Analysis of risk factors for kidney stones in patients with hyperuricemia: a cross-sectional study based on NHANES

Introduction

Hyperuricemia, a common metabolic disorder characterized by elevated serum uric acid (SUA) levels, has become a significant public health issue. Its prevalence has been steadily increasing in the United States and globally (1,2), establishing it as a key metabolic abnormality alongside hypertension, hyperglycemia, and hyperlipidemia (3). The kidneys play a central role in maintaining uric acid homeostasis, making them particularly susceptible to the pathological consequences of sustained hyperuricemia (4). While classically known for causing gout, the clinical impact of hyperuricemia extends to a spectrum of systemic conditions, including chronic kidney disease and cardiovascular events (3,5).

Nephrolithiasis, or kidney stone disease, is a frequent and well-documented renal complication directly linked to hyperuricemia (6,7). Uric acid stones, which form readily in the acidic and concentrated urinary environment promoted by high SUA levels, constitute a substantial portion of all urinary calculi (6). These stones can cause acute kidney injury, chronic obstruction, and ultimately lead to irreversible renal damage (8). Moreover, beyond forming pure uric acid stones, elevated urate can act as a nidus for the crystallization of calcium oxalate, implying a broader role for hyperuricemia in the pathogenesis of nephrolithiasis (6).

The risk profile for developing kidney stones in the setting of hyperuricemia is complex and multifactorial, influenced by an interplay of genetic, metabolic, and environmental factors (9,10). Lifestyle choices, including diet, hydration, and alcohol consumption, are known modulators of risk (11,12). Concurrently, metabolic conditions such as obesity, diabetes, and hypertension create a pro-lithogenic milieu (9). While associations between systemic inflammation and kidney stone risk have been explored (13), a comprehensive analysis of demographic, clinical, and biochemical parameters within a large, nationally representative cohort of hyperuricemic individuals is still warranted.

The U.S. National Health and Nutrition Examination Survey (NHANES) provides a unique and robust dataset to investigate these complex relationships (14). By utilizing this resource, our study aims to identify key risk factors for the concurrent presence of kidney stones in a representative sample of U.S. adults with hyperuricemia. The specific objectives are to quantify the associations of demographic, clinical, and laboratory variables—including comorbid hypertension, serum calcium, and blood urea nitrogen (BUN)—with nephrolithiasis, thereby providing an evidence base for targeted screening and prevention strategies. We hypothesized that beyond established metabolic factors, specific clinical characteristics and biochemical markers, such as comorbid hypertension, total serum calcium, and BUN, would be independently associated with the prevalence of kidney stones in this high-risk population. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-512/rc) (15).

Methods

Study design and population

This study employed a cross-sectional design using publicly available data from four consecutive cycles of the NHANES (2011–2012, 2013–2014, 2015–2016, and 2017–2018), with data collection spanning from January 2011 to March 2018. NHANES is a program of studies conducted by the National Center for Health Statistics (NCHS) to assess the health and nutritional status of adults and children in the United States. The survey uses a complex, multistage probability sampling design to select a representative sample of the civilian, non-institutionalized population. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study used publicly available and de-identified data from the NHANES. The NHANES protocol was approved by the NCHS Research Ethics Review Board, and all participants provided written informed consent. Because the data are anonymized and publicly accessible, additional ethical approval was not required.

We identified participants with hyperuricemia from the dataset. Exclusion criteria were: (I) missing data on SUA; (II) missing data on the kidney stone questionnaire; (III) use of medications known to significantly alter purine metabolism or uric acid levels (e.g., diuretics, immunosuppressants, chemotherapy agents); and (IV) missing data for any of the key covariates included in the analysis. After applying these criteria, a final sample of 661 participants was included in the study. The detailed screening process for participant selection is shown in Figure 1.

Figure 1 Flowchart of participant selection from the NHANES 2011–2018 database. NHANES, National Health and Nutrition Examination Survey.

Definition of variables

The primary outcome was the presence of kidney stones, which was ascertained from the Kidney Conditions - Urology section of the NHANES questionnaire. Participants were asked, “Have you ever had a kidney stone?” (variable #KIQ026). A “Yes” response was defined as having kidney stones.

Hyperuricemia was defined based on the clinically established thresholds set by the 2020 American College of Rheumatology (ACR) guideline for the management of gout (16), as a SUA level >7.0 mg/dL (>420 µmol/L) for males and >6.0 mg/dL (>360 µmol/L) for females.

Potential risk factors were selected based on previous literature (9,13,17) and clinical relevance. These included: (I) sociodemographic characteristics: age, sex, race/ethnicity, marital status, education level, and family income-to-poverty ratio (PIR); (II) anthropometric measurements: body mass index (BMI) and waist-hip ratio; (III) lifestyle factors: physical activity, sedentary time, alcohol consumption, and water intake; (IV) comorbidities: self-reported history of diabetes and hypertension; and (V) laboratory data: total serum calcium, BUN, serum creatinine, and serum cotinine. Serum cotinine was included as a biomarker for tobacco exposure to control for the potential confounding effects of smoking on systemic inflammation and metabolic health.

Statistical analysis

All statistical analyses were performed using R software (version 4.1.0; R Foundation for Statistical Computing, Vienna, Austria) and SPSS version 26.0 (IBM Corp., Armonk, NY, USA). A two-tailed P value <0.05 was considered statistically significant. Continuous variables were presented as mean ± standard deviation (SD) for normally distributed data or median [interquartile range (IQR)] for skewed data. Categorical variables were presented as counts (n) and percentages (%). Differences between the kidney stone and non-kidney stone groups were compared using Student’s t-test or Mann-Whitney U test for continuous variables, and the Rao-Scott Chi-squared test for categorical variables to account for the complex survey design of NHANES.

Variables with a P value <0.10 in the univariate analysis were included in a multivariable logistic regression model to identify independent risk factors for kidney stones. Results were presented as odds ratios (ORs) with 95% confidence intervals (CIs). We then used restricted cubic splines (RCS) with four knots, placed at the 5th, 35th, 65th, and 95th percentiles of the predictor distributions, to model and visualize the dose-response relationship between continuous predictors and the risk of kidney stones. Finally, a receiver operating characteristic (ROC) curve was constructed using the significant predictors from the logistic regression model to assess their combined predictive performance. The area under the curve (AUC), sensitivity, and specificity were calculated.

Bias consideration

To ensure the validity of our findings, several potential sources of bias were considered. Selection bias was minimized by using the nationally representative NHANES dataset, which employs a complex, multistage probability sampling design. However, the exclusion of participants with missing data could potentially introduce some selection bias if the missingness is not completely at random. Recall bias is a potential concern, as the diagnosis of kidney stones was based on self-report; this may lead to non-differential misclassification, likely biasing results toward the null. Finally, confounding bias was addressed by including a wide range of clinically relevant covariates—identified from existing literature—in our multivariable logistic regression model.

Results

Baseline characteristics of participants

A total of 661 participants with hyperuricemia were included in the final analysis. The mean age was 54.2±17.8 years, and 396 (59.9%) were male. Overall, 78 participants (11.8%) reported a history of kidney stones. A comprehensive comparison of baseline characteristics is presented in Table 1.

Compared to the control group, participants in the kidney stone group were more likely to be non-Hispanic White, had a higher waist-hip ratio, a higher prevalence of comorbid hypertension, lower total serum calcium levels, and higher BUN and serum creatinine levels (all P<0.05). Other factors such as age, sex, marital status, education, BMI, and lifestyle habits did not show significant differences between the two groups.

Risk factors for kidney stones

Variables with P<0.10 in the univariate analysis (race, waist-hip ratio, comorbid hypertension, total serum calcium, BUN, and serum creatinine) were included in the multivariable logistic regression model. As shown in Table 2, after mutual adjustment, comorbid hypertension, total serum calcium, and BUN remained independently associated with kidney stones. Specifically, patients with hypertension had a 2.25-fold higher odds of having kidney stones (OR =2.25, 95% CI: 1.29–4.21, P=0.005). An inverse association was observed for total serum calcium, where higher levels were associated with lower odds of kidney stones (OR per mmol/L increase =0.04, 95% CI: 0.003–0.68, P=0.03). In contrast, higher BUN levels were associated with increased odds of kidney stones (OR =1.12 per mmol/L increase, 95% CI: 1.05–1.20, P=0.002). Race, age, waist-hip ratio, and serum creatinine were not significant in the final model (P>0.05).

Dose-response relationships and predictive model

RCSs were used to explore the dose-response relationships. After adjusting for confounders, there was an inverse association between total serum calcium and the odds of kidney stones, which was generally linear (P for overall association <0.05, P for non-linearity =0.18), as shown in Figure 2. Conversely, a positive, dose-dependent relationship was observed between BUN levels and the odds of kidney stones that was also consistent with linearity (P for overall association <0.01, P for non-linearity =0.68), as presented in Figure 3.

Figure 2 Dose-response relationship between total serum calcium and the risk of kidney stones in patients with hyperuricemia. The solid line represents the adjusted odds ratio, and the shaded area represents the 95% confidence intervals. The model is adjusted for age, sex, race, waist-hip ratio, hypertension, and BUN. BUN, blood urea nitrogen.

Figure 3 Dose-response relationship between BUN and the risk of kidney stones in patients with hyperuricemia. The solid line represents the adjusted odds ratio, and the shaded area represents the 95% confidence intervals. The model is adjusted for age, sex, race, waist-hip ratio, hypertension, and total serum calcium. BUN, blood urea nitrogen.

The ROC model incorporating comorbid hypertension, total serum calcium, and BUN to predict the risk of kidney stones is shown in Figure 4. The AUC was 0.692 (95% CI: 0.651–0.730), indicating moderate predictive ability. The optimal cut-off point, determined by the Youden index (0.334), corresponded to a sensitivity of 82.1% and a specificity of 51.3%.

Figure 4 ROC curve for the prediction model of kidney stones in patients with hyperuricemia. The model includes comorbid hypertension, total serum calcium, and blood urea nitrogen. AUC, area under the curve; ROC, receiver operating characteristic.

Discussion

In this large, nationally representative cross-sectional study, we found a kidney stone prevalence of 11.8% among U.S. adults with hyperuricemia. This rate is substantially higher than that reported in general populations (18), reinforcing the concept that hyperuricemia is a significant risk state for nephrolithiasis. This prevalence is consistent with findings from other regional studies in high-risk populations but is notably higher than the 7–9% reported in the general U.S. population (18,19). Our primary findings identify comorbid hypertension, lower total serum calcium, and elevated BUN as factors independently associated with kidney stones in this high-risk group. These results underscore the complex metabolic interplay underlying stone formation.

The robust association between hypertension and kidney stones (OR =2.25) is well-supported by existing literature, with recent Mendelian randomization studies confirming hypertension as an independent risk factor for kidney stone formation (OR =1.001, P=0.003) (20). This relationship is likely bidirectional; hypertension can induce renal microvascular damage and ischemia, which impairs the handling of urinary solutes and promotes an environment conducive to stone formation. Conversely, hyperuricemia contributes to hypertension through mechanisms like endothelial dysfunction, systemic inflammation, and activation of the renin-angiotensin system (21,22). Our findings highlight that for hyperuricemic patients, the presence of hypertension more than doubles the odds of having kidney stones, emphasizing the critical need for integrated management of both conditions.

The inverse relationship observed between total serum calcium and kidney stone risk, while seemingly counterintuitive, aligns with modern understanding of calcium metabolism and stone formation. Higher dietary calcium is known to bind oxalate in the intestine, thereby reducing its absorption and urinary excretion, which is the primary driver of calcium oxalate stones (23). Our data, reflecting serum calcium, may suggest that a state of lower serum calcium does not confer protection and could be a surrogate for inadequate dietary calcium intake or other metabolic disturbances that promote higher urinary oxalate. This contrasts with the traditional focus solely on hypercalciuria as a risk factor and is supported by reviews cautioning against indiscriminate dietary calcium restriction (24).

Elevated BUN, a marker of both renal function and protein metabolism, emerged as a significant risk factor. This is biologically plausible, as impaired renal function reduces the clearance of stone precursors (25). While self-reported water intake did not differ significantly between groups, it is plausible that subclinical dehydration could contribute to both elevated BUN and a concentrated urinary environment favorable for stone formation, a factor not fully captured by our data. Moreover, high protein intake, a primary determinant of BUN levels, increases the metabolic acid load (from sulfur-containing amino acids), which in turn lowers urinary pH and reduces urinary citrate (a key inhibitor of stone formation), while simultaneously increasing urinary calcium excretion. This combination creates a highly pro-lithogenic environment (11,26). Recent research also points towards the gut-kidney axis, where protein metabolism and gut dysbiosis can influence systemic and urinary metabolites relevant to stone formation (27,28). Alterations in gut microbiota can affect oxalate degradation and intestinal permeability, further impacting the urinary excretion of stone-forming substances. For instance, gut microbiota-derived metabolites like indole-3-acetic acid (IAA) reduce calcium oxalate crystal deposition and renal inflammation by modulating the AHR/NF-κB signaling pathway (29). Additionally, interventions such as fecal microbiota transplantation restore oxalate-degrading bacteria (e.g., Ruminococcaceae_UCG-014) and enhance intestinal barrier function, thereby lowering urinary oxalate excretion and crystal formation (30).

Clinical implications

Our findings have direct clinical relevance for the management of patients with hyperuricemia. The identified risk factors—hypertension, lower serum calcium, and elevated BUN—are all readily measurable in routine clinical practice. This suggests that clinicians should adopt a multifactorial risk assessment approach. For hyperuricemic individuals, particularly those with comorbid hypertension, proactive monitoring of renal function (via BUN and creatinine) and calcium homeostasis is warranted. Furthermore, our results support dietary counseling that focuses on optimizing protein intake to avoid excessive metabolic acid load, rather than simply restricting calcium. These integrated strategies could help stratify patients at higher risk for nephrolithiasis and guide targeted preventive interventions to mitigate their stone burden.

Limitations

This study has several limitations inherent to its design. First, the cross-sectional nature precludes any inference of causality. Second, the diagnosis of kidney stones relied on self-report, which is susceptible to recall bias. Third, we lacked crucial data on urine composition (e.g., pH, volume, specific solutes) and, importantly, stone analysis, which would have provided definitive insights into the types of stones (e.g., uric acid vs. calcium oxalate) prevalent in this cohort. To provide context on this issue, we have summarized typical stone compositions reported in hyperuricemic populations from existing literature (Table S1). Fourth, the definition of hyperuricemia is based on U.S. guidelines (15) and may not be universally applicable. Finally, while our predictive model incorporating these factors showed moderate discrimination (AUC =0.692), its low specificity (51.3%) limits its clinical utility as a standalone screening tool. This suggests that while these factors are important, they are part of a larger constellation of variables contributing to stone risk. Furthermore, the relatively small number of participants in the kidney stone group (n=78) may have limited our statistical power to detect associations with smaller effect sizes for other potential risk factors. Future models could be improved by incorporating novel biomarkers or genetic data (10,31).

Conclusions

In conclusion, this study identifies comorbid hypertension, lower total serum calcium, and elevated BUN as significant factors associated with the prevalence of kidney stones in a large, representative sample of U.S. adults with hyperuricemia. These findings advocate for a multifaceted preventive strategy focusing on diligent blood pressure control, ensuring balanced calcium homeostasis rather than restriction, and optimizing protein intake while monitoring renal function. Clinicians should consider this cluster of risk factors to better stratify and manage individuals with hyperuricemia, aiming to mitigate their burden of nephrolithiasis. Future prospective studies are needed to confirm these associations and to explore whether interventions targeting these risk factors can effectively reduce the incidence of nephrolithiasis in the hyperuricemic population.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-512/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-512/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-512/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. NHANES protocols were approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and all participants provided written informed consent. The present analysis used only de-identified, publicly available data; therefore, no further ethical approval was required.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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