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Abstract

Objective:

This study aimed to further investigate the relationship between the ratio of red cell distribution width to serum albumin (RAR) index and the risk of kidney stones in the US population, focusing on the mediating effect of body mass index (BMI), and to provide new insights into the prevention and management of kidney stone disease.

Methods:

This was a cross-sectional study derived from the National Health and Nutrition Examination Survey (NHANES) 2009–2018. The total sample size of the study was 20,755, and the association between RAR and kidney stones was studied using multiple logistic regression, subgroup analysis and mediation analysis.

Results:

The findings of this study demonstrate a substantial positive correlation between the prevalence of kidney stones and the RAR index (OR: 1.37, 95% CI: 1.27, 1.49). This association remains consistent even after adjusting for relevant covariates (OR: 1.23, 95% CI: 1.12, 1.36). Utilising curve fitting, threshold effect analysis and restrictive cubic spline methods, an ‘S’-shaped non-linear dose–response relationship between the RAR index and the prevalence of kidney stones was identified (p for overall < 0.001, p for non-linear = 0.002). In subgroup analyses, the association between RAR index and the risk of kidney stones was more significant in the obese population (OR: 1.33, 95% CI: 1.18, 1.51, p for interaction = 0.021). The results of the mediation analysis found that BMI played a 24.5% mediating role between the RAR index and kidney stones. Sensitivity analysis also confirmed the stability of this result.

Conclusion:

A non-linear association has been identified between the RAR index and the prevalence of kidney stones. The study found that BMI is a mediating variable in this association. In obese individuals of Other Race – Including Multi-Racial, the RAR index has been shown to have a more marked effect on the prevalence of kidney stones.

Keywords

BMI kidney stone mediation analysis NHANES obese RAR index

Introduction

Kidney stone disease is one of the most common benign diseases of the urinary tract. However, with the global incidence of kidney stone disease increasing every year, it has become a major public health burden worldwide.¹ The pathological mechanisms associated with kidney stones are still partly controversial, but the harm they cause to patients is certain.^2,3

In most studies of kidney stones, exposure factors, such as vitamin D levels, obesity and other related indicators, diet, etc., are often chosen to be directly related to independent risk factors for kidney stones.^4–7 But some newer, indirect indicators of the body’s health are often overlooked. The ratio of red cell distribution width to serum albumin (RAR) is a new blood indicator that reflects and assesses the body’s inflammatory response, nutritional status and prognosis for a variety of diseases.^8,9

The mechanism underlying kidney stone formation is a controversial and complex process. Several studies have shown that kidney stones are closely linked to oxidative stress, inflammatory responses and nutritional status.^10–12 Reactive oxygen species (ROS) and inflammatory cascades play a significant role in this process. The production of ROS and the activation of inflammasomes can encourage the deposition of crystalline deposits in the renal interstitium.^12,13 Other studies have found that serum albumin is abundant in kidney stones, which is related to the ability of serum albumin to promote crystallisation and crystal adhesion.^12,14,15 A cross-sectional study has also confirmed that elevated serum albumin levels are a risk factor for calcium oxalate formation.¹⁶ The primary treatment for kidney stones is currently surgical removal of the stones. Preliminary explorations into treatment options have been conducted through in vitro and in vivo experiments based on the pathophysiological mechanisms underlying kidney stone formation. Research has shown that downregulating the expression of ROS and blocking the NLRP3 inflammasome pathway reduces the release of inflammatory factors, thereby decreasing crystal formation and renal tubular damage and ultimately achieving a preventive and therapeutic effect against kidney stones.^17–19 An increase in oxidative stress levels leads to an increase in inflammatory factor release and a negative nutritional balance (low albumin levels), which, in turn, leads to an increase in red blood cell distribution width (RDW) levels.^20–22 Therefore, it is crucial to regulate RDW and albumin levels in the body to prevent and treat the formation and recurrence of kidney stones. Numerous clinical studies have demonstrated the close association between the novel clinical indicator RAR index and various inflammatory-related diseases, including diabetes, metabolic syndrome and rheumatoid arthritis.^23–25 Wu et al.²⁶ suggested a non-linear relationship between the RAR index and the prevalence of kidney stones in US adults. However, this study has obvious shortcomings, and there is no further research that can significantly influence the RAR index in specific populations in the prevalence of kidney stones. Therefore, it is important to further investigate the relationship between the RAR index and the risk of kidney stones.

This study aimed to analyse the association between RAR and kidney stones using data from the National Health and Nutrition Examination Survey (NHANES). To provide new evidence and perspectives for the control and prevention of kidney stone disease.

Methods

Study population and design

The reporting of this study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement.²⁷ The National Center for Health Statistics (NCHS), a division of the Centers for Disease Control and Prevention (CDC), was responsible for the conceptualisation and execution of the NHANES. The objective of the NHANES was to systematically assess the health and nutritional status of adults and children in the United States through home interviews, physical examinations and laboratory tests. All participants in the NHANES programme provided written informed consent. The study encompassed 49,693 participants from five survey cycles from 2009 to 2018, and data on participants’ demographics, exercise time and kidney stones were collected. However, data pertaining to missing values in all variables were excluded from the analysis (see Figure 1 for details).

Figure 1.

Flow chart of the study design.

Definition of exposure and outcome variables

RAR index: RAR = RDW (%)/serum albumin (g/dL).²⁸

Kidney stone disease

In the questionnaire section of the NHANES database, participants were asked ‘Have you ever had a kidney stone?’ and those who answered ‘yes’ were included in the kidney stone group, and those who answered ‘no’ were included in the non-kidney stone group. Those who answered ‘yes’ were included in the kidney stone group, and those who answered ‘no’ were included in the non-kidney stone group.²⁶ This diagnostic approach has been proven to be reliable and scientifically sound by numerous studies.^29,30

Selection of covariates

In this study, we included age, gender, race, educational level, marital status, family income-to-poverty ratio (PIR), smoking, drinking, hypertension, diabetes mellitus, hyperlipidaemia, moderate physical activity, sedentary behaviour, body mass index (BMI), serum uric acid, serum creatinine and serum urea nitrogen as covariates. Smoking based on ‘at least 100 cigarettes in a lifetime?’ Identified as a smoker. Drinking based on ‘at least 12 alcoholic drinks a year?’ Identified as a drinker. Diabetes, according to ‘Doctor told you have diabetes?’ or ‘Take diabetic pills to lower blood sugar?’ or ‘HbA1c ⩾6.5%’ or ‘Fasting blood glucose ⩾7 mmol/L’ or ‘2-hour oral glucose tolerance test plasma glucose ⩾11.1 mmol/L’ is recognised as diabetes mellitus.³¹ Hyperlipidaemia was based on total cholesterol (TC) levels ⩾ 200 mg/dL, triglyceride levels ⩾ 150 mg/dL, low-density lipoprotein (LDL) levels ⩾ 130 mg/dL, or high-density lipoprotein levels ⩽50 mg/dL for women and ⩽40 mg/dL for men Participants self-reporting the use of lipid-lowering medications were also categorised as hyperlipidaemic.³² Moderate recreational activities are recognised according to ‘In a typical week, do {you/SP} continuously engage in any moderate-intensity exercise, fitness, or recreational activity that results in a slight increase in respiration or heart rate, such as brisk walking, cycling, swimming, or volleyball, for at least 10 minutes?’ Identified as moderate recreational activity.^4,33 Sedentary activity is recognised as ‘yes’ based on ‘sitting for more than 5 hours a day’.³³

Statistical analysis

No sampling weights were applied in this study; all analyses were based on the unweighted study sample. Data for continuous variables were presented as means with standard deviations (mean ± SD); data for categorical variables were presented as categorical data by number (%), and comparisons between two groups were made using the chi-squared test. The relationship between the RAR index and kidney stones was analysed using logistic regression models. It was analysed using three models, where model I was the crude model without adjustment for any covariates. Model II is a partially adjusted model, adjusting for age, gender and race. Model III is a fully adjusted model adjusting for age, gender, race, education level, marital status, PIR, smoking, drinking, BMI, hypertension, moderate recreational activities, serum uric acid, serum creatinine, serum urea nitrogen, diabetes mellitus, sedentary activity and hyperlipidaemia. To further explore the dose–response relationship between the RAR index and the prevalence of kidney stones, we employed curve fitting, threshold effect analysis and restricted cubic spline analysis. Curve fitting provides an intuitive visualisation of the trend between the RAR index and the prevalence of kidney stones; threshold effect analysis identifies whether a turning point exists between the two by comparing the goodness-of-fit of two linear models; and restricted cubic spline analysis combines p-values from non-linear tests to assess the linear or non-linear relationship between the RAR index and the prevalence of kidney stones. We used subgroup analysis to assess the moderating effect of BMI on the relationship between the RAR index and kidney stone prevalence, and employed mediation analysis to quantify the proportion of BMI mediating this association. We employed sensitivity analysis to validate the stability of the results. All statistical analyses were performed using R software (version 4.21, R Foundation for Statistical Computing, Vienna, Austria), with a statistical significance threshold of p < 0.05.

Results

Characteristics of the participants

Table 1 demonstrates the baseline characteristics of patients with kidney stones associated with the RAR index. A total of 20,755 participants were included in the study, of whom 2041 (9.83%) had kidney stones. The results showed that the mean age of patients with kidney stones was significantly higher than that of patients without kidney stones (55.53 vs 48.84 years). A higher prevalence of kidney stones was observed in male non-Hispanic whites; more participants smoked, drank alcohol, had high blood pressure, diabetes mellitus, hyperlipidaemia, moderate recreational activities and sedentary activity, suggesting that unhealthy behaviours and populations with underlying medical conditions are more susceptible to developing kidney stones. In addition, the RAR index was significantly higher in the kidney stone group than in the non-kidney stone group.

Table 1.

Characteristics of participants.

Characteristics	Without a kidney stone(N = 18,714)	Kidney stone(N = 2041)	p-Value
Age (years)	48.84 ± 17.65	55.53 ± 16.17	<0.001
Gender			<0.001
Male	9157 (48.93%)	1118 (54.78%
Female	9557 (51.07%)	923 (45.22%)
Race			<0.001
Mexican American	2683 (14.34%)	260 (12.74%)
Other Hispanic	1833 (9.79%)	221 (10.83%)
Non-Hispanic White	7775 (41.55%)	1123 (55.02%)
Non-Hispanic Black	3989 (21.32%)	264 (12.93%)
Other race – including multi-racial	2434 (13.01%)	173 (8.48%)
Education level			0.250
Less than high school	3978 (21.26%)	464 (22.73%)
High school	4269 (22.81%)	445 (21.80%)
More than high school	10,467 (55.93%)	1132 (55.46%)
Marital status			<0.001
Married/living with partners	11,012 (58.84%)	1290 (63.20%)
Never married	3632 (19.41%)	209 (10.24%)
Widowed/divorced/separated	4070 (21.75%)	542 (26.56%)
Family income-to-poverty ratio			0.348
⩽1.3	6014 (32.14%)	638 (31.26%)
>1.3–3.5	7010 (37.46%)	798 (39.10%)
>3.5	5690 (30.41%)	605 (29.64%)
Drinking			0.005
Yes	12,876 (68.80%)	1342 (65.75%)
No	5838 (31.20%)	699 (34.25%)
Smoking			<0.001
Yes	8245 (44.06%)	1065 (52.18%)
No	10,469 (55.94%)	976 (47.82%)
Hypertension			<0.001
Yes	6578 (35.15%)	1045 (51.20%)
No	12,136 (64.85%)	996 (48.80%)
Diabetes mellitus			<0.001
Yes	3343 (17.86%)	627 (30.72%)
No	15,371 (82.14%)	1414 (69.28%)
Hyperlipidaemia			<0.001
Yes	13,370 (71.44%)	1613 (79.03%)
No	5344 (28.56%)	428 (20.97%)
Moderate recreational activities			<0.001
Yes	7950 (42.48%)	738 (36.16%)
No	10,764 (57.52%)	1303 (63.84%)
Sedentary activity			0.113
Yes	11,594 (61.95%)	1301 (63.74%)
No	7120 (38.05%)	740 (36.26%)
BMI (kg/m²)	29.26 ± 7.08	30.83 ± 7.18	<0.001
Serum uric acid (mg/dL)	5.45 ± 1.44	5.63 ± 1.54	<0.001
Serum creatinine (mg/dL)	0.90 ± 0.43	0.97 ± 0.65	<0.001
Serum urea nitrogen (mg/dL)	13.72 ± 5.80	15.25 ± 7.31	<0.001
RDW (%)	13.45 ± 1.36	13.65 ± 1.44	<0.001
Serum albumin(g/dL)	4.24 ± 0.35	4.18 ± 0.34	<0.001
RAR	3.21 ± 0.50	3.30 ± 0.52	<0.001

Data are expressed as the mean ± SD, median (interquartile range) or percentage.

BMI, body mass index, RAR, ratio of red cell distribution width to serum albumin; RDW, red blood cell distribution width.

Association between RAR index and kidney stones

Table 2 shows the relationship between the RAR index and the prevalence of kidney stones analysed by several regression equation models. In model I, a positive correlation was identified between the RAR index and the prevalence of kidney stones (OR = 1.37, 95% CI: 1.27, 1.49). Following adjustment for multiple confounding variables using the model III method, a 23% increase in the prevalence of kidney stones was observed (OR = 1.23, 1.12–1.36), with a one-unit increase in the RAR index. The RAR index was trichotomised, and a positive correlation was identified between the RAR index and the prevalence of kidney stones in each model. Consequently, as the RAR index increased, so too did the prevalence of kidney stones. This finding was further validated by the outcomes of the trend test, which yielded a p-value for trend less than 0.001. The findings indicated a consistent and significant positive correlation between the RAR index and the prevalence of kidney stones, irrespective of the model employed.

Table 2.

Association of the RAR index with kidney stones.

Exposure	Model I	p	Model II	p	Model III	p
	95% CI		95% CI		95% CI
RAR	1.37 (1.27, 1.49)	<0.001	1.44 (1.32, 1.58)	<0.001	1.23 (1.12, 1.36)	<0.001
RAR group
T1	1(ref)		1(ref)		1(ref)
T2	1.39 (1.23, 1.56)	<0.001	1.31 (1.16, 1.48)	<0.001	1.21 (1.07, 1.37)	0.002
T3	1.69 (1.51, 1.90)	<0.001	1.67 (1.48, 1.90)	<0.001	1.37 (1.20, 1.57)	<0.001
p for trend		<0.001		<0.001		<0.001

Model I adjust for none. Adjust II model adjust for age, gender and race. Adjust III model adjust for age, gender, race, education level, marital status, family income-to-poverty ratio, smoking, drinking, BMI, hypertension, moderate recreational activities, serum uric acid, serum creatinine, serum urea nitrogen, diabetes mellitus, sedentary activity and hyperlipidaemia.

BMI, body mass index; RAR, ratio of red cell distribution width to serum albumin.

Non-linear association between RAR index and prevalence of kidney stones

In this study, a non-linear relationship between the RAR index and the prevalence of kidney stones was obtained using curve fitting, threshold effect methods and restricted cubic spline. As shown in Figure 2, there is an ‘S’ curve relationship between the RAR index and the prevalence of kidney stones. In this study, the inflection points in the curves were obtained by the threshold effect analysis method (see Table 3). The results of the standard linear model demonstrated an adjusted ratio (OR) of 1.23 (95% CI: 1.12, 1.36), thereby indicating that an increase in RAR is positively associated with the prevalence of kidney stones. When analysed using a two-stage linear model, the inflection point of the RAR index was determined to be 3.29. The adjusted OR for kidney stones was 1.87 (95% CI: 1.44, 2.41) when the RAR was lower than 3.29 and 1.06 (95% CI: 0.92, 1.21) when the RAR was higher than 3.29, indicating a significant reduction in risk. The log-likelihood ratio of the model was less than 0.001, further supporting the correlation between RAR and kidney stone prevalence. Restricted cubic spline results indicate a significant non-linear association between the RAR index and the prevalence of kidney stones (p for overall < 0.001, p for non-linear = 0.002). See Figure 2S for details.

Figure 2.

Dose–response relationship between RAR index and kidney stones. Curve fitting analysis was used to determine the non-linear relationship between RAR and the risk of kidney stone. The solid red line represents the fitted curve between RAR and risk of kidney stone, while the light red shaded area represents the 95% confidence interval of the curve. The analysis was further adjusted for age, sex, race, educational level, marital status, PIR, smoking, alcohol consumption, BMI, hypertension, moderate physical activity, serum uric acid, serum creatinine, serum urea nitrogen, diabetes mellitus, sedentary behaviour and hyperlipidaemia.

Table 3.

Threshold effect approach to analyse the relationship between RAR index and kidney stones.

Kidney stone	Adjust OR (95% CI)
Fitting by the standard linear model I	1.23 (1.12, 1.36)
Fitting by the two-piecewise linear model II
Inflection point	3.29
RAR < 3.29	1.87 (1.44, 2.41)
RAR > 3.29	1.06 (0.92, 1.21)
Log likelihood ratio	<0.001

Adjustment variables: age, gender, race, education level, marital status, PIR, smoking, drinking, BMI, hypertension, moderate recreational activities, serum uric acid, serum creatinine, serum urea nitrogen, diabetes mellitus, sedentary activity, hyperlipidaemia.

PIR, family income-to-poverty ratio; RAR, ratio of red cell distribution width to serum albumin.

Subgroup analysis

To further investigate the relationship between RAR index and kidney stones, we performed subgroup analyses and focused on the interactions between factors. In this study, age, gender, race, smoking, drinking, hypertension, diabetes, hyperlipidaemia, moderate activity intensity, sedentary activity and BMI variables were stratified. The findings demonstrated that the correlation between the RAR index and kidney stone exhibited stability and consistency across the majority of the subgroups (p > 0.05). Of particular note, a significant interaction was observed between BMI stratification and RAR index, after adjusting for confounders (p > 0.05). This finding indicates that the association between RAR index and the prevalence of developing kidney stones is more pronounced in the obese population (see Table 4 and Figure 3).

Table 4.

Subgroup analysis between RAR and kidney stones.

Variable	OR (95% CI)	p	p for interaction
Age			0.075
⩾50	1.17 (1.04, 1.33)	0.01
<50	1.38 (1.21, 1.57)	<0.001
Gender			0.074
Male	1.43 (1.25, 1.63)	<0.001
Female	1.21 (1.07, 1.38)	0.003
Race			0.112
Mexican American	1.42 (1.13, 1.78)	0.002
Other Hispanic	1.02 (0.78, 1.34)	0.862
Non-Hispanic White	1.33 (1.16, 1.52)	<0.001
Non-Hispanic Black	1.08 (0.88, 1.33)	0.474
Other race – including multi-racial	1.48 (1.10, 2.00)	0.01
Drinking			0.346
Yes	1.30 (1.16, 1.46)	<0.001
No	1.19 (1.03, 1.38)	0.017
Smoking			0.078
Yes	1.21 (1.06, 1.38)	0.004
No	1.41 (1.25, 1.60)	<0.001
Hypertension			0.093
Yes	1.17 (1.03, 1.33	0.014
No	1.36 (1.20, 1.55)	<0.001
Diabetes mellitus			0.974
Yes	1.26 (1.08, 1.46)	0.003
No	1.26 (1.12, 1.41)	<0.001
Hyperlipidaemia			0.832
Yes	1.26 (1.14, 1.40)	<0.001
No	1.24 (1.02, 1.49)	0.029
Moderate recreational activities			0.236
Yes	1.36 (1.16, 1.60)	<0.001
No	1.22 (1.09, 1.36)	<0.001
Sedentary activity			0.984
Yes	1.26 (1.12, 1.41)	<0.001
No	1.26 (1.09, 1.46)	0.002
BMI			0.021
⩾30	1.33 (1.18, 1.51)	<0.001
<30	1.08 (0.94, 1.24)	0.276

The analysis was fully adjusted for age, gender, race, education level, marital status, PIR, smoking, drinking, hypertension, moderate recreational activities, serum uric acid, serum creatinine, serum urea nitrogen, diabetes mellitus, sedentary activity and hyperlipidaemia.

BMI, body mass index; PIR, family income-to-poverty ratio; RAR, ratio of red cell distribution width to serum albumin.

Figure 3.

Association between RAR and prevalence of kidney stones in obese and non-obese populations. The association between RAR and the risk of kidney stone prevalence in the presence or absence of obesity was analysed using a curve-fitting method. After full adjustment for confounders (age, sex, race, educational level, marital status, PIR, smoking, alcohol consumption, BMI, hypertension, moderate physical activity, serum uric acid, serum creatinine, serum urea nitrogen, diabetes mellitus, sedentary behaviour and hyperlipidaemia), there was a positive association between RAR and the prevalence of kidney stones in the obese population. In the non-obese population, there was an inverted U-shaped relationship between RAR and the risk of kidney stone prevalence.

Racial associations between obese and non-obese people and kidney stones

Table 5 shows the results of analysing the relationship between race and kidney stones using multiple logistic regression. Model II was adjusted for age and gender, and Model III was adjusted for the other covariates contained within Model II, which included education level, marital status, PIR, smoking, drinking, hypertension, diabetes mellitus, hyperlipidaemia, moderate activity intensity, sedentary activity, serum uric acid, serum creatinine and serum urea nitrogen. The study found that the obese population was statistically insignificant among Mexican Americans, using the non-obese population as a reference, suggesting that the presence or absence of obesity in this particular population does not increase the risk of kidney stone prevalence in this population. However, in the other four races (other Hispanic, non-Hispanic white, non-Hispanic black and other), the occurrence of obesity had a significant effect on this population, and the results were stable (p < 0.05), which demonstrates that the occurrence of obesity increases the prevalence of kidney stones in this population.

Table 5.

Race in obesity and kidney stones.

Exposure	Model I	Model II	Model III
	OR (95% CI, p)	OR (95% CI, p)	OR (95% CI, p)
Mexican American
Non-obesity	1(ref)	1(ref)	1(ref)
Obesity	1.26 (0.98, 1.63)p = 0.0726	1.25 (0.96, 1.61)p = 0.0936	1.06 (0.81, 1.40)p = 0.6592
Other Hispanic
Non-obesity	1(ref)	1(ref)	1(ref)
Obesity	1.44 (1.09, 1.91)p = 0.0105	1.47 (1.11, 1.95)p = 0.0074	1.37 (1.01, 1.86)p = 0.0418
Non-Hispanic White
Non-obesity	1(ref)	1(ref)	1(ref)
Obesity	1.60 (1.41, 1.81)p < 0.0001	1.60 (1.41, 1.82)p < 0.0001	1.38 (1.20, 1.59)p < 0.0001
Non-Hispanic Black
Non-obesity	1(ref)	1(ref)	1(ref)
Obesity	1.52 (1.18, 1.96)p = 0.0011	1.54 (1.19, 1.99)p = 0.0011	1.33 (1.01, 1.76)p = 0.0427
Other race – including multi-racial
Non-obesity	1(ref)	1(ref)	1(ref)
Obesity	1.84 (1.32, 2.58)p = 0.0004	1.97 (1.40, 2.78)p < 0.0001	1.59 (1.09, 2.30)p = 0.0147

Non-adjusted model adjust for none. Adjust I model adjust for age and gender. Adjust II model adjust for age, gender, education level, marital status, PIR, smoking, drinking, BMI, hypertension, moderate recreational activities, serum uric acid, serum creatinine, serum urea nitrogen, diabetes mellitus, sedentary activity and hyperlipidaemia.

BMI, body mass index; PIR, family income-to-poverty ratio.

Mediation analysis

In this study, the relationship between RAR index and kidney stones was analysed using mediated effect analysis. The results in Figure 4 show that RAR levels may play a role in kidney stone disease through BMI as a mediating variable. BMI accounted for 24.5% of the mediated effect ratio.

Figure 4.

Mediation analysis.

Sensitivity analysis

In this study, to mitigate potential bias risks from various major comorbidities affecting the association between the RAR index and kidney stone prevalence, data were supplemented and excluded from the Supplemental Materials (see Figure 1S). Following the exclusion of chronic inflammatory diseases, advanced liver disease, advanced kidney disease and malignant tumours, a multivariate logistic regression model was established and a mediation effect analysis was conducted (see Table 1S and Figure 3S). The results of the sensitivity analysis indicate that the multivariate logistic regression model and mediation effect analysis, after excluding relevant diseases, maintain consistency and stability with the original findings.

Discussion

This study used information from the NHANES database to analyse the relationship between the RAR index and the prevalence of kidney stones. This study found a non-linear relationship between RAR index and prevalence of kidney stones, which is consistent with the findings of Wu et al.²⁶ On this basis, this study also found a significant interaction between BMI and the RAR index. This indicates a more significant effect of the RAR index on the prevalence of kidney stones in the obese population. To further investigate whether there is a specific population association between the presence of obesity and the development of kidney stones, we conducted analyses using multiple logistic regression modelling methods. The results show that the presence or absence of obesity does not affect the risk of kidney stone prevalence in the Mexican American population. Of note, in other Hispanic, non-Hispanic white, non-Hispanic black and other racial populations, obesity significantly increased the risk of kidney stone prevalence compared with non-obese populations, and the results were robust. The results of the mediation analysis suggested BMI as a mediating variable in the association between RAR index and kidney stones, suggesting that the effect of RAR index on kidney stones is mediated, both directly and indirectly, through BMI. These findings highlight the importance of obesity in the association between RAR index and kidney stones, and provide a reference point for clinical prevention or early intervention of kidney stone disease.

Through extensive literature reviews, we found that the current focus of research on the RAR index is primarily on assessing inflammatory disease prognosis and predicting mortality risk. Liu and Tingting et al. demonstrated that the RAR index is independently associated with depression and has a high predictive value for the condition.^28,34 A large-scale cohort study revealed that the RAR index is independently associated with all-cause and specific mortality in patients with prehypertension and hypertension.³⁵ These studies emphasise the RAR index’s important clinical significance in disease prediction and mortality risk assessment. Furthermore, research has shown a significant positive correlation between inflammatory markers and the prevalence of kidney stones.^29,36 The inflammatory immune response has been shown to promote the formation of Randall’s plaques and calcium stones.^12,37 One study found that serum albumin may play a dual role in the mechanism of renal stone formation.³⁸ On the one hand, as a component of the stone matrix, albumin may promote renal stone formation through crystal aggregation and adhesion.^39,40 On the other hand, albumin has anti-inflammatory and antioxidant effects and inhibits renal stone formation by binding to inflammatory factors to inhibit the inflammatory response and by scavenging reactive oxygen species (ROS) to regulate the balance of oxidative stress.^41–43 Furthermore, the Composite Dietary Antioxidant Index and the oxidative balance score were found to significantly reduce kidney stone prevalence.^44,45 In this study, an S-shaped non-linear relationship was found between the RAR index and kidney stones using curve-fitting analysis. Therefore, the inflection point between the two was obtained by threshold effect analysis. We found that when the RAR index was less than 3.29, there was a significant increase in the risk of developing kidney stones. This is in general agreement with the results of previous studies.²⁶ This may be because the RAR index is a reflection of the level of inflammation and the level of nutritional status of individuals.^28,46

Building on previous findings of a non-linear relationship between the RAR index and kidney stones, the present study also found a significant interaction between obesity and the RAR index. This indicates a significant increase in the risk of kidney stones in an obese population. BMI, WHR and other indices of adiposity are independent risk factors for kidney stones, as numerous studies have shown.^47–49 Therefore, we investigated the role of BMI between the RAR index and kidney stones. We used a mediated effects approach, and the results suggest that BMI plays a partial mediating effect role between RAR index and kidney stones, and the mediation utility ratio reached 24.5%. In addition, this study identified four races that significantly increased the risk of kidney stone prevalence in the obese population: other Hispanic, non-Hispanic white, non-Hispanic black and other races. These findings highlight the importance and complexity of BMI variables, especially in obese populations, in the association between RAR index and kidney stones. It is therefore particularly important to further investigate the mechanisms associated with how BMI, especially in obese populations, affects kidney stones.

Limitations

There are several limitations to this study. Firstly, the nature of this study is a cross-sectional study, which limits the results of this study from showing a causal relationship between exposure factors and outcomes. To confirm this, future studies could consider a large prospective study or a more refined Mendelian randomisation study to confirm. Second, based on the design plan of this study, most of the content was derived from the participants’ questionnaires, and participant recall bias affected the results to some extent. Third, all the data were obtained from the NHANES database, and these findings only indirectly reflect the basic situation of the US population, but may not be applicable to populations in other countries or regions. Finally, although we adjusted for as many confounders as possible, the presence of residual confounding cannot be ruled out. These residual confounders may also affect the final results.

Implications for future research

This study used a nationally representative sample to clarify the non-linear dose–response relationship between RAR index and the prevalence of kidney stones. It also quantified the role of BMI as a partial mediator. The study also found that four ethnic groups in the United States significantly increase the risk of kidney stones in obese populations. Based on these findings, future prospective cohort studies and Mendelian randomisation analyses should be conducted to confirm causal relationships and test whether lifestyle or drug interventions guided by the RAR index can reduce the incidence of kidney stones. In terms of pathogenic mechanisms, as BMI mediates 24.5% of the association between RAR and kidney stones, future studies should explore how obesity amplifies or promotes kidney crystal formation and whether targeted antioxidant or nutritional interventions (e.g. reducing RDW or increasing serum albumin) can inhibit crystal formation. Additionally, the observed racial heterogeneity—particularly the lack of significant effects among Mexican Americans—suggests the need for genome-wide or dietary pattern analyses to identify protective modifying factors. In terms of clinical prediction, integrating the RAR index with existing inflammatory and antioxidant markers could improve kidney stone prediction models. In summary, the findings of this study have clinical significance and provide a reference for the aforementioned future research directions.

Conclusion

The present study demonstrates a non-linear relationship between the RAR index and the prevalence of kidney stones, with an increased risk of kidney stone prevalence observed as the RAR index rises. In specific populations, obesity is associated with a significantly higher risk of developing kidney stones. Furthermore, the RAR index may influence kidney stone disease through BMI as a mediating variable. These findings provide valuable insights into the association mechanism between the RAR index and kidney stones, highlighting the potential role of BMI in this relationship.

Supplemental Material

sj-docx-1-tau-10.1177_17562872251397201 – Supplemental material for BMI mediates the association between red cell distribution width to serum albumin index and kidney stone risk: a population-based study

Supplemental material, sj-docx-1-tau-10.1177_17562872251397201 for BMI mediates the association between red cell distribution width to serum albumin index and kidney stone risk: a population-based study by Yuwen Zhong, Ganglin Kang, Jing He, Kaimin Xiao and Li Li in Therapeutic Advances in Urology

Footnotes

Appendix

Acknowledgements

We would like to thank all participants in this study.

Declarations

ORCID iDs

Kaimin Xiao

Li Li

Supplemental material

Supplemental material for this article is available online.

References

Sorokin

Mamoulakis

Miyazawa

Epidemiology of stone disease across the world. World J Urol 2017; 35(9): 1301–1320.

Tamborino

Cicchetti

Mascitti

Pathophysiology and main molecular mechanisms of urinary stone formation and recurrence. Int J Mol Sci 2024; 25(5): 3075.

Khan

Pearle

Robertson

WG.

Kidney stones. Nat Rev Dis Primers 2016; 2: 16008.

Zhou

Jiang

Age-stratified analysis of the BMI-kidney stone relationship: findings from a national cross-sectional study. Front Med-Lausanne 2025; 12: 1513799.

Association between dietary niacin intake and kidney stones in American adults. Sci Rep-Uk 2025; 15(1): 5666.

Zhang

Uncovering the subtle relationship between vitamin D and kidney stones: a cross-sectional NHANES-based study. Eur J Med Res 2025; 30(1): 202.

Ghoneim

Alghaythee

Alasmari

Impact of diet on renal stone formation. J Fam Med Prim Care 2024; 13(11): 4800–4809.

Tan

You

Cai

The red cell distribution width to albumin ratio: a novel prognostic indicator in Hepatitis B virus-related hepatocellular carcinoma. Int J Med Sci 2025; 22(2): 441–450.

Cai

Zhang

Zhou

Association between red blood cell distribution width-to-albumin ratio and prognosis in post-cardiac arrest patients: data from the MIMIC-IV database. Front Cardiovasc Med 2024; 11: 1499324.

10.

Thongboonkerd

Yasui

Khan

SR.

Editorial: immunity and inflammatory response in kidney stone disease. Front Immunol 2021; 12: 795559.

11.

Siener

Nutrition and kidney stone disease. Nutrients 2021; 13(6): 1917.

12.

Khan

Canales

Dominguez-Gutierrez

PR.

Randall’s plaque and calcium oxalate stone formation: role for immunity and inflammation. Nat Rev Nephrol 2021; 17(6): 417–433.

13.

Merchant

Cummins

Wilkey

DW.

Proteomic analysis of renal calculi indicates an important role for inflammatory processes in calcium stone formation. Am J Physiol-Renal 2008; 295(4): F1254–F1258.

14.

Okumura

Tsujihata

Momohara

Diversity in protein profiles of individual calcium oxalate kidney stones. Plos One 2013; 8(7): e68624.

15.

Dussol

Geider

Lilova

Analysis of the soluble organic matrix of five morphologically different kidney stones. Evidence for a specific role of albumin in the constitution of the stone protein matrix. Urol Res 1995; 23(1): 45–51.

16.

Wang

Zhu

Luo

Analysis of components and related risk factors of urinary stones: a retrospective study of 1055 patients in Southern China. Sci Rep-Uk 2024; 14(1): 28357.

17.

Boldt

Di Sole

Targeting the NLRP3 inflammasome for calcium oxalate stones: pathophysiology and emerging pharmacological interventions. Front Physiol 2025; 16: 1614438.

18.

Sun

Zhang

New insight into oxidative stress and inflammatory responses to kidney stones: potential therapeutic strategies with natural active ingredients. Biomed Pharmacother 2024; 179: 117333.

19.

Sun

Liu

Guan

Atorvastatin inhibits renal inflammatory response induced by calcium oxalate crystals via inhibiting the activation of TLR4/NF-kappaB and NLRP3 inflammasome. Iubmb Life 2020; 72(5): 1065–1074.

20.

Haenggi

Kaegi-Braun

Wunderle

Red blood cell distribution width (RDW) - a new nutritional biomarker to assess nutritional risk and response to nutritional therapy?

Clin Nutr 2024; 43(2): 575–585.

21.

Patel

Ferrucci

Ershler

WB.

Red blood cell distribution width and the risk of death in middle-aged and older adults. Arch Intern Med 2009; 169(5): 515–523.

22.

Weiss

Goodnough

LT.

Anemia of chronic disease. New Engl J Med 2005; 352(10): 1011–1023.

23.

Chen

Guan

Yan

Prognostic value of red blood cell distribution width-to-albumin ratio in ICU patients with coronary heart disease and diabetes mellitus. Front Endocrinol 2024; 15: 1359345.

24.

Zhang

Kang

Red cell distribution width/albumin ratio and mortality risk in rheumatoid arthritis patients: Insights from a NHANES study. Int J Rheum Dis 2024; 27(9): e15335.

25.

Guo

Wang

Miao

Red cell distribution width/albumin ratio as a marker for metabolic syndrome: findings from a cross-sectional study. BMC Endocr Disord 2024; 24(1): 227.

26.

Zhang

Chen

Association of red blood cell distribution width to albumin ratio with the prevalence of kidney stones among the general adult population. Immun Inflamm Dis 2024; 12(11): e70070.

27.

von Elm

Altman

Egger

. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med 2007; 147(8): 573–577.

28.

Shangguan

Weng

Red blood cell distribution width to albumin ratio is associated with increased depression: the mediating role of atherogenic index of plasma. Front Psychiatry 2025; 16: 1504123.

29.

Liu

Xiang

Association between the systemic immune-inflammation index and kidney stone: a cross-sectional study of NHANES 2007-2018. Front Immunol 2023; 14: 1116224.

30.

Weinberg

Patel

Chertow

GM.

Diabetic severity and risk of kidney stone disease. Eur Urol 2014; 65(1): 242–247.

31.

American Diabetes Association Professional Practice Committee. 2. Diagnosis and classification of diabetes: standards of care in diabetes-2024. Diabetes Care 2024; 47(Suppl. 1): S20–S42.

32.

Aygun

Tokgozoglu

Comparison of current international guidelines for the management of dyslipidemia. J Clin Med 2022; 11(23): 7249.

33.

Yang

Yao

JM.

Association between the neutrophil-to-high-density lipoprotein cholesterol ratio with kidney stone risk: a cross-sectional study. Front Endocrinol 2025; 16: 1523890.

34.

Liu

Xiang

Chen

The association between red blood cell distribution width-to-albumin ratio and risk of depression: a cross-sectional analysis of NHANES. J Affect Disorders 2025; 379: 250–257.

35.

Liu

Chen

Association between mortality and the ratio of red blood cell distribution width to albumin concentration in pre-hypertension and hypertension: a population-based cohort study. Maturitas 2025; 198: 108604.

36.

Lee

Bae

Associations between multiple inflammatory biomarkers and the risk of developing kidney stones. BMC Urol 2025; 25(1): 48.

37.

Mulay

Kulkarni

Rupanagudi

KV.

Calcium oxalate crystals induce renal inflammation by NLRP3-mediated IL-1beta secretion. J Clin Invest 2013; 123(1): 236–246.

38.

Priyadarshini Negi

Faujdar

Exploring the molecular level interaction of human serum albumin with calcium oxalate monohydrate crystals. Protein Peptide Lett 2021; 28(11): 1281–1289.

39.

Tanaka

Maruyama

Okada

Multicolor imaging of calcium-binding proteins in human kidney stones for elucidating the effects of proteins on crystal growth. Sci Rep-Uk 2021; 11(1): 16841.

40.

Kaneko

Yamanobe

Onoda

Analysis of urinary calculi obtained from a patient with idiopathic hypouricemia using micro area X-ray diffractometry and LC-MS. Urol Res 2005; 33(6): 415–421.

41.

Wigner

Grebowski

Bijak

The molecular aspect of nephrolithiasis development. Cells-Basel 2021; 10(8): 1926.

42.

Caironi

Tognoni

Gattinoni

Albumin replacement in severe sepsis or septic shock. New Engl J Med 2014; 371(1): 84.

43.

Turell

Radi

Alvarez

The thiol pool in human plasma: the central contribution of albumin to redox processes. Free Radical Bio Med 2013; 65: 244–253.

44.

Duan

Huang

Zhang

Association between composite dietary antioxidant index and kidney stone prevalence in adults: data from National Health and Nutrition Examination Survey (NHANES, 2007–2018). Front Nutr 2024; 11: 1389714.

45.

Chen

Association between oxidative balance score and kidney stone in United States adults: analysis from NHANES 2007–2018. Front Physiol 2023; 14: 1275750.

46.

Cao

Miao

Chen

Association of inflammation and nutrition-based indicators with chronic obstructive pulmonary disease and mortality. J Health Popul Nutr 2024; 43(1): 209.

47.

Chen

Xie

Luo

Association between weight-adjusted waist index and kidney stones: a propensity score matching study. Front Endocrinol 2024; 15: 1266761.

48.

Lovegrove

Besevic

Wiberg

Central adiposity increases risk of kidney stone disease through effects on serum calcium concentrations. J Am Soc Nephrol 2023; 34(12): 1991–2011.

49.

Taylor

Stampfer

Curhan

GC.

Obesity, weight gain, and the risk of kidney stones. Jama-J Am Med Assoc 2005; 293(4): 455–462.

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