Nonlinear association between estimated glucose disposal rate and kidney stones: a cross-sectional study

Introduction

Kidney stone disease represents a significant global health burden, with a reported prevalence of 1.7%–14.8% worldwide and demonstrating an increasing trend over recent decades.1–5 This condition not only substantially impacts patients’ quality of life but also poses a considerable economic burden on healthcare systems, with annual costs exceeding $10 billion in the United States.6,7 Studies have highlighted the complex relationship between glucose metabolism disorders and kidney stone formation.8–12 Notably, insulin resistance (IR) has emerged as a significant pathophysiological factor potentially contributing to stone formation through various mechanisms.13–15

While the hyperinsulinemic-euglycemic clamp remains the benchmark for evaluating IR,16 its clinical applicability is constrained by its invasive procedure and significant expense. The estimated glucose disposal rate (eGDR), originally developed for insulin sensitivity assessment in type 1 diabetes, has become acknowledged as an effective and practical proxy for insulin sensitivity.17,18 This metric demonstrates strong correlation with clamp-based measurements and offers distinct advantages over traditional indices such as the homeostasis model assessment of insulin resistance (HOMA-IR). While HOMA-IR relies primarily on fasting insulin and glucose concentrations—posing challenges for individuals undergoing insulin therapy—eGDR is derived from glycosylated hemoglobin (HbA1c), waist circumference (WC), and hypertension status. This makes eGDR more clinically applicable and less affected by insulin treatment.

However, direct evidence linking eGDR to kidney stones is currently lacking. This cross-sectional study aims to address this knowledge gap by investigating the relationship between eGDR and kidney stone disease, hypothesizing that lower eGDR values (indicating greater insulin resistance) are associated with a higher probability of kidney stone prevalence. Our findings could potentially identify eGDR as a novel risk predictor for kidney stone disease and inform targeted preventive strategies. Despite these advantages, the potential relationship between eGDR and nephrolithiasis remains unexplored. Our investigation addresses this knowledge gap through a comprehensive cross-sectional analysis, hypothesizing that lower eGDR values (indicating greater insulin resistance) are associated with a higher probability of kidney stone prevalence. We propose that this research could establish eGDR as an innovative predictor of stone risk and guide the development of preventive interventions based on metabolic parameters.

Methods and Materials

Study design and population

We performed a cross-sectional analysis of six consecutive cycles (2007–2018) of the National Health and Nutrition Examination Survey (NHANES), a continuous, nationally representative health surveillance program that collects interview and examination data.

The study targeted participants aged ≥20 years and initially included 34,770 adults. After excluding pregnant participants (n = 372) and those with incomplete data on kidney stone history or eGDR (n = 4645), the analytic sample comprised 29,753 individuals, with detailed selection parameters illustrated in Figure 1. Reporting followed STROBE guidelines for observational studies,19 and all data were collected under the National Center for Health Statistics (NCHS) ethics board approvals with participants’ informed consent (Protocol #2005-06; Protocol #2011-17; Protocol #2018-01).

FIGURE 1. Flowchart of the study population

Outcome and exposure assessment

The dependent variable was self-reported kidney stone history, determined by affirmative responses to stone occurrence queries in NHANES questionnaires “Have you/Has sample person (SP) ever had kidney stones?”.

The primary exposure factor, eGDR, was calculated using: eGDR (mg/kg/min) = 21.158 − (0.09 × WC) − (3.407 × hypertension) − (0.551 × HbA1c),20 where WC stands for waist circumference (in cm), hypertension coded as yes = 1 or no = 0, and HbA1c represents the glycohemoglobin percentage. Hypertension classification required any of: systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, antihypertensive medication use, or physician diagnosis. We stratified participants into eGDR tertiles, with the lowest representing maximum IR serving as the reference group.

Covariates assessment

Our analysis incorporated numerous adjustment variables across several domains: Demographics characteristics included age (dichotomized at 60 years), sex, racial/ethnic background, marital status, education attainment, poverty income ratio (PIR; categorized as ≤1.3, >1.3 & ≤3.5, >3.5); Anthropometric measurements encompassed body mass index (BMI; stratified as <25, 25–30, and ≥30 kg/m2); Behavioral factors comprised smoking history (defined as “current” for participants reporting >100 lifetime cigarettes with ongoing use either daily or on some days; “former” for those with >100 lifetime cigarettes but current abstinence; and “never” for individuals with <100 lifetime cigarettes), alcohol intake (classified as drinker: ≥12 drinks annually; non-drinker: <12 drinks annually), physical activity (<500 or ≥500 metabolic equivalent of tasks [METs]) per week); Dietary patterns utilized the healthy eating index 2015 scores (HEI-2015, divided into tertiles using the methodology described previously21); Medical comorbidities (coded dichotomously) encompassed gout, stroke, diabetes mellitus (established via clinical diagnosis, glycohemoglobin ≥6.5%, fasting glucose ≥7.0 mmol/L, random glucose ≥11.1 mmol/L, abnormal oral glucose tolerance test (OGTT) results (≥11.1 mmol/L), or current use of glucose-lowering medications. Participants with self-reported prediabetes, impaired glucose tolerance, and impaired fasting glycaemia were classified under “pre-diabetes”), cardiovascular disease (CVD; established by presence of coronary heart disease, angina, or congestive heart failure), chronic kidney disease (CKD; characterized by estimated glomerular filtration rate <60 mL/min per 1.73 m2 and/or albuminuria ≥30 mg/g22) and cancer. Survey cycle served as an additional controlling factor. Missing data indicators were created when incomplete information exceeded 2% for any covariate.

Statistical analysis

Statistical analyses employed NHANES complex survey weighting methodology as specified by the Centers for Disease Control and Prevention (CDC). Survey weights were combined across six sequential cycles according to established NHANES analytical guidelines. Descriptive data are reported as weighted means with 95% confidence intervals (CIs) for continuous measures and weighted percentages with 95% CIs for categorical variables.

Three sequential regression frameworks were developed to examine associations between eGDR and kidney stones. Model 1 served as the unadjusted baseline analysis. Model 2 controlled for basic demographic parameters (age, sex, ethnicity). The comprehensive third model (model 3) accounted for multiple variables: demographic characteristics, socioeconomic indicators (PIR, education, marital status), lifestyle factors (smoking, alcohol consumption, physical activity, HEI-2015), and comorbid conditions (gout, CVD, CKD, diabetes, stroke, and cancer). eGDR was evaluated both continuously and categorized into tertiles for a complete assessment of kidney stone associations. Trend testing utilized ordinal treatment of tertile classifications. Non-linear relationship detection employed a generalized additive model (GAM) with spline smoothing techniques and segmented regression analysis. Additionally, we performed subgroup analyses using stratified logistic models based on all potential confounders included in the fully adjusted model (model 3), excluding the stratifying variable itself.

Statistical computations utilized R software version 4.2.0 and EmpowerStats (http://www.empowerstats.com; X&Y Solutions, Inc., Wilmington, DE, USA), with statistical significance defined as p < 0.05 (two-tailed).

Results

Study population characteristics

Our analysis included 29,753 U.S. adults (Figure 1 describes the detailed inclusion and exclusion criteria), with a weighted mean age of 47.52 years (95% CI: 47.08–47.97) and female representation of 51.11% (95% CI: 50.51–51.72). Table 1 displays participant characteristics stratified by eGDR tertiles. The sample-weighted kidney stone prevalence was 9.90% (95% CI: 9.42–10.41), with a mean eGDR of 7.83 (95% CI: 7.75–7.90).

Association between eGDR and kidney stones

Multivariable analyses revealed an inverse relationship between eGDR and kidney stone prevalence after comprehensive covariate adjustment (odds ratio [OR] = 0.92, 95% CI: 0.89–0.94). When examining eGDR tertiles, participants in the highest tertile demonstrated substantially lower odds of kidney stone compared to those in the lowest tertile (adjusted OR = 0.52, 95% CI: 0.43–0.64). All regression models consistently showed significant negative trends (p < 0.001) across tertiles (Table 2).

Stratified analyses

Stratified analyses (Figure 2) identified several effect modifiers in the eGDR-nephrolithiasis relationship, including age, educational attainment, kidney function status, and diabetes diagnosis. Notably, while the magnitude of association varied across subgroups, the inverse relationship remained consistent (OR < 1.0) throughout all stratifications.

FIGURE 2. Subgroup analysis of the association between kidney stones and estimated glucose disposal rate (eGDR). Note: BMI, body mass index; HEI-2015, healthy eating index 2015 scores; CKD, chronic kidney disease; CVD, cardiovascular disease; CI, confidence interval

This forest plot demonstrates the heterogeneous associations between kidney stones and eGDR across various demographic and clinical subgroups. The OR and 95% CI indicate the strength and statistical significance of the relationship in different strata. Notably, significant interactions were observed in age, educational status, CKD, and diabetes (p for interaction <0.05), suggesting that the impact of kidney stones on eGDR varies across these characteristics. A value below 1.0 indicates a negative association between kidney stones and eGDR.

Nonlinear analysis

Smooth curve fitting analysis uncovered a nonlinear pattern between eGDR and stone occurrence (Figure 3). The analysis indicated a fully adjusted smooth curve. A likelihood-ratio test was employed to compare the piecewise linear regression model with the binary logistic regression model. The results indicated a significant difference between the two models, with a p-value of less than 0.001, as shown in Table 3.

FIGURE 3. Nonlinear relationship between kidney stones and estimated glucose disposal rate (eGDR)

Smooth curve fitting analysis revealing the potential nonlinear correlation between kidney stones and eGDR. The curve illustrates the complex, potentially dose-dependent relationship, showing how the glucose disposal rate may change with variations in kidney stone prevalence.

Our findings demonstrated a threshold effect, with the inflection point identified at eGDR = 7.95. Specifically, when eGDR ≤ 7.95, no significant correlation was observed between eGDR and the prevalence of kidney stones. In contrast, an eGDR greater than 7.95 was significantly negatively associated with the prevalence of kidney stones.

We further divided the dataset into two segments based on the inflection point derived from the piecewise linear regression model and conducted weighted multivariable logistic regression analyses for each subset. The results (Table 4) showed that, for eGDR ≤ 7.95, there was still no significant correlation between eGDR and the prevalence of kidney stones (OR = 0.96, 95% CI: 0.91–1.02), whereas for eGDR > 7.95, a significant negative association was found (OR = 0.81, 95% CI: 0.71–0.92).

Discussion

In this large-scale cross-sectional study of 29,753 American adults, we found that the overall prevalence of kidney stones was 9.90%, and we identified a significant inverse relationship between eGDR and kidney stone prevalence, with a threshold effect observed at an eGDR value of 7.95. Specifically, we observed that the negative association between eGDR and kidney stones was only significant when eGDR exceeded 7.95 (OR = 0.81, 95% CI: 0.71–0.92), while no significant association was found below this threshold. Our findings suggest that individuals with higher insulin sensitivity, as indicated by higher eGDR values, were associated with a lower kidney stones prevalence.

Previous studies have investigated the association between insulin resistance and kidney stones.9,23–26 Cross-sectional analyses of NHANES data have provided compelling evidence for this relationship. Specifically, an analysis of NHANES 2007–2010 demonstrated that after adjusting for relevant factors, individuals in the highest tertile of HOMA-IR had significantly higher odds of kidney stones compared to those in the lowest tertile (OR = 1.50, 95% CI: 1.09–2.07).23 Similarly, a subsequent study using NHANES 2015–2018 data revealed even stronger associations, with participants in the highest quartile of HOMA-IR showing more than twice the odds of kidney stones compared to those in the lowest quartile (OR = 2.36, 95% CI: 1.55–3.61).9 Our findings, which demonstrate an inverse relationship between insulin sensitivity and kidney stone formation, align with these previous observations. However, it is noteworthy that not all studies have reached consistent conclusions. For instance, a cross-sectional study involving 2171 Japanese participants found no significant association between fasting serum insulin or HOMA-IR and kidney stone risk.24 These inconsistent results highlight that the link between insulin resistance and kidney stones is complex, and different markers of insulin resistance may vary in their predictive value.

The mechanistic relationship between insulin resistance and kidney stone formation has been well-documented in the literature. Insulin resistance has been shown to induce several pathophysiological changes that promote stone formation, including decreased urinary pH, impaired ammoniagenesis, altered sodium reabsorption, and hypocitraturia.8,15,27,28 Some studies elaborated on these mechanisms, demonstrating that insulin resistance leads to alterations in 24-h urinary electrolyte excretion patterns, resulting in more acidic urine, impaired renal acid excretion, and hypocitraturia.8,15,28 The impact of insulin resistance on renal ammonium production occurs through both direct and indirect pathways.28 Insulin resistance directly compromises renal tubular ammonia production and increases sodium reabsorption, which leads to more acidic urine.27,28 This acidic environment promotes kidney stone formation.29

These findings provide important insights into the metabolic aspects of kidney stone disease, though the direct clinical applicability of eGDR requires careful consideration. While the identification of an eGDR threshold at 7.95 represents a novel epidemiological finding, it is important to recognize that eGDR is derived from waist circumference, blood pressure status, and HbA1c—parameters that are already well-established independent predictors of both metabolic health and stone risk. For practicing clinicians, these individual components already provide actionable information for patient assessment and management without requiring additional calculations. Currently, eGDR should be viewed primarily as a research tool and epidemiological construct that integrates multiple metabolic parameters into a single continuous variable, allowing for more standardized risk stratification across populations in research settings. The main value of eGDR lies in its ability to provide a unified measure of insulin sensitivity that facilitates comparative studies and population-level analyses. However, its superiority over individual component variables in predicting stone risk in clinical practice remains to be established. Future studies would need to demonstrate that eGDR provides incremental predictive value beyond its individual components before recommending its routine use in frontline clinical practice. Moreover, it’s important to consider that this relationship may be bidirectional. While eGDR might affect kidney stone risk, treating stone disease may also change eGDR components. For example, interventions like exercise and diabetes management—both important for stone prevention—can reduce waist circumference and HbA1c levels, thereby increasing eGDR values. For eGDR values below 7.95, no correlation was observed, which may indicate the presence of a threshold effect. There could be a critical point of insulin sensitivity above which protective effects against kidney stones become evident. Alternatively, reverse causation may play a role: individuals with lower eGDR may already have multiple metabolic abnormalities, with kidney stones being a consequence rather than a cause. Additionally, residual confounding may be a factor, as individuals with lower eGDR may have more unmeasured confounders. These underscore the need for a comprehensive approach that accounts for the potential interplay between eGDR, metabolic factors, and kidney stone formation.

Future research should focus on prospective studies to establish causality between eGDR and kidney stone formation. Investigations incorporating stone composition analysis and detailed metabolic profiling could help elucidate the underlying mechanisms. Additionally, intervention studies targeting insulin sensitivity improvement could evaluate whether this approach effectively reduces stone risk. Validation studies in diverse populations are needed to confirm the generalizability of the eGDR threshold effect.

Strengths and limitations

Our study has several strengths, including its large sample size, nationally representative population, and comprehensive adjustment for potential confounders. The use of NHANES data ensures high-quality standardized measurements and general applicability to the U.S. adult population. However, several limitations should be acknowledged. First, the cross-sectional design precludes causal inference regarding the relationship between eGDR and kidney stones. Second, the reliance on self-reported kidney stone history may introduce recall and misclassification bias. Third, we lacked information on stone composition and stone recurrence status, and could not assess whether the observed associations differ by stone type and recurrence status. And, the absence of urinary biochemical data to support the mechanisms. Additionally, although this study adjusted for a number of relevant covariates, there may still be other confounding factors, such as detailed diet, genetics, etc. Finally, the single time-point measurement of eGDR may not fully capture the dynamic nature of insulin sensitivity. Furthermore, the threshold effect observed at eGDR = 7.95 should be interpreted as an exploratory finding that requires validation in independent cohorts before clinical application.

Conclusions

In conclusion, this large cross-sectional study demonstrates a novel nonlinear relationship between eGDR and kidney stones, with a significant threshold effect at eGDR = 7.95. The inverse association between eGDR and kidney stone prevalence was only observed above this threshold, suggesting a complex relationship between insulin sensitivity and stone formation. These findings provide new insights into the metabolic aspects of kidney stone disease and suggest potential strategies for risk assessment and prevention. While causality needs to be established through prospective studies, our results support the importance of metabolic health in kidney stone disease and suggest that eGDR could be a valuable clinical tool for stone risk assessment.

Acknowledgement

We thank the staff of the National Center for Health Statistics (NCHS) of the Centers for Disease Control (CDC) and Prevention and the participants who contributed to NHANES.

Funding Statement

This work was supported by the Doctoral Fund Project of North Sichuan Medical College (grant number: CBY22-QDA26) and the Opening Project of the Medical Imaging Key Laboratory of Sichuan Province (grant number: MIKL202408).

Author Contributions

Conceptualization and methodology: Shan Yin; data acquisition: Shan Yin, Zhenzhen Yang, Linxin Jiang; software and formal analysis: Zhenzhen Yang, Linxin Jiang, Shan Yin; writing—original draft: Zhenzhen Yang, Linxin Jiang; writing—review & editing: all authors; data curation and supervision: Shan Yin. All authors reviewed the results and approved the final version of the manuscript.

Availability of Data and Materials

The data presented in this study are available on the NHANES website (https://wwwn.cdc.gov/nchs/nhanes/default.aspx, accessed on 01 January 2025).

Ethics Approval

Data for this study were collected under the National Center for Health Statistics (NCHS) ethics board approval with participants’ informed consent (Protocols #2005-06, #2011-17, and #2018-01).

Conflicts of Interest

The authors declare no conflicts of interest to report regarding the present study.

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