Background
Kidney stones are a common disorder of the urinary system characterized by the supersaturation of urinary solutes, leading to crystal precipitation within the renal collecting system and subsequent stone formation [1, 2]. Typical clinical manifestations include the sudden onset of severe renal colic, hematuria, secondary urinary tract infections, and progressive renal dysfunction, with a notable tendency toward recurrence [3]. In recent years, the global prevalence of kidney stones has been steadily increasing, driven by shifts in lifestyle patterns and an aging population [4].
Approximately 10% of individuals are predicted to encounter at least one episode of kidney stones throughout their lifetime, with recurrence rates increasing markedly, consequently considerably diminishing their quality of life and functional status [5, 6]. Beyond localized urological damage, kidney stones have been associated with a range of chronic systemic conditions, including chronic kidney disease, cardiovascular diseases, and metabolic syndrome [7, 8]. Taken together, these largely observational findings suggest that nephrolithiasis more commonly occurs in the context of systemic metabolic disturbances and is more appropriately regarded as a potential downstream complication of metabolic disorders.
Moreover, the condition often necessitates repeated medical consultations and interventions, contributing to considerable healthcare resource utilization and economic burden [9, 10]. Therefore, identifying metabolic factors closely associated with kidney stone formation is of substantial clinical and public health importance. Such insights may support more precise preventive strategies and inform the rational allocation of healthcare resources.
The formation of kidney stones is not driven by a single factor but results from the long-term accumulation and interaction of multiple metabolic abnormalities at the individual level [8, 11]. Elements such as body fat distribution, glycemic and lipid metabolism, and liver function are interrelated and collectively shape overall metabolic burden, contributing to the phenotypic diversity and etiological heterogeneity observed in kidney stone prevalence [12–14]. Traditionally, assessments of kidney stone prevalence have relied on single or limited metabolic parameters, which often fail to comprehensively capture an individual’s broader metabolic profile in clinical and epidemiological contexts [15–17].
This limitation is particularly evident in population-level screening and stratification efforts, where the absence of simple, integrative tools for quantifying metabolic burden constrains the precise identification and management of individuals with elevated risk. Developing an indicator that reflects multidimensional metabolic burden and is accessible and straightforward to implement in routine practice would enable a more complete understanding of the metabolic basis of stone formation and support effective population stratification. In this context, the Zhejiang University Index (ZJU Index) was developed as a sex-specific composite metabolic score that integrates body mass index (BMI), fasting plasma glucose (FPG), triglycerides (TG), and the alanine aminotransferase/aspartate aminotransferase (ALT/AST) ratio into a single indicator of overall metabolic status.
Originally proposed to characterize metabolic burden in nonalcoholic fatty liver disease (NAFLD) [18], the ZJU Index is simple to calculate, relies on routine clinical measurements, and is readily applicable, which has supported its use in epidemiological studies examining metabolic profiles [19, 20]. Cross-sectional analyses have reported positive associations between the ZJU Index and the prevalence of diabetes [21, 22], while other research suggests it may serve as a useful marker for assessing the severity of hepatic steatosis and fibrosis in individuals with NAFLD [23]. Although the ZJU Index was initially designed for evaluating metabolic burden related to NAFLD and diabetes, its integration of body fat distribution, glycemic and lipid metabolism, and liver function theoretically captures the multidimensional metabolic abnormalities that may also be associated with kidney stone prevalence.
In this context, the ZJU Index can be regarded not merely as a liver-related marker but as a pragmatic summary measure of multidimensional metabolic burden, providing a biologically plausible framework for examining its association with kidney stone prevalence. Given that kidney stones are increasingly recognized as a clinical manifestation of systemic metabolic burden within the urinary system, investigating the applicability of the ZJU Index in this context holds important scientific and public health value. Accordingly, this study used the National Health and Nutrition Examination Survey (NHANES) to systematically examine the association between the ZJU Index and kidney stone prevalence, aiming to assess its potential utility as a comprehensive metabolic indicator for epidemiological characterization of urinary stone disease.
Methods
Data source and study design
This study utilized a cross-sectional design, employing data from the 2007–2018 NHANES, which includes structured interviews, physical evaluations, and laboratory tests. All research protocols received approval from the NCHS Ethics Review Board, and signed informed permission was secured from all eligible individuals. Individuals were eligible for inclusion if they satisfied the following criteria: (1) age ≥ 20 years and non-pregnant; (2) provided complete information regarding kidney stone history; and (3) had valid values for all variables required to compute the ZJU index.
Participants with missing values for any of these variables were excluded from the analytic sample.
Definition of the ZJU index
The ZJU index is a composite scoring tool developed to evaluate overall metabolic status. Originally designed to predict the risk of NAFLD and hepatic fibrosis, it has since been applied in epidemiological studies of various metabolic disorders [18, 23, 24]. According to previous studies, the formula is calculated as:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:ZJU=\frac{\mathrm{A}LT}{AST}*3+BMI+TG+FPG(+2\:if\:female)$$\end{document}
Higher scores indicate a greater overall metabolic burden. The additional 2 points assigned to women represent the sex-specific weighting proposed in the original development of the index to account for sex differences in hepatic fat accumulation and related metabolic risk. FPG and TG are expressed in mmol/L, and ALT and AST in U/L.
All biochemical indicators were measured using venous blood samples collected at mobile examination centers and analyzed in certified laboratories. BMI was calculated from measured height and weight. Because it consists of routinely collected clinical parameters, the ZJU index is easy to calculate, accessible, and low in cost, making it highly practical for both clinical and research use.
In this study, the ZJU index was analyzed as a continuous variable and also categorized into quartiles (Q1: <35, Q2: 35–39, Q3: 39–45, Q4: >45) to facilitate group comparisons and trend analyses.
Definition of kidney stones
Kidney stone status was determined using the NHANES questionnaire item: “Have you ever been told by a doctor or other health professional that you had kidney stones?”. Participants who answered “yes” were classified as stone formers, and those who answered “no” were classified as non–stone formers. In this study, kidney stone status represents a self-reported history of stone former identification rather than a clinically adjudicated diagnosis of nephrolithiasis based on imaging or stone composition.
Accordingly, the outcome reflects lifetime stone former status rather than incident stone events. This definition has been widely adopted in previous NHANES-based epidemiological studies and has demonstrated acceptable validity [25–27].
Covariates
To comprehensively investigate the relationship between the ZJU Index and kidney stone occurrence, this study incorporated a broad set of covariates, encompassing demographic information, lifestyle habits, and clinical profiles. Demographic variables encompassed age, sex, race/ethnicity, educational attainment, and the Poverty Income Ratio (PIR), which was classified into three categories (< 1, 1 to < 3, and ≥ 3) according to the ratio of yearly family income to the federal poverty threshold. Lifestyle characteristics included smoking behavior, alcohol intake, and physical activity.
Smoking status was assessed based on whether the subject had smoked a minimum of 100 cigarettes during their lifetime, whereas alcohol use was characterized by the consumption of over 12 alcoholic beverages in the preceding year. Physical activity was quantified using the Global Physical Activity Questionnaire (GPAQ), which estimated weekly metabolic equivalent (MET) minutes; participants reporting < 600 MET-min/week were classified as physically inactive. Clinical variables—such as diabetes, hypercholesterolemia, and chronic kidney disease—were derived from self-reports or prior clinical diagnoses.
Inclusion of these variables in multivariate models aimed to minimize confounding and improve the precision of estimated associations between the ZJU Index and kidney stone prevalence. In a subsample of participants with available laboratory data, additional metabolic and urinary parameters related to kidney stone formation were also considered, including serum uric acid, serum calcium, serum phosphorus, serum sodium.
Statistical analysis
All statistical analyses were based on NHANES 2007–2018 data. The NHANES fasting subsample weights (WTSAF2YR) were applied in all analyses to account for the complex multistage sampling design and to obtain nationally representative estimates. Descriptive statistics were given as means ± SD for continuous variables and weighted proportions for categorical variables.
Group differences were assessed using appropriate tests. Following sequential correction for demographic, lifestyle, and medical variables, multivariable logistic regression was used to assess the independent correlation between the ZJU Index and kidney stone status. Potential multicollinearity between the ZJU Index and correlated metabolic covariates was evaluated using variance inflation factors (VIFs), and no evidence of problematic collinearity was observed (all VIFs were within conventional acceptable limits).
The ZJU Index was evaluated as a continuous and categorical variable, with trend tests performed across quartiles. Restricted cubic spline logistic regression with three knots at the 10th, 50th, and 90th percentiles of the ZJU Index was used to assess nonlinear associations, and a data-driven two-piecewise logistic regression model was then fitted to estimate a change-point in this relationship. Subgroup analyses by sex, age, BMI, and comorbidities were conducted to examine heterogeneity.
In an additional sensitivity analysis, the multivariable logistic regression models were further adjusted for serum uric acid, serum calcium, serum phosphorus, 25-hydroxyvitamin D, and spot urine pH in the subsample with these measurements, in order to evaluate whether the association between the ZJU Index and stone former status persisted after accounting for stone-related biochemical factors. All analyses were performed using R (version 4.2.3), and p < 0.05 was deemed statistically significant.
Results
Baseline characteristics
A total of 14,593 eligible participants were analyzed, consisting of 13,192 persons without kidney stones and 1,401 with kidney stones (Fig. 1). In comparison to non-stone participants, those with kidney stones were often older, had a greater proportion of males and non-Hispanic White individuals, and demonstrated a higher frequency of comorbid illnesses including hypercholesterolemia, diabetes mellitus, and chronic kidney disease (Table 1).
Individuals with kidney stones had markedly increased ZJU Index values, indicating a possible metabolic factor contributing to the pathophysiology of kidney stones.
Fig. 1Participant selection and inclusion process
Table 1Baseline characteristics of the study populationCharacteristicGroupOverallNon-kidney stonesKidney stonesP-valuen14,59313,1921401Age (%)< 507406 (50.8)6891 (52.2)515 (36.8)< 0.001> 507187 (49.2)6301 (47.8)886 (63.2)Sex (%)Female7493 (51.3)6863 (52.0)630 (45.0)< 0.001Male7100 (48.7)6329 (48.0)771 (55.0)Race (%)Mexican American2229 (15.3)2043 (15.5)186 (13.3)< 0.001Non-Hispanic black2919 (20.0)2755 (20.9)164 (11.7)Non-Hispanic white6052 (41.5)5299 (40.2)753 (53.7)Others3393 (23.3)3095 (23.5)298 (21.3)Education level (%)Under high school3661 (25.1)3298 (25.0)363 (25.9)0.567High school3287 (22.5)2976 (22.6)311 (22.2)Above high school7631 (52.3)6904 (52.3)727 (51.9)No record14 (0.1)14 (0.1)0 (0.0)PIR (%)< 12813 (21.4)2553 (21.5)260 (20.5)0.6981–35589 (42.6)5042 (42.6)547 (43.2)> 34713 (35.9)4253 (35.9)460 (36.3)Smoke (%)No8104 (55.5)7390 (56.0)714 (51.0)0.001Yes6477 (44.4)5791 (43.9)686 (49.0)No record12 (0.1)11 (0.1)1 (0.1)Drink (%)No3348 (25.0)3015 (25.0)333 (25.4)0.883Yes10,013 (74.9)9035 (74.9)978 (74.5)No record7 (0.1)6 (0.0)1 (0.1)Activity status (%)Active7570 (51.9)6910 (52.4)660 (47.1)< 0.001Inactive7023 (48.1)6282 (47.6)741 (52.9)Hypercholesterolemia (%)No8056 (61.5)7364 (62.6)692 (52.0)< 0.001Yes4946 (37.8)4318 (36.7)628 (47.1)No record90 (0.7)78 (0.7)12 (0.9)Diabetes (%)No12,275 (84.1)11,246 (85.2)1029 (73.4)< 0.001Yes1953 (13.4)1629 (12.3)324 (23.1)No record365 (2.5)317 (2.4)48 (3.4)CKD (%)No14,077 (96.5)12,784 (96.9)1293 (92.3)< 0.001Yes494 (3.4)390 (3.0)104 (7.4)No record22 (0.2)18 (0.1)4 (0.3)BMI (mean (SD)) (kg/m2)29.14 (6.93)28.97 (6.89)30.65 (7.14)< 0.001Glucose (mean (SD)) (mmol/L)6.12 (2.04)6.08 (2.01)6.51 (2.27)< 0.001TG (mean (SD)) (mmol/L)1.41 (1.20)1.39 (1.20)1.54 (1.18)< 0.001ALT (mean (SD)) (U/L)24.95 (19.51)24.98 (19.88)24.74 (15.61)0.671AST (mean (SD)) (U/L)25.37 (20.33)25.43 (21.09)24.75 (10.95)0.23ZJU index (mean (SD))40.61 (8.23)40.41 (8.17)42.57 (8.51)< 0.001ZJU index (%)Q1 (< 35)3649 (25.0)3419 (25.9)230 (16.4)< 0.001Q2 (35–39)3648 (25.0)3317 (25.1)331 (23.6)Q3 (39–45)3648 (25.0)3267 (24.8)381 (27.2)Q4 (> 45)3648 (25.0)3189 (24.2)459 (32.8)Mean (SD) for continuous variables, % for categorical variablesALT alanine aminotransferase, AST aspartate aminotransferase, BMI body mass index, CKD chronic kidney disease, PIR poverty income ratio, TG triglyceride
Association between ZJU index and kidney stone prevalence
The association between the ZJU Index and kidney stone status was examined through multivariable logistic regression analyses (Table 2). In the unadjusted model (Model 1), the likelihood of kidney stones was significantly associated with higher ZJU Index values (OR = 1.03, 95% CI: 1.03–1.04). The association remained statistically significant in the fully adjusted model (Model 3), which took into consideration demographic, behavioral, and clinical confounders.
A distinct dose–response relationship was observed when participants were stratified into quartiles. Specifically, those in the highest quartiles exhibited a higher likelihood of kidney stones than those in the lowest quartile (Q1). These findings were consistent across all adjustment models, strengthening a consistent and independent positive association between the prevalence of kidney stones and elevated ZJU Index levels.
Table 2The relationship between ZJU index and kidney stonesModel 1OR (95%CI)P-valueModel 2OR (95%CI)P-valueModel 3OR (95%CI)P-valueKidney stonesZJU index1.03 (1.03, 1.04)1.04 (1.03, 1.04)< 0.0011.03 (1.02, 1.04)< 0.001Q1[Reference]< 0.001[Reference][Reference]Q21.60 (1.29, 1.98) < 0.001< 0.0011.49 (1.20, 1.85)< 0.0011.45 (1.12, 1.89)0.005Q31.93 (1.53, 2.43)< 0.0011.82 (1.44, 2.30)< 0.0011.75 (1.33, 2.29)< 0.001Q42.41 (1.95, 2.98)< 0.0012.42 (1.95, 3.01)< 0.0012.14 (1.62, 2.82)< 0.001P for trend< 0.001< 0.001< 0.001CI confidence interval, OR odds ratio, Q quartilesModel 1: no covariates adjusted; Model 2: adjusted for age, sex, and race; Model 3: adjusted for age, sex, race, educational level, PIR, smoke, drink, activity status, diabetes, CKD, hypercholesterolemia
Nonlinear association between ZJU index and kidney stone prevalence
Restricted cubic spline analysis with 95% confidence bands demonstrated a nonlinear association between the ZJU Index and the prevalence of kidney stones (Fig. 2). The adjusted odds of prevalent kidney stones increased with higher ZJU Index values, with a steeper increase at lower to moderate levels and an apparent attenuation of the association at higher levels, accompanied by widening confidence intervals toward the upper end of the distribution.
In two-piecewise logistic regression analysis, a data-driven change in the slope of the association was observed around a ZJU Index value of approximately 44 (Table 3). At lower ZJU Index levels, each 1-unit increase in the ZJU Index was associated with higher odds of prevalent kidney stones (OR = 1.06; 95% CI: 1.04–1.08). At higher ZJU Index levels, the estimated association appeared attenuated, and the corresponding odds ratios did not differ significantly from unity.
This pattern may partly reflect reduced statistical power at the upper end of the ZJU Index distribution due to fewer participants and kidney stone cases.
Fig. 2The RCS curve fitting the association between ZJU index and kidney stones. Adjusted for age, sex, race, educational level, PIR, smoke, drink, activity status, diabetes, CKD, hypercholesterolemia
Table 3Two-stage logistic regression between ZJU index and kidney stonesZJU INDEXOR (95%CI)P-valueKidney stonesStandard linear model1.03 (1.02, 1.04)< 0.001ZJU index < 441.06 (1.04, 1.08)< 0.001ZJU index > 441.01 (1.00, 1.02)0.100Log-likelihood ratio test< 0.001CI Confidence Interval, OR Odds Ratio
Subgroup analyses
Subgroup analyses were performed using the fully adjusted model (Model 3) to explore potential effect modification (Table 4). Across the examined categories, higher ZJU index values were generally associated with a higher prevalence of kidney stones, although the magnitude of the association and the width of the confidence intervals varied between subgroups. Interaction terms between the ZJU index and subgroup variables did not reach statistical significance.
These findings do not provide strong evidence for effect modification; however, because subgrouping reduces sample sizes and statistical power to detect interactions. Overall, the positive association between the ZJU index and prevalent kidney stones appeared broadly consistent across the evaluated strata.
Table 4Subgroup analysis between ZJU index and kidney stonesCharacteristicGroupOR (95%CI)P-valueP for interactionAge< 501.02 (1.00, 1.03)0.0110.3> 501.04 (1.02, 1.06)< 0.001SexMale1.03 (1.01, 1.05)< 0.0010.8Female1.03 (1.01, 1.04)< 0.001SmokeNo1.03 (1.02, 1.05)< 0.0010.3Yes1.03 (1.01, 1.04)< 0.001DrinkingNo1.03 (1.01, 1.04)< 0.0010.6Yes1.03 (1.02, 1.04)< 0.001Activity StatusActive1.03 (1.02, 1.05)< 0.001> 0.9Inactive1.03 (1.01, 1.04)< 0.001CKDNo1.03 (1.02, 1.04)< 0.0010.4Yes1.05 (1.02, 1.09)0.005DiabetesNo1.03 (1.02, 1.04)< 0.0010.7Yes1.04 (1.01, 1.06)0.001HypercholesterolemiaNo1.03 (1.02, 1.04)< 0.0010.2Yes1.03 (1.01, 1.05)0.002
Sensitivity analysis
To evaluate the stability of the findings, a sensitivity analysis was conducted by excluding participants with extreme ZJU Index values (± 3 SD), yielding a final analytic sample of 14,433 individuals (13,057 without and 1,376 with kidney stones). The detailed results are presented in Supplementary Table 1. In both the unadjusted and fully adjusted models, the ZJU Index remained significantly associated with the prevalence of kidney stones (OR = 1.04, 95% CI: 1.03–1.05), supporting the robustness and reliability of the association irrespective of outlier exclusion or analytical strategy.
Discussion
This analysis utilized nationally representative data from NHANES to investigate the relationship between the ZJU Index and kidney stone prevalence through a cross-sectional design. Because kidney stone status in NHANES is based on self-reported stone former history rather than clinically confirmed nephrolithiasis, the observed associations should be interpreted as reflecting relationships between metabolic burden and stone former status at the population level. The findings demonstrated that elevated ZJU Index levels were significantly and consistently associated with higher odds of kidney stone prevalence, even after adjustment for a wide range of covariates.
Moreover, a clear nonlinear dose–response association was observed. Given that the ZJU Index integrates routine clinical parameters, it offers a practical, cost-efficient, and widely applicable metric for use in both clinical and epidemiological research.
The ZJU Index integrates multiple metabolic components that are closely related to clinically diagnosed conditions such as diabetes, dyslipidemia, and chronic kidney disease, and thus reflects cumulative metabolic burden rather than any single disease entity. Previous epidemiological studies have documented associations between single metabolic indicators such as obesity, hyperglycemia, and dyslipidemia and the prevalence of kidney stones [28–31]. However, these analyses generally focused on isolated variables and had limited ability to evaluate the cumulative impact of multidimensional metabolic disturbances.
In this study, the ZJU Index served as an integrated metabolic burden score that combines indicators of body fat distribution, glycemic and lipid metabolism, and liver function within a single framework, thereby addressing some limitations of previous approaches. Our findings indicate that higher ZJU Index values are consistently associated with greater kidney stone prevalence, even after adjusting for a range of potential confounders. The ZJU Index is intended to reflect cumulative metabolic burden rather than any single metabolic disease entity.
In this context, clinically diagnosed conditions such as diabetes and chronic kidney disease may represent downstream manifestations of long-term metabolic burden, while also acting as potential confounders in observational analyses. Accordingly, adjustment for these conditions provides a more conservative estimate of the association between cumulative metabolic burden and kidney stone prevalence, independent of overt, clinically diagnosed metabolic disease. This suggests that a composite score incorporating these metabolic dimensions can effectively characterize the metabolic burden associated with kidney stone prevalence.
Previous epidemiological studies have documented associations between single metabolic indicators such as obesity, hyperglycemia, and dyslipidemia and the prevalence of kidney stones. However, these analyses generally focused on isolated variables and had limited ability to evaluate the cumulative impact of multidimensional metabolic disturbances. In this study, the ZJU Index served as an integrated metabolic burden score that combines indicators of body fat distribution, glycemic and lipid metabolism, and liver function within a single framework, thereby addressing some limitations of previous approaches.
Our findings indicate that higher ZJU Index values are consistently associated with greater kidney stone prevalence, even after adjusting for a range of potential confounders. This suggests that a composite score incorporating these metabolic dimensions can effectively characterize the metabolic burden associated with kidney stone prevalence.
Restricted cubic spline analyses further identified a non-linear dose–response pattern between the ZJU Index and kidney stone prevalence: at lower to moderate ZJU Index levels, prevalence increased more steeply, whereas the slope of the association appeared to attenuate at higher levels. This non-linear pattern should be interpreted as an exploratory and descriptive finding rather than evidence of a definitive biological threshold, particularly given the cross-sectional nature of the data. From a pathophysiological perspective, this attenuation may reflect that, at higher levels of metabolic burden, contributory processes such as urinary supersaturation, acidification, and depletion of crystallization inhibitors approach saturation, thereby limiting further increases in prevalence [32–34].
In addition, compensatory physiological responses—such as enhanced secretion of crystallization inhibitors or improved urinary buffering capacity—may contribute to maintaining urinary chemical stability [35, 36]. These proposed mechanisms remain hypothetical and warrant validation in studies incorporating longitudinal designs, urinary biochemical measurements, and detailed metabolic profiling. Subgroup studies indicated that the positive association between the ZJU Index and kidney stone prevalence remained stable across all demographic and clinical subgroups, with no statistically significant interactions observed.
This consistency indicates strong stability and possible generalizability across other groups. These findings validate the prospective efficacy of the ZJU Index in identifying individuals with increased metabolic load associated with kidney stone prevalence and underscore its potential role in preliminary screening and population stratification initiatives. Especially in resource-limited primary care settings, the ZJU Index—based on routinely collected, easily obtainable clinical data—could serve as a practical tool to help identify individuals with higher metabolic burden who may benefit from further assessment or targeted prevention strategies.
Kidney stone disease is clinically and pathophysiologically heterogeneous, encompassing diverse stone compositions, recurrence patterns, and disease trajectories. In this context, the mechanistic considerations discussed below are intended to reflect population-average pathways linking overall metabolic burden to stone former status. An elevated ZJU Index reflects accumulated metabolic burden arising from abnormal body fat distribution, dysregulated glycemic and lipid metabolism, and altered liver function—all of which are recognized contributors to kidney stone formation.
From a pathophysiological perspective, these multidimensional abnormalities may disrupt urinary composition and crystallization dynamics through several pathways, creating conditions conducive to stone formation. Higher BMI is typically associated with insulin resistance, visceral adiposity, and low-grade chronic inflammation, which may increase urinary excretion of calcium, oxalate, and uric acid while lowering concentrations of inhibitors such as citrate [37–40]. Elevated fasting plasma glucose indicates impaired glucose metabolism, potentially altering renal tubular reabsorption of sodium and uric acid, which can increase urine concentration and acidification, facilitating crystal deposition [41–43].
Increased triglyceride levels reflect dysregulated lipid metabolism, which has been linked to tubular lipotoxicity and urate deposition, and may promote stone formation by altering urinary pH or facilitating uric acid crystallization [15, 44]. Additionally, a higher ALT/AST ratio may indicate nonalcoholic fatty liver disease and associated systemic inflammation, with chronic inflammatory and oxidative stress potentially damaging renal tubular epithelial cells and promoting crystal adhesion [45, 46]. Together, these pathways provide a biological basis for the observed association between the ZJU Index and kidney stone prevalence.
However, these mechanisms remain largely speculative and indirect, underscoring the need for future mechanistic and longitudinal studies to clarify causal relationships and identify potential intervention targets.
This study has numerous significant strengths. The utilization of a substantial, nationally representative NHANES sample augments the external validity and generalizability of the results. Secondly, the ZJU Index is based on standard clinical characteristics, rendering it extremely applicable for widespread clinical use.
Third, a variety of robust statistical techniques—including multivariable logistic regression, quartile trend analyses, limited cubic spline modeling, and two-piecewise regression—were utilized. Subgroup and sensitivity analysis reinforced the consistency and robustness of the observed correlations across many parameters. However, several limitations merit consideration.
The cross-sectional design precludes inference of temporality, limiting conclusions regarding causality. In addition, kidney stone status was based on self-report, potentially introducing recall bias or misclassification. Furthermore, the analysis did not adjust for important confounding variables such as urinary biochemical parameters, dietary intake, and medication use, which may have led to residual confounding.
To further substantiate the clinical relevance of the ZJU Index, future longitudinal studies are warranted to validate its predictive value for kidney stone occurrence and assess its utility in clinical risk stratification models.
Conclusion
In U.S. adults, elevated ZJU index levels are nonlinearly and positively associated with the prevalence of kidney stones, suggesting that the ZJU index may serve as a potential metabolic indicator for assessing urolithiasis risk. Given the routine availability and accessibility of its component variables, the ZJU index holds promise as a valuable tool for epidemiological screening and metabolic risk management in populations at high risk for kidney stones.
Supplementary Information
Supplementary Material 1.