Relationship between serum glucose-to-albumin ratio and kidney stones among nondiabetic U.S. adults: a population-based study

Introduction

Kidney stones are common urinary diseases caused by the deposition of some crystal substances (e.g., calcium, oxalate, urate, and cystine) that are attached to the renal papilla (1). According to the previous study, the prevalence of kidney stones in the U.S. was 11%, with a 1-year incidence of 2.1% (2). Kidney stones, a common urological condition, can progress to chronic kidney disease (CKD), potentially necessitating dialysis (3,4).

Kidney stones are typically diagnosed when they pass spontaneously, are removed, or are detected by imaging tests or surgery. A clinical diagnosis often requires confirmation through imaging techniques such as intravenous urography, noncontrast-enhanced computed tomography (NCCT), X-ray, or ultrasonography (5). Among these methods, plain abdominal X-rays are generally not recommended for evaluating flank pain, whereas intravenous urography and NCCT can be costly. Additionally, the sensitivity of ultrasonography for detecting kidney stones is only 70% (5). Consequently, a cost-effective method for detecting the population with high-risk new-onset and recurrent kidney stones is urgently needed.

Given these diagnostic challenges, new biomarkers for kidney stone risk assessment are under exploration. The serum glucose-to-albumin ratio (sGAR) is a newly introduced index described in a recent study (6). Serum albumin serves as an indicator of systemic inflammatory and nutritional status, which may increase the risk of kidney injury (7). Additionally, albuminuria has long been recognized for its role in triggering pathogenic pathways associated with kidney injury. The clinical significance of proteinuria, particularly albuminuria, is well established in both kidney and cardiovascular disease (8). A recent study highlighted the kidney’s essential role in glucose homeostasis (9). We propose that the sGAR could be linked to the occurrence of kidney stones.

However, limited clinical studies have investigated the possible link between sGAR and kidney stones. In this study, we utilized large-scale, nationally representative data from the National Health and Nutrition Examination Survey (NHANES) 2007–2014 to explore the associations between sGAR and prevalent kidney stones among a nondiabetic population of U.S. adults. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-555/rc).

Methods

Study population

The study was based on data from the NHANES, a nationwide project conducted every 2 years. NHANES employs a complex sampling design to evaluate noninstitutionalized adults’ and children’s health and nutritional status. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Further details on the NHANES are publicly accessible on the official website.

The study initially enrolled 40,617 participants from NHANES 2007 to 2014. Figure 1 demonstrates the flowchart of participant selection. We first excluded individuals with diabetes mellitus (DM) (n=4,375) and those aged <18 years (n=15,823). Participants with missing sGAR data (n=2,057) and kidney stone information (n=1,139) were excluded. Additionally, individuals with missing covariates were excluded (n=7,674). The study ultimately included 9,549 participants.

Figure 1 Flowchart of the population selection process. NHANES, National Health and Nutrition Examination Survey; sGAR, serum glucose-to-albumin ratio.

Variables

sGAR was the exposure variable and was quantified via serum glucose and albumin from blood samples obtained at the mobile examination center (MEC) and stored at −20 ℃. Consistent with prior research, the sGAR was calculated using the formula (10):

sGAR=(serumglucose(mmol/L)serumalbumin(g/L))×100[1]

The outcome variable was prevalent kidney stones. The occurrence and recurrence of kidney stones were assessed through participants’ responses to two interview questions: “Have you ever had kidney stones?” and “How many times have you passed a kidney stone?”. Participants responding “yes” to the first question were classified as having kidney stones. Among individuals with prevalent kidney stones, those who had undergone kidney stones at least two times were seen as having kidney stone recurrence.

Assessment of covariates

The study comprised covariates, including age, sex, race, education level, marital status, poverty-to-income ratio (PIR), body mass index (BMI), energy intake, physical activity, smoking status, alcohol use, hypertension, and coronary heart disease.

Family income was measured through the PIR, including low (PIR ≤1.30), middle (PIR: 1.31–3.50), and high (PIR >3.50). BMI was determined by dividing weight in kilograms by height in meters squared and classified into three categories: BMI <25 kg/m² was normal, BMI ranging from 25.0 to 29.9 kg/m² was overweight, and BMI ≥30 kg/m² was obese. Low energy intake was specified as consuming under 2,000 kcal/day for males and under 1,600 kcal/day for females, while adequate intake ranged from 2,000 to 3,000 kcal/day for males and 1,600 to 2,400 kcal/day for females. Smoking status was determined as never, former, or current use. Hypertension was characterized by a mean blood pressure of ≥140/90 mmHg, self-reported hypertension, or a history of antihypertensive medication (11). Hyperuricemia was defined as a serum uric acid level >7.0 mg/dL for males, and >6.0 mg/dL for females, and gout was identified through self-reported diagnosis (12).

Statistical analysis

This study utilized complex weighted analyses based on the 2-year examination weight. Individuals were categorized into three groups according to the tertiles of the sGAR. Continuous parameters are expressed as means ± standard errors (SEs), while categorical variables are shown as counts and percentages. Clinical characteristics across three groups were analyzed using Kruskal-Wallis H tests among continuous parameters and Pearson’s Chi-squared tests among categorical variables. Weighted logistic regression analyses assessed relationships between sGAR and kidney stone prevalence. Additionally, the associations of serum glucose and serum albumin with kidney stone prevalence were tested via weighted logistic regression analyses. Findings were shown with odds ratios (ORs) and 95% confidence intervals (CIs). Three models were developed: Model 1 was a crude model; Model 2 included adjustments for age, sex, race, educational level, marital status, and family income; Model 3 incorporated additional adjustments for BMI, smoking status, alcohol consumption, energy intake, physical activity, hypertension, and coronary heart disease. Restricted cubic spline (RCS) analyses, adjusted for all covariates, were used to investigate the possible nonlinear relationships.

We performed subgroup analyses to examine the relationships among different factors, such as age, gender, race, education, marital situation, PIR category, BMI, smoking, alcohol use, energy intake, hypertension, coronary heart disease, hyperuricemia and gout. Interaction effects were determined in different subgroups.

Ultimately, we utilized sensitivity analyses to ascertain the robustness of results. Three methods were used in this study. First, sGAR values >99th or <1st percentile were excluded to examine whether extreme values affected the results. Second, we further adjusted for the estimated glomerular filtration rate (eGFR) (13). Third, we adjusted for water intake, an independent variable for the onset and progression of kidney stones.

The study performed all analyses using R software (version 4.4.2). Significant differences were identified for P values <0.05.

Results

Baseline characteristics

The study included 9,549 participants (mean age was 44.52±0.37 years, and females accounted for 49.5%), with 7.71% suffering from kidney stones. sGAR was divided according to tertiles, with 3,190 individuals in Tertile 1, 3,155 in Tertile 2, and 3,204 in Tertile 3. The ranges of the sGAR were <10.98 for Tertile 1, 10.98–12.46 for Tertile 2, and >12.46 for Tertile 3. Clinical characteristics based on tertiles of the sGAR are illustrated in Table 1. Participants in the higher sGAR tertiles tended to be older, female, non-Hispanic black, divorced, less educated, obese, current smokers, alcohol consumers, physically inactive, hypertensive, and prevalent with coronary heart disease (all P<0.05). Family income and energy intake showed no significant differences.

Correlation between sGAR and kidney stone prevalence

Table 2 presents the association between sGAR and prevalent kidney stones. sGAR was significantly associated with higher odds of kidney stone occurrence in three models: Model 1 (OR 1.10, 95% CI: 1.06–1.15), Model 2 (OR 1.08, 95% CI: 1.03–1.13), and Model 3 (OR 1.06, 95% CI: 1.01–1.10). Tertile 3 sGAR showed a positive association with kidney stone occurrence compared to tertile 1 (Model 3: OR 1.45, 95% CI: 1.16–1.82). The relationships between sGAR across tertiles and kidney stone occurrence were significant in three models (all P<0.05). The RCS revealed a linear relationship between sGAR and kidney stone occurrence (P for nonlinearity =0.89) (Figure 2A).

Figure 2 RCS analysis of the sGAR and kidney stone prevalence. (A) Kidney stone occurrence; (B) kidney stone recurrence. RCS, restricted cubic spline; sGAR, serum glucose-to-albumin ratio.

Similarly, a significant positive association of sGAR with kidney stone recurrence was detected (Table 2). In adjusted Model 3, continuous sGAR was linked to elevated odds of kidney stone recurrence (OR 1.08, 95% CI: 1.01–1.16). Individuals in the highest tertile were more likely to have kidney stone recurrence compared to those in the lowest tertile, with odds of 2.04 (95% CI: 1.44–2.89) in Model 1, 1.82 (95% CI: 1.21–2.75) in Model 2, and 1.60 (95% CI: 1.05–2.43) in Model 3. A significant trend of the relationships above was observed (P for trend =0.02). A linear pattern regarding the correlations was ascertained using RCS analysis (P for nonlinearity =0.75) (Figure 2B).

Associations of serum glucose and serum albumin with kidney stone prevalence were tested. As shown in Table S1, continuous serum glucose was significantly linked to increased odds of kidney stone occurrence in all three models (Model 3: OR 1.15, 95% CI: 1.01–1.39, P=0.03). As shown in Table S2, continuous serum albumin was significantly related to a reduced risk of kidney stone occurrence (Model 3: OR 0.94, 95% CI: 0.91–0.97, P<0.001). When divided into tertiles, both serum glucose and albumin remained significantly associated with kidney stone occurrence across all three models. Compared with tertile 1, tertile 2 and 3 of serum glucose increased the risk of kidney stone occurrence by 32% and 29%, respectively, whereas tertile 2 and 3 of serum albumin reduced the risk by 21% and 33% (all P<0.05). Additionally, the trends of associations were statistically significant (all P for trend <0.05). Nevertheless, neither serum glucose nor serum albumin alone indicated a statistically significant association with kidney stone recurrence.

Subgroup analysis and sensitivity analysis

Subgroup analyses across different variables were conducted. As shown in Table 3, no factors were considered moderators of the sGAR and kidney stone occurrence association (all P values for interaction >0.05). Similarly, no modification effect on sGAR or kidney stone recurrence was observed for various factors (all P for interaction >0.05).

Additionally, we performed subgroup analyses through hyperuricemia and gout (Table S3). The sGAR was more related to kidney stone prevalence in participants with hyperuricemia than those without hyperuricemia (kidney stone occurrence: OR 1.27 versus 1.06; recurrence: OR 1.31 versus 1.06). Similarly, the relationship between sGAR and kidney stone prevalence was stronger among participants with gout (kidney stone occurrence: OR 1.14 versus 1.06; recurrence: OR 1.13 versus 1.06).

Sensitivity analyses were established to assess the study’s robustness. After excluding extreme sGAR values, three models indicated a positive association between sGAR and kidney stone prevalence (Table S4). After adjusting for eGFR, the significant associations between sGAR and prevalent kidney stones remained consistent with the initial findings (Table S5). After adjusting for water intake, participants with higher sGAR consistently showed increased odds of kidney stone occurrence and recurrence (Table S6).

Discussion

The population-based study identified an association between the sGAR and higher odds of kidney stone incidence in nondiabetic individuals. RCS analysis confirmed that the sGAR and kidney stone prevalence relationships followed a linear pattern. Additionally, no interaction effect on the correlation between sGAR and kidney stone incidence was detected across various subgroups. Furthermore, the positive associations remained robust in several sensitivity analyses, reinforcing the reliability of our findings.

The management of kidney stones has become a significant concern because of its substantial healthcare costs and societal impact (14). Current treatments for kidney stones include pain control, invasive endourological therapies, and surgical treatment. Serum albumin is a crucial indicator for diagnosing and monitoring numerous diseases, such as cancer, rheumatoid arthritis, ischemia, obesity in postmenopausal women, and other conditions that require glycemic control monitoring (15). Researchers have identified potential macromolecular biomarkers within the albumin-enriched fraction that are associated with serum albumin. This discovery positions the albumin-enriched fraction and albumin as crucial tools for biomarker research (16). Consequently, alterations in the serum albumin concentration may correlate with certain diseases, such as kidney stones, suggesting that these alterations could be biomarkers. Multiple factors, including obesity, diabetes, and hypertension, contribute to kidney stone formation (5). Since the kidney is a critical metabolic organ, it is particularly susceptible to dysfunction stemming from glucose metabolism disorders. The triglyceride-glucose (TyG) index is a well-established marker for insulin resistance (IR). In a cross-sectional study, Qin et al. utilized four NHANES survey cycles and identified positive association of TyG index with kidney stone prevalence in U.S. adults (17). Similarly, another cross-sectional study involving 14,158 adult participants from the NHANES underscored the pivotal role of the TyG index in kidney stone prevalence (18). These studies indicated a significant relationship between blood glucose levels and kidney stones. Given that serum albumin and glucose are risk factors for kidney stone incidence, the sGAR may also contribute to kidney stone development, supporting our study’s findings.

Urinary stones have a recurrence rate of about 50% within 10 years (19). This recurrence can elevate treatment expenses, diminish quality of life, and necessitate patient absences, thereby heightening the economic burden on healthcare systems (20). Kidney stones increase the risk of chronic and end-stage renal disease, often due to renal impairment from obstructive nephropathy (21). A low-protein diet may help lower the probability of kidney stone recurrence. For example, Borghi et al. conducted a 5-year randomized controlled trial that investigated the impact of protein intake on kidney stone recurrence, revealing that a low-protein diet reduced the risk of recurrence (22). Additionally, the recurrence of kidney stones may also affect metabolism in humans. Skolarikos et al. suggested that regular metabolic assessments and follow-up in kidney stone patients can help prevent recurrence (23).

Our study has three notable strengths. First, the NHANES database employs a complex sampling technique and adheres to rigorous quality assurance and standardization protocols, ensuring both data accuracy and national representativeness. Second, subgroup analyses demonstrated that the associations between sGAR and prevalent and recurrent kidney stones remained consistent across diverse populations. Third, we performed three sensitivity analyses to solidify our findings further: (I) including only the 1st to 99th percentiles of data; (II) adjusting for eGFR, given its relevance to kidney function and potential impact on kidney stones; and (III) adjusting for water intake, which is commonly linked to stone formation. All three analyses yielded consistent results.

While the study has notable strengths, it also presents certain limitations. First, the cross-sectional design precludes causal inference and limits the ability to determine whether elevated sGAR levels occurred before or after kidney-stone formation. Further prospective studies are needed to evaluate the predictive potential of sGAR. Second, the reliance on self-report questionnaires for diagnosing kidney stones may introduce recall bias. Additionally, analyses exploring the associations of sGAR with different types of kidney stones were not performed due to the lack of data on stone chemical composition. Further prospective studies are warranted to report chemical compositions of kidney stones and investigate these associations. Third, the NHANES database lacks data on kidney stone composition and provides limited information on water intake and specific dietary habits. Fourth, information on family history of kidney stones was unavailable in the study, which may result in residual confounding. Finally, the lack of dynamic sGAR data restricts the ability to assess how changes in sGAR over time may be related to kidney stone prevalence.

Conclusions

The study identified a notable link between sGAR and kidney stone prevalence. Increased sGAR levels were linked to an elevated risk of kidney stone occurrence and recurrence among nondiabetic American adults.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-555/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-555/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-2025-555/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.

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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