2 | Q3 | Q4 | Q5 | ||
| Median score | 46.34 | 57.13 | 63.85 | 69.80 | 76.91 | ||
| Cases | 1,370 | 1,241 | 1,222 | 1,083 | 949 | ||
| Person-years | 9,764 | 8,771 | 8,634 | 7,826 | 6,925 | ||
| Incidence rate a | 3.44 | 3.10 | 3.04 | 2.70 | 2.37 | ||
| Age, sex and center-stratified HR (95% CI) b | 1.00 (Ref) | 0.90 (0.83–0.97) | 0.81 (0.75–0.88) | 0.75 (0.69–0.81) | 0.68 (0.63–0.74) | 0.87 (0.85–0.90) | < 0.001 |
| Multivariable-adjusted HR (95% CI) c | 1.00 (Ref) | 0.95 (0.88–1.03) | 0.89 (0.82–0.97) | 0.85 (0.78–0.92) | 0.80 (0.73–0.87) | 0.92 (0.90–0.94) | < 0.001 |
| MED Score | 0–2 | 3–4 | 5–9 | ||||
| Cases | 1,943 | 2,364 | 1,558 | ||||
| Person-years | 13,845 | 16,651 | 11,424 | ||||
| Incidence rate a | 3.36 | 2.90 | 2.32 | ||||
| Age, sex and center-stratified HR (95% CI) b | 1.00 (Ref) | 0.95 (0.89–1.01) | 0.77 (0.72–0.82) | 0.90 (0.88–0.92) | < 0.001 | ||
| Multivariable-adjusted HR (95% CI) c | 1.00 (Ref) | 0.98 (0.92–1.04) | 0.87 (0.81–0.93) | 0.94 (0.92–0.97) | < 0.001 | ||
| AMED Score | 0–2 | 3–4 | 5–9 | ||||
| Cases | 1,871 | 2,382 | 1,612 | ||||
| Person-years | 13,195 | 16,850 | 11,876 | ||||
| Incidence rate a | 3.39 | 2.96 | 2.28 | ||||
| Age, sex and center-stratified HR (95% CI) b | 1.00 (Ref) | 0.93 (0.87–0.99) | 0.73 (0.68–0.78) | 0.88 (0.86–0.91) | < 0.0001 | ||
| Multivariable-adjusted HR (95% CI) c | 1.00 (Ref) | 0.98 (0.92–1.05) | 0.84 (0.79–0.90) | 0.94 (0.91–0.96) | < 0.0001 | ||
Abbreviations: hPDI, Healthful Plant-Based Diet Index; HEI-2015, Healthy Eating Index-2015; MED Score, Mediterranean Diet Score; AMED Score, Alternate Mediterranean Diet Score
a Incidence rate represents the number of CKD cases per one hundred people
b Basic cox regression model adjusted for age, sex (Female and male), and assessment center (different UK Biobank recruitment assessment centre)
c Multivariable-adjusted cox regression model adjusted for ethnic background, index of multiple deprivation (IMD), body mass index (BMI), smoking, drinking, physical activity, sleep time, dietary supplement (vitamin and mineral) use, medication use (NSAIDs, ACEIs, ARBs and beta-blockers), histories of hypertension, histories of diabetes mellitus and long-standing illness
Sensitivity analyses and subgroup analyses
The inverse relationship between dietary scores and CKD incidence remained robust when exposure was lagged by two years (Supplemental Table S5) and four years (Supplemental Table S6). Similar associations were observed when excluding participants with diabetes mellitus (Supplemental Table S7) and those with hypertension (Supplemental Table S8) at baseline. Similarly, the results were similar before (Model 1) and after (Model 2) including total energy intake as a correction variable (Table S9).
Subgroup analyses of other potential CKD risk factors revealed that the inverse association between dietary scores and CKD incidence remained consistent across all subgroups (Fig. 2). Notable interactions were observed between MED Score/AMED Score and CKD risk by BMI; HRs were lower in males compared to females, and in participants aged < 65 years compared to those > 65 years. The inverse association between HEI-2015 and CKD was slightly stronger among individuals who exercised regularly than in those who did not (P for interaction = 0.02; Fig. 2). Stratified analyses by sex showed no statistically significant differences in the associations between dietary patterns and CKD risk (P for trend > 0.05). Similarly, no significant differences were found in the associations between dietary scores and CKD risk across groups defined by alcohol consumption, hypertension status, or medication use (e.g., beta-blockers, ACEIs, and ARBs) (all p for trend > 0.05; Fig. 2).
Fig. 2.
Hazard ratios of chronic kidney disease to the four dietary scores across subgroups
a Cox proportional hazards models were employed to calculate the hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs)
CKD risk stratified by baseline predicted CKD risk and dietary scores
The results indicated that higher values from the equations predicting CKD incidence were associated with an increased risk of CKD in the UK Biobank cohort (Supplemental Table S10). Stratification of CKD risk by baseline predicted risk and dietary scores (Table 3; Fig. 3) revealed that greater adherence to the four healthy dietary patterns was linked to a reduced incidence of CKD, regardless of the baseline predicted risk.
Table 3.
HRs (95% CI) for dietary scores and baseline predicted CKD risk
| Predicted CKD risk a | Case | Participants | hPDI | HEI-2015 | MED Score | AMED Score | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| HR (95%CI) per SD b | P for interaction | HR (95%CI) per SD b | P for interaction | HR (95%CI) per SD b | P for interaction | HR (95%CI) per SD b | P for interaction | |||
| Q1 | 441 | 51,213 | 0.99 (0.89–1.11) | 0.45 | 0.93 (0.83–1.04) | 0.92 | 0.88 (0.78–0.99) | 0.29 | 0.88 (0.79–0.99) | 0.27 |
| Q2 | 933 | 51,065 | 0.90 (0.84–0.98) | 0.93 (0.87–1.01) | 0.98 (0.91–1.05) | 0.99 (0.92–1.06) | ||||
| Q3 | 1,600 | 48,219 | 0.91 (0.86–0.97) | 0.94 (0.89–0.99) | 0.97 (0.91–1.02) | 0.97 (0.92–1.03) | ||||
| Q4 | 2,146 | 40,831 | 0.93 (0.89–0.98) | 0.93 (0.89–0.97) | 0.97 (0.93–1.02) | 0.95 (0.91–0.99) | ||||
a The primary cox regression models for participants without diabetes included demographic variables (age, sex, race/ethnicity), eGFR (linear splines with a knot at 90 mL/min/1.73 m2), history of cardiovascular disease, ever smoker, hypertension, body mass index (BMI), and albuminuria. The primary model for participants with diabetes also included diabetes medications (insulin vs. only oral medications vs. none), hemoglobin A1c values, and the interaction between the two, in addition to the above variables
b Multivariable-adjusted cox regression model adjusted for ethnic background, index of multiple deprivation (IMD), body mass index (BMI), smoking, drinking, physical activity, sleep time, dietary supplement (vitamin and mineral) use, medication use (NSAIDs, ACEIs, ARBs and beta-blockers), histories of hypertension, histories of diabetes mellitus and long-standing illness
Fig. 3.
CKD risk stratified by baseline predicted CKD risk and dietary scores
a Cox proportional hazards models were employed to calculate the hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs)
Discussion
This prospective cohort study, spanning over two decades and involving 207,268 participants, examined the relationship between adherence to four distinct healthy dietary scores (hPDI, HEI-2015, MED Score, and AMED Score) and the incidence of CKD in individuals without pre-existing CKD or related conditions. The findings revealed a significant inverse relationship between adherence to a healthy diet and the incidence of CKD. This negative association remained consistent across subgroups, including populations with varying baseline CKD risks, and was robust even after adjusting for multiple potential confounders. Notably, individuals with a low baseline CKD risk who consistently followed a healthy diet exhibited the lowest incidence of CKD.
As far as we know, research on the relationship between various healthy dietary patterns and the onset of adult CKD remains limited [30]. The present study found that participants with higher scores tended to have lower IMD. While an association between IMD and CKD has been reported, the extent to which this link reflects other risk factors and comorbidities, rather than deprivation itself, remains unclear [31, 32]. The results align with previous studies suggesting that higher hPDI scores are associated with slower eGFR decline and a reduced CKD risk [33]. Furthermore, a systematic review and meta-analysis demonstrated that individuals following a diet with a high hPDI score had a 30% lower risk of CKD and a 23% reduced risk of proteinuria [34]. Data from the Tehran Lipid and Glucose Study [35] and the Uppsala Longitudinal Study of Adult Men cohort [36] further support the association between adherence to the Mediterranean diet and a decreased risk of incident CKD. Additionally, the Northern Manhattan Study [37], ATTICA Study [38], and PREDIMED Trial [39] confirmed the positive impact of the Mediterranean diet on renal function. Consistent with our findings, prior research has shown that higher scores on indices such as HEI, Alternative Healthy Eating Index (AHEI), AMED, and MED are linked to a lower risk of CKD [12] and reduced mortality from renal causes [40].
Despite differences in specific components across the four dietary scores, all emphasize the consumption of vegetables, fruits, and whole grains, while advising limited intake of refined grains, sugars, and red meat. A comparison of the “Eatwell Guide” (UK) [41] and the “Dietary Guidelines for Americans” [19] revealed notable similarities, with both advocating for moderate calorie intake, high consumption of fruits and vegetables, and restriction of sugars, saturated fat, and sodium. Although data from the UK are used in this study, the dietary patterns recommended by the US Dietary Guidelines are selected, as the UK Guidelines focus solely on food group proportions, whereas the US Guidelines prescribe specific dietary patterns [19, 41]. Diets rich in fruits and vegetables reduce the net endogenous acid load, which in turn lowers biomarkers of renal damage and the risk of CKD [42]. These four healthy dietary patterns also emphasize increased fiber intake, with evidence suggesting that a daily increase of 5 g in fiber consumption correlates with an 11% reduction in CKD risk [43]. Furthermore, fiber improves blood sugar regulation and insulin secretion, thereby decreasing the risk of microalbuminuria and proteinuria [44]. The ARIC study found that high red meat consumption was linked to a greater risk of CKD (HR, 1.19; 95% CI, 1.03–1.36) [45], a finding that was also observed in Iranian participants (HR, 1.73; 95% CI, 1.33–2.24) [46]. Both the HEI-2015 and AMED underscore the importance of a balanced fatty acid intake. Moreover, adequate omega-3 and omega-9 fatty acid consumption helps reduce systemic inflammation. A previous study reported that higher levels of seafood-derived n-3 PUFAs were associated with a lower risk of incident CKD, whereas this relationship was not observed for plant-derived n-3 PUFAs [48].
This study expands the understanding of how various healthy diets contribute to reducing CKD risk. The results align with the Dietary Guidelines for Americans, 9th edition [19] which emphasize that adherence to a single dietary pattern is not essential for achieving optimal dietary scores. In clinical practice, combining CKD risk prediction models with dietary interventions can effectively reduce CKD risk.
This study demonstrates the potential benefits of adhering to healthy dietary patterns, highlighting the efficacy of various dietary approaches in mitigating CKD risk. Notably, this effect is independent of predicted risk. The results further reinforce the value of maintaining healthy dietary habits for overall health improvement. Furthermore, the findings suggest that interventions designed to promote healthy eating behaviors could effectively reduce CKD incidence. Supporting evidence from randomized controlled trials [39], meta-analyses of cohort studies [34], and other prospective cohorts [36–38] also indicate that healthy diets are associated with a reduced risk of CKD, corroborating our results. These results have significant implications for dietary guidelines aimed at CKD prevention. Instead of advocating for a single dietary pattern, the emphasis should be on promoting a balanced diet that includes a variety of foods. Additionally, overlap exists among the four dietary patterns assessed in this study, as they all emphasize vegetables, fruits, whole grains, and other similar food groups. This commonality may contribute to their effectiveness in preventing CKD. Compared to unstructured dietary plans, following a comprehensive, guideline-based dietary approach may offer more substantial health benefits.
Nevertheless, this study has several limitations. The 24-hour dietary recall may not accurately reflect actual intake, and recall bias is a potential concern. Additionally, the analysis was not repeated with multiple 24-hour recall assessments. Although several confounding factors were controlled for, the observational nature of the study cannot rule out residual or unmeasured confounding. Furthermore, the study focused on participants aged 40–69 from various regions of England. Replication of these findings in other populations, including those from different countries and with age groups both younger (under 40) and older (over 69), is essential before generalizing the results. Further research is necessary to confirm these results and better understand the long-term effects of dietary interventions in CKD prevention.
This study indicates a correlation between adherence to healthy dietary patterns and a reduced risk of chronic kidney disease (CKD).
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
The authors acknowledge the contributions of participants from the UK Biobank studies. This prospective study utilized data from the ongoing UK Biobank cohort (Application number: 51671; approved in August 2019). The data are accessible directly from the UK Biobank (https://ukbiobank.dnanexus.com/landing).
Abbreviations
- ACEIs
Angiotensin-converting enzyme inhibitor
- AHEI
Alternative Healthy Eating Index
- AMED
Alternative Mediterranean Diet
- ARBs
Angiotensin receptor blockers
- BMI
Body mass index
- CI
Confidence interval
- CKD
Chronic kidney disease
- HbA1c
Albuminuria and glycated hemoglobin
- HEI
Healthy Eating Index
- hPDI
Healthful Plant-based Diet Index
- HR
Hazard ratio
- IMD
Index of multiple deprivation
- MED
Mediterranean Diet
- NSAIDs
Nonsteroidal anti-inflammatory drugs
- PUFAs
Polyunsaturated fatty acids
Author contributions
Concept and design: YPL, BX, TZ, XHW, JQY and ZHZ; Data acquisition, analysis, or interpretation: YPL, BX, TZ, CBQ, SQW and JYZ. Statistical analysis: YPL, BX, TZ, and QSH; Manuscript drafting: YPL, BX, and TZ; Critical revision for intellectual content: CT, YYX, and TJC; Funding acquisition: JQY, ZHZ, BX, and QSH; Study supervision: CT, JQY, ZHZ and XHW; All authors reviewed, revised, and approved the final manuscript.
Funding
This research was supported by the National Natural Science Foundation of China (grant numbers: 82003524, 82170690, 82003408, 82103913) and the Startup Fund for the 100 Top Talents Program, SYSU (grant number: 392012).
Data availability
This prospective study utilized data from the ongoing UK Biobank cohort. The UK Biobank obtained approval from the National Information Governance Board for Health and Social Care in England and Wales, as well as the Community Health Index Advisory Group in Scotland (Application number: 51671; approved in August 2019). Written informed consent was obtained from all participants prior to data collection.
Declarations
Ethics approval and consent to participate
This prospective study utilized data from the ongoing UK Biobank cohort. The UK Biobank obtained approval from the National Information Governance Board for Health and Social Care in England and Wales, as well as the Community Health Index Advisory Group in Scotland (Application number: 51671; approved in August 2019). Written informed consent was obtained from all participants prior to data collection.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yong-Ping Lu, Bin Xia and Xiao-Hua Wang contributed equally to this work.
Contributor Information
Ting Zhu, Email: zhut37@mail.sysu.edu.cn.
Jin-Qiu Yuan, Email: yuanjq5@mail.sysu.edu.cn.
Chun Tang, Email: tangchun@sysush.com.
References
- 1.Zhang L, Wang F, Wang L, et al. Prevalence of chronic kidney disease in China: a cross-sectional survey. Lancet. 2012;379:815–22. [DOI] [PubMed] [Google Scholar]
- 2.Gansevoort RT, Correa-Rotter R, Hemmelgarn BR, et al. Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention. Lancet. 2013;382:339–52. [DOI] [PubMed] [Google Scholar]
- 3.Global regional. National burden of chronic kidney disease, 1990–2017: a systematic analysis for the global burden of Disease Study 2017. Lancet. 2020;395:709–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Romagnani P, Remuzzi G, Glassock R, et al. Chronic kidney disease. Nat Reviews Disease Primers. 2017;3:17088. [DOI] [PubMed] [Google Scholar]
- 5.Crews DC, Liu Y, Boulware LE. Disparities in the burden, outcomes, and care of chronic kidney disease. Curr Opin Nephrol Hypertens. 2014;23:298–305. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Norton JM, Moxey-Mims MM, Eggers PW, et al. Social determinants of racial disparities in CKD. J Am Soc Nephrology: JASN. 2016;27:2576–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Stevens PE, Ahmed SB, Carrero JJ, et al. KDIGO 2024 Clinical Practice Guideline for the evaluation and management of chronic kidney disease. Kidney Int. 2024;105:S117–314. [DOI] [PubMed] [Google Scholar]
- 8.Kazancioğlu R. Risk factors for chronic kidney disease: an update. Kidney Int Suppl (2011). 2013;3(4):368–371. 10.1038/kisup.2013.79. PMID: 25019021. [DOI] [PMC free article] [PubMed]
- 9.Palmer SC, Maggo JK, Campbell KL, et al. Dietary interventions for adults with chronic kidney disease. Cochrane Database Syst Rev. 2017;4:Cd011998. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Lin YC, Lai YJ, Lin YC, et al. Effect of weight loss on the estimated glomerular filtration rates of obese patients at risk of chronic kidney disease: the RIGOR-TMU study. J cachexia Sarcopenia Muscle. 2019;10:756–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Kovesdy CP, Furth SL, Zoccali C. Obesity and kidney disease: hidden consequences of the epidemic. Kidney Int. 2017;91:260–2. [DOI] [PubMed] [Google Scholar]
- 12.Hu EA, Steffen LM, Grams ME, et al. Dietary patterns and risk of incident chronic kidney disease: the atherosclerosis risk in communities study. Am J Clin Nutr. 2019;110:713–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Hu EA, Coresh J, Anderson CAM, et al. Adherence to healthy dietary patterns and risk of CKD progression and all-cause mortality: findings from the CRIC (chronic renal insufficiency cohort) study. Am J Kidney Diseases: Official J Natl Kidney Foundation. 2021;77:235–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Neuhouser ML. The importance of healthy dietary patterns in chronic disease prevention. Nutr Res (New York NY). 2019;70:3–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Shan Z, Li Y, Baden MY, et al. Association between Healthy Eating Patterns and risk of Cardiovascular Disease. JAMA Intern Med. 2020;180:1090–100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Carrero JJ, González-Ortiz A, Avesani CM, et al. Plant-based diets to manage the risks and complications of chronic kidney disease. Nat Rev Nephrol. 2020;16:525–42. [DOI] [PubMed] [Google Scholar]
- 17.Kim H, Yu B, Li X, et al. Serum metabolomic signatures of plant-based diets and incident chronic kidney disease. Am J Clin Nutr. 2022;116:151–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Kim H, Caulfield LE, Garcia-Larsen V, et al. Plant-based diets are Associated with a lower risk of Incident Cardiovascular Disease, Cardiovascular Disease Mortality, and all-cause mortality in a General Population of Middle-aged adults. J Am Heart Association. 2019;8:e012865. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.U.S. Department of Health and Human Services and U.S. Department of Agriculture. (2020). Dietary Guidelines for Americans, 2020–2025. Retrieved from https://health.gov/our-work/nutrition-physical-activity/dietary-guidelines/current-dietary-guidelines
- 20.Klahr S, Levey AS, Beck GJ, et al. The effects of dietary protein restriction and blood-pressure control on the progression of chronic renal disease. Modification of Diet in Renal Disease Study Group. N Engl J Med. 1994;330:877–84. [DOI] [PubMed] [Google Scholar]
- 21.Di Iorio BR, Bellizzi V, Bellasi A, et al. Phosphate attenuates the anti-proteinuric effect of very low-protein diet in CKD patients. Nephrol dialysis Transplantation: Official Publication Eur Dialysis Transpl Association - Eur Ren Association. 2013;28:632–40. [DOI] [PubMed] [Google Scholar]
- 22.Satija A, Bhupathiraju SN, Rimm EB. Plant-based dietary patterns and incidence of type 2 diabetes in US men and women: results from three prospective cohort studies. PLoS Med. 2016;13:e1002039. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Shan Z, Wang F, Li Y, et al. Healthy Eating Patterns and risk of total and cause-specific mortality. JAMA Intern Med. 2023;183:142–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Livingstone KM, Abbott G, Bowe SJ, et al. Diet quality indices, genetic risk and risk of cardiovascular disease and mortality: a longitudinal analysis of 77 004 UK Biobank participants. BMJ open. 2021;11:e045362. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Fung TT, Rexrode KM, Mantzoros CS. Mediterranean diet and incidence of and mortality from coronary heart disease and stroke in women. Circulation. 2009;119:1093–100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Sudlow C, Gallacher J, Allen N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Orliacq J, Pérez-Cornago A, Parry SA, et al. Associations between types and sources of dietary carbohydrates and liver fat: a UK Biobank study. BMC Med. 2023;21:444. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Nelson RG, Grams ME, Ballew SH, et al. Development of risk prediction equations for incident chronic kidney disease. JAMA. 2019;322:2104–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.von Elm E, Altman DG, Egger M et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. [DOI] [PMC free article] [PubMed]
- 30.Guthrie GD, Bell S. Deprivation and kidney disease—a predictor of poor outcomes. Clin Kidney J. 2019;13:128–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Vart P, Gansevoort RT, Joosten MM, et al. Socioeconomic disparities in chronic kidney disease: a systematic review and meta-analysis. Am J Prev Med. 2015;48:580–92. [DOI] [PubMed] [Google Scholar]
- 32.Kim H, Caulfield LE, Garcia-Larsen V, et al. Plant-based diets and incident CKD and kidney function. Clin J Am Soc Nephrol. 2019;14:682–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Bach KE, Kelly JT, Palmer SC, et al. Healthy dietary patterns and incidence of CKD: a Meta-analysis of Cohort studies. Clin J Am Soc Nephrol. 2019;14:1441–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Asghari G, Farhadnejad H, Mirmiran P, et al. Adherence to the Mediterranean diet is associated with reduced risk of incident chronic kidney diseases among tehranian adults. Hypertens Res. 2017;40:96–102. [DOI] [PubMed] [Google Scholar]
- 35.Huang X, Jiménez-Moleón JJ, Lindholm B, et al. Mediterranean diet, kidney function, and mortality in men with CKD. Clin J Am Soc Nephrol. 2013;8:1548–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Khatri M, Moon YP, Scarmeas N, et al. The association between a Mediterranean-style diet and kidney function in the Northern Manhattan Study cohort. Clin J Am Soc Nephrol. 2014;9:1868–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Chrysohoou C, Panagiotakos DB, Pitsavos C, et al. Adherence to the Mediterranean diet is associated with renal function among healthy adults: the ATTICA study. J Ren Nutr. 2010;20:176–84. [DOI] [PubMed] [Google Scholar]
- 38.Díaz-López A, Bulló M, Martínez-González M, et al. Effects of Mediterranean diets on kidney function: a report from the PREDIMED trial. Am J Kidney Dis. 2012;60:380–9. [DOI] [PubMed] [Google Scholar]
- 39.Smyth A, Griffin M, Yusuf S, et al. Diet and Major Renal outcomes: a prospective cohort study. The NIH-AARP Diet and Health Study. J Ren Nutr. 2016;26:288–98. [DOI] [PubMed] [Google Scholar]
- 40.UK Government. The Eatwell Guide. Available online: https://www.gov.uk/government/publications/the-eatwell-guide (accessed on 30 Mar. 2024).
- 41.Rebholz CM, Coresh J, Grams ME, et al. Dietary acid load and incident chronic kidney disease: results from the ARIC Study. Am J Nephrol. 2015;42:427–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Mirmiran P, Yuzbashian E, Asghari G, et al. Dietary fibre intake in relation to the risk of incident chronic kidney disease. Br J Nutr. 2018;119:479–85. [DOI] [PubMed] [Google Scholar]
- 43.Fioretto P, Bruseghin M, Berto I, et al. Renal protection in diabetes: role of glycemic control. J Am Soc Nephrol. 2006;17:S86–89. [DOI] [PubMed] [Google Scholar]
- 44.Haring B, Selvin E, Liang M, et al. Dietary protein sources and risk for incident chronic kidney disease: results from the atherosclerosis risk in communities (ARIC) Study. J Ren Nutr. 2017;27:233–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Mirmiran P, Yuzbashian E, Aghayan M, et al. A prospective study of Dietary Meat Intake and Risk of Incident chronic kidney disease. J Ren Nutr. 2020;30:111–8. [DOI] [PubMed] [Google Scholar]
- 46.Ong KL, Marklund M, Huang L, et al. Association of omega 3 polyunsaturated fatty acids with incident chronic kidney disease: pooled analysis of 19 cohorts. BMJ. 2023;380:e072909. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
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Supplementary Materials
Data Availability Statement
This prospective study utilized data from the ongoing UK Biobank cohort. The UK Biobank obtained approval from the National Information Governance Board for Health and Social Care in England and Wales, as well as the Community Health Index Advisory Group in Scotland (Application number: 51671; approved in August 2019). Written informed consent was obtained from all participants prior to data collection.