Abstract
Background
Kidney stone disease is a common urological condition with a rising worldwide incidence. Although multiple metabolic and lifestyle risk factors have been identified, determinants influencing the age at which symptoms begin remain poorly defined. Understanding factors associated with earlier disease presentation may improve prevention strategies.
Methods
This observational cohort study included 546 patients with confirmed nephrolithiasis. Participants were stratified into early-onset (<45 years) and late-onset (≥45 years) groups. Sociodemographic, lifestyle, clinical, urinalysis, and biochemical data were collected. Group comparisons were performed using the Chi-square test. Multivariable linear regression analyses evaluated independent associations between age of onset and clinical, laboratory, and hormonal variables. Statistical significance was set at p < 0.05.
Results
Early-onset disease accounted for 57.1% of cases. Higher body mass index, smoking, functional status, employment, and education level were significantly associated with age of onset (p < 0.001). Several comorbidities, including hypertension, diabetes mellitus, cardiovascular disease, renal impairment, gout, gallstones, and renal cell carcinoma, were inversely associated with age of onset. Regression analyses confirmed younger onset as an independent predictor of hypertension (β = −0.374), diabetes (β = −0.381), heart disease (β = −0.253), renal impairment (β = −0.110), gout (β = −0.120), and gallstones (β = −0.148) (p < 0.05). Urinalysis parameters showed limited associations, with urinary crystals as the only significant predictor (p = 0.012). Elevated serum urea and calcium were independently associated with earlier onsets.
Conclusion
Early-onset nephrolithiasis is strongly associated with metabolic and cardiovascular comorbidities and specific biochemical abnormalities. Age of onset may serve as a clinically meaningful marker for risk stratification and targeted preventive interventions.
Keywords: nephrolithiasis, age of onset, cardiometabolic comorbidities, biochemical risk factors, risk stratification
Introduction
Kidney stone disease, also known as nephrolithiasis, is a chronic and recurrent condition with a substantial global health burden. It affects healthcare systems, patients, and economies worldwide, with estimates suggesting that 5% to 12% of individuals will experience kidney stones during their lifetime, with considerable geographic variation.1,2 In recent decades, the incidence of nephrolithiasis has steadily increased, particularly in populations undergoing rapid dietary and lifestyle transitions.3 Notably, the disease is no longer confined to middle-aged men; it is increasingly observed in women, adolescents, and even children.4,5 This evolving epidemiology reflects the complex interplay of genetic, metabolic, dietary, environmental, and demographic factors that contribute to stone formation.6–8 Despite significant advances in understanding disease recurrence and risk factors, relatively little attention has been given to the timing of initial disease manifestation.
The timing of nephrolithiasis onset represents a clinically important yet underexplored dimension of disease characterization. While prior studies have predominantly focused on prevalence and recurrence, fewer investigations have examined the timing of disease onset.9 This distinction is important because early and late presentation may reflect fundamentally different underlying mechanisms. Early manifestation, particularly during childhood or young adulthood, is often associated with inherited metabolic disorders or rare genetic conditions such as cystinuria, primary hyperoxaluria, or Dent disease.10–12 These patients may experience more aggressive disease progression, including recurrent stone formation, nephrocalcinosis, and progression to chronic kidney disease if not appropriately managed.13 In contrast, later presentation is more commonly linked to age-related metabolic changes and is frequently associated with comorbid conditions such as obesity, hypertension, and diabetes, as well as lifestyle factors, including high sodium intake, reduced fluid intake, and increased animal protein intake.14,15 Accordingly, the timing of disease onset may serve as a meaningful clinical indicator reflecting the relative contributions of genetic predisposition and acquired metabolic risk.
The concept of disease onset timing as a marker of broader biological processes has been increasingly recognized in other chronic conditions. Emerging evidence suggests that earlier disease manifestation is often associated with accelerated biological aging, systemic inflammation, and metabolic dysregulation. Studies have demonstrated that biomarkers and composite indices of biological age correlate strongly with the early development of conditions such as diabetes mellitus, cardiovascular disease, and autoimmune disorders.16–21 In nephrolithiasis, a similar framework suggests that early stone formation may reflect an underlying systemic metabolic phenotype, whereas later onset may indicate cumulative environmental exposure and progressive metabolic burden.22 In addition, several biochemical markers have been implicated in stone pathogenesis and may vary with age. For example, urinary osteopontin and fetuin-A have been proposed as potential modulators of crystal formation and inhibitors of calcification, with alterations in their levels associated with an increased risk of stone formation. These biomarkers highlight the complex interaction between metabolic, inflammatory, and biochemical pathways in the development of nephrolithiasis.
Beyond biological mechanisms, sociodemographic and clinical characteristics may further influence the timing of disease onset. Although men have historically exhibited higher incidence rates, recent evidence indicates a rising burden among women, particularly in younger age groups.8 Factors such as socioeconomic status, occupational exposure, dietary habits, genetic predisposition, and regional climate have all been implicated in stone risk.2,6 Laboratory abnormalities, including hypercalciuria, hypocitraturia, hyperuricemia, and systemic inflammatory markers, have also been associated with increased risk.7,23 However, few studies have comprehensively integrated these sociodemographic, clinical, and laboratory variables with respect to disease-onset timing. Consequently, important questions remain unanswered: whether metabolic factors such as obesity accelerate the initial formation of stones, or whether specific biochemical profiles predispose individuals to earlier disease manifestation. Addressing these questions has important implications for risk stratification, early detection, and targeted prevention strategies.
\In Jordan and the broader Middle East, nephrolithiasis represents a significant and growing public health concern. The region is characterized by high baseline prevalence, driven by climatic conditions, fluid loss, and dietary patterns, alongside increasing rates of obesity, diabetes, and hypertension.3,14 Despite this, there is a relative lack of comprehensive studies examining the determinants of nephrolithiasis, particularly regarding disease onset. Jordan provides a unique setting in which environmental, cultural, and metabolic risk factors intersect, offering an opportunity to explore how these variables influence the timing of stone formation.
In addition to established metabolic and clinical risk factors, emerging evidence highlights the role of specific biochemical biomarkers that may influence kidney stone formation and vary with age. Osteopontin, a glycoprotein involved in crystal adhesion and renal tubular interactions, has been proposed as a potential biomarker associated with nephrolithiasis and may be influenced by nutritional status and body composition. Similarly, fetuin-A, a systemic inhibitor of pathological calcification, plays a critical role in mineral metabolism, and reduced serum and urinary levels have been associated with increased risk of kidney stone formation. These biomarkers underscore the complex interactions among metabolic, inflammatory, and biochemical pathways in nephrolithiasis and may contribute to differences in disease susceptibility and the timing of presentation.24,25
This study aims to investigate the factors associated with the age of onset of nephrolithiasis by integrating sociodemographic, clinical, and laboratory variables. We hypothesize that an earlier age of onset is not a random occurrence but reflects an underlying systemic metabolic phenotype, characterized by an increased burden of cardiometabolic comorbidities and specific biochemical abnormalities.
Methods
Study Design and Setting
This study is a retrospective–prospective cohort study, combining retrospective data collection from medical records with prospective acquisition of demographic and lifestyle information through structured patient interviews. Clinical and laboratory data were obtained retrospectively from hospital records, while demographic and lifestyle variables were collected prospectively at the time of patient contact. The study was conducted at two tertiary referral centers: The University of Jordan Hospital and Al-Balqa Applied University-affiliated hospitals, which provide comprehensive urological care to diverse populations from both urban and rural regions. Data were collected from January 2018 to December 2024.
Participants
The study included patients diagnosed with nephrolithiasis who had a documented age at first stone occurrence in their medical records.
Inclusion criteria were: (1) confirmed diagnosis of kidney stone disease based on clinical and/or radiological findings; (2) availability of a documented age of first stone episode; and (3) availability of key clinical and laboratory data relevant to the study variables.
Exclusion criteria were: (1) missing or unclear documentation of age at first stone occurrence; (2) incomplete clinical or laboratory data that could affect the analysis; and (3) patients with conditions that could significantly alter metabolic or renal parameters independent of nephrolithiasis (if applicable, otherwise remove this point). After applying these criteria, a total of 546 patients were included in the final analysis.
Variables
The primary outcome variable was the age of kidney stone onset, defined as the age at which participants first experienced a documented kidney stone episode. Age of onset was analyzed as both a continuous and a categorical variable, using predefined age groups. In additional analyses, age of onset was dichotomized into early onset (<45 years) and late onset (≥45 years).
Sociodemographic variables included sex, body mass index (BMI; categorised according to standard WHO thresholds), educational level, educational field, employment status, marital status, place of residence (urban vs rural), monthly household income, smoking status, physical activity, dietary habits (overall diet pattern, high animal protein intake, high sugar intake, and high salt intake), daily water intake, and whether a family member or spouse worked in the medical field.
Clinical variables comprised self-reported physician-diagnosed comorbidities, including hypertension, diabetes mellitus, cardiovascular disease, urinary tract infection, hyperparathyroidism, hypothyroidism, hyperthyroidism, chronic bowel inflammation, renal impairment, renal cell carcinoma, gout, and gallstone disease.
Laboratory variables included urinalysis parameters (bacteriuria, urine colour, crystalluria, proteinuria, urine culture results, red blood cells per high-power field [RBC/HPF], white blood cells per high-power field [WBC/HPF], urine pH, glycosuria, and urine transparency). Blood chemistry variables included serum creatinine, urea, uric acid, calcium, phosphorus, sodium, potassium, and albumin. Hormonal and metabolic markers included glycosylated hemoglobin (HbA1c), vitamin B12, parathyroid hormone (PTH), and thyroid-stimulating hormone (TSH). Laboratory variables were analysed as categorical variables, defined by standard clinical reference ranges, with a separate category for missing values where applicable.
All variables were selected a priori based on clinical relevance and prior literature on kidney stone disease and age-related metabolic risk.
Data Collection
Data was extracted from electronic medical records and patient charts using a standardized data collection form. Laboratory data were obtained as single-time-point measurements from patient records. For each variable, the most recent or clinically representative value closest to the time of kidney stone diagnosis was used. These measurements were not necessarily collected during an acute stone episode but were considered reflective of the patient’s general metabolic and biochemical status at the time of disease evaluation.
Statistical Analysis
Statistical analyses were performed using SPSS 30. Descriptive statistics were used to summarize study variables. Categorical variables are presented as frequencies and percentages, while continuous variables are presented as means ± standard deviation (SD) or medians with interquartile range (IQR), as appropriate.
The primary outcome was the age of kidney stone onset, analyzed both as a continuous variable and as a categorical variable stratified into predefined age groups. Associations between categorical variables and age-of-onset categories were assessed using the Chi-square (χ2) test.
Pearson correlation analysis was performed to evaluate bivariate associations between age of onset and selected sociodemographic, clinical, laboratory, and hormonal variables.
To identify factors independently associated with age of onset, a series of linear regression models was constructed with age of onset as the dependent variable. Separate models were fitted for:
Clinical comorbidities,
Urinalysis parameters, and
Blood chemistry and hormonal markers.
Variables were entered simultaneously within each model based on clinical relevance and prior univariable findings. Regression results are reported as unstandardized coefficients (B), standardized coefficients (β), t-statistics, and p-values. Overall model performance was evaluated using analysis of variance (ANOVA).
All statistical tests were two-tailed, and a p-value of <0.05 was considered statistically significant.
Results
Sociodemographic Characteristics
A total of 546 patients were included in the analysis, comprising 376 males (68.9%) and 170 females (31.1%). The distribution of sex did not differ significantly across age-of-onset categories (χ2 = 1.226, df = 3, p = 0.747) (Table 1). Overall, 312 participants (57.1%) experienced early-onset kidney stone disease (<44 years), whereas 234 participants (42.9%) had late-onset disease (≥45 years).
Table 1.
Sociodemographic Characteristics of Participants Stratified by Age of Onset of Kidney Stones
| Characteristic | Total (N=546) | Early-Onset (<44 yrs) N (%) |
Late-Onset (≥45 yrs) N (%) |
Chi-square (χ2) p-value |
|---|---|---|---|---|
| Gender | 0.747 | |||
| Male | 376 (68.9) | 219 (58.2) | 157 (41.8) | |
| Female | 170 (31.1) | 93 (54.7) | 77 (45.3) | |
| BMI | <0.001* | |||
| <18.5 | 10 (1.8) | 6 (60.0) | 4 (40.0) | |
| 18.5–24.9 | 151 (27.9) | 133 (88.1) | 18 (11.9) | |
| 25–29.9 | 184 (33.9) | 140 (76.1) | 44 (23.9) | |
| ≥30 | 197 (36.3) | 116 (58.9) | 81 (41.1) | |
| Educational Level | 0.088 | |||
| High school or less | 211 (38.6) | 136 (64.5) | 75 (35.5) | |
| Undergraduate | 184 (33.7) | 116 (63.0) | 68 (37.0) | |
| Graduate | 151 (27.7) | 124 (82.1) | 27 (17.9) | |
| Employment Status | <0.001* | |||
| Employed | 299 (54.8) | 260 (87.0) | 39 (13.0) | |
| Unemployed | 247 (45.2) | 116 (47.0) | 131 (53.0) | |
| Smoking Status | ||||
| Non-smoker | 293 (53.7) | 156 (53.2) | 137 (46.8) | <0.001* |
| Smoker | 253 (46.3) | 220 (87.0) | 33 (13.0) |
Notes: *Indicate significance, P <0.001; Statistical analysis performed using the Chi-square (χ2) test.
Body mass index (BMI) was significantly associated with age of onset (χ2 = 16.575, df = 3, p < 0.001). Higher BMI categories were more frequently observed among patients with an earlier onset of kidney stones. When stratified by age-of-onset groups, BMI remained significantly associated (χ2 = 36.936, df = 9, p < 0.001).
Functional status was strongly associated with age of onset (χ2 = 100.9, df = 1, p < 0.001), with employed individuals more commonly represented in earlier-onset categories. Educational level was also significantly associated with age of onset (χ2 = 17.194, df = 2, p < 0.001), although differences across detailed age-of-onset categories did not reach statistical significance (χ2 = 10.997, df = 6, p = 0.088).
Smoking status showed an influential association with age of onset (χ2 = 71.973, df = 1, p < 0.001), which remained significant when analysed across age-of-onset categories (χ2 = 22.694, df = 3, p < 0.001). Dietary factors, including high intake of animal protein (χ2 = 11.088, df = 3, p = 0.011), sugar (χ2 = 15.674, df = 3, p < 0.001), and salt (χ2 = 10.726, df = 3, p = 0.013), were also significantly associated with age of onset.
No significant associations were observed between age of onset and place of residence (urban vs. rural), total monthly income, exercise status, or educational field (all p > 0.05).
Clinical Characteristics
The distribution of clinical comorbidities across age-of-onset categories is summarized in Table 2. Hypertension was present in 216 participants (39.2%) and was strongly associated with age at kidney stone onset (χ2 = 54.63, df = 3, p < 0.001), with a higher prevalence among patients with later-onset kidney stones. Similarly, diabetes mellitus, reported in 163 participants (29.6%), was significantly associated with age of onset (χ2 = 63.564, df = 3, p < 0.001), with increasing frequency across older age categories. Cardiovascular conditions, including heart attack and arrhythmias, were identified in 79 participants (14.3%) and were also significantly associated with age of onset (χ2 = 22.942, df = 3, p < 0.001).
Table 2.
Clinical, Laboratory, and Hormonal Parameters of Participants, Stratified by Age at Kidney Stone Onset
| Variable | Total (N=546) | Early-Onset (<44 yrs) N (%) |
Late-Onset (≥45 yrs) N (%) |
Chi-square (χ2) p-value |
|---|---|---|---|---|
| Hypertension | 216 (39.2) | 83 (38.4) | 133 (61.6) | <0.001* |
| Diabetes | 163 (29.6) | 56 (34.4) | 107 (65.6) | <0.001* |
| Heart Disease | 79 (14.3) | 31 (39.2) | 48 (60.8) | <0.001* |
| Gallstones | 72 (13.1) | 27 (37.5) | 45 (62.5) | 0.003* |
| Serum Creatinine >1.2 mg/dL |
94 (17.1) | 41 (43.6) | 53 (56.4) | 0.012* |
| Serum Urea >46 mg/dL |
61 (11.1) | 26 (42.6) | 35 (57.4) | 0.012* |
| HbA1c level ≥6.2% |
76 (13.8) | 32 (42.1) | 44 (57.9) | 0.049* |
Notes: *Indicate significance, P <0.05; Statistical analysis performed using the Chi-square (χ2) test.
Gallstone disease was reported in 72 participants (13.1%) and was significantly associated with age at onset (χ2 = 14.255, df = 3, p = 0.003), with a higher prevalence in older age groups.
In contrast, no significant associations with age of onset were observed for urinary tract infections (56.1%; χ2 = 0.89, df = 3, p = 0.828), hyperparathyroidism (2.7%; χ2 = 1.905, df = 3, p = 0.592), hypothyroidism (3.1%; χ2 = 4.379, df = 3, p = 0.223), hyperthyroidism (1.6%; χ2 = 2.605, df = 3, p = 0.457), chronic bowel inflammation (29.9%; χ2 = 3.112, df = 3, p = 0.375), renal impairment (15.2%; χ2 = 3.217, df = 3, p = 0.359), or gout (12.9%; χ2 = 4.394, df = 3, p = 0.222). Variables showing no statistically significant associations were therefore omitted from the corresponding table.
Laboratory and Biochemical Characteristics
The distribution of laboratory, biochemical, and hormonal parameters across age-of-onset categories is summarized in Table 2.
Blood Chemistry and Renal Function
Serum creatinine levels were significantly associated with both sex (χ2 = 48.6, p < 0.001) and age of onset (χ2 = 21.1, p = 0.012). Serum urea was significantly associated with age of onset (χ2 = 21.0, p = 0.012), but not with sex. Serum uric acid levels differed significantly by sex (χ2 = 10.475, p = 0.015), whereas no association with age of onset was observed. Serum calcium, phosphorus, sodium, and potassium levels did not differ significantly across age-of-onset categories. Serum albumin showed a significant association with sex (χ2 = 9.637, p = 0.022), but not with age of onset.
Hormonal and Metabolic Markers
HbA1c categories were significantly associated with age of onset (χ2 = 12.63, p = 0.049), while no sex-based differences were observed. Vitamin B12 (χ2 = 16.963, p < 0.001) and parathyroid hormone levels (χ2 = 12.274, p = 0.007) differed significantly by sex but not by age of onset. Thyroid-stimulating hormone levels showed significant associations with both sex (χ2 = 31.291, p < 0.001) and age of onset (χ2 = 17.12, p = 0.047).
Urinalysis
Bacteriuria was significantly associated with sex (χ2 = 9.441, p = 0.002) but not with age of onset (p = 0.968). Urine culture results (χ2 = 14.994, p < 0.001) and urinary white blood cell counts (χ2 = 10.553, p = 0.014) differed significantly by sex, with no significant variation across age-of-onset categories. Other urinalysis parameters, including urine color, crystals, proteinuria, hematuria, urine pH, glycosuria, and transparency, were not significantly associated with age of onset.
Regression Analysis
Linear regression analyses were conducted to identify factors associated with age at kidney stone onset (Table 3). Age of onset was treated as a continuous dependent variable.
Table 3.
Summary of Regression Analyses Identifying Clinical, Urinalysis, and Biochemical Predictors of the Age of Onset of Kidney Stones
| Predictor | Model | β (Standardized) | t | p-value |
|---|---|---|---|---|
| Hypertension | Clinical comorbidities | −0.374 | −9.017 | <0.001* |
| Diabetes mellitus | Clinical comorbidities | −0.381 | −9.127 | <0.001* |
| Heart disease | Clinical comorbidities | −0.253 | −5.840 | <0.001* |
| Renal impairment | Clinical comorbidities | −0.110 | −2.470 | 0.014* |
| Gout | Clinical comorbidities | −0.120 | −2.700 | 0.007* |
| Gallstones | Clinical comorbidities | −0.148 | −3.336 | <0.001* |
| TSH | Blood chemistry/hormones | −0.114 | −1.962 | 0.050 |
| Serum urea | Blood chemistry/hormones | 0.161 | 3.100 | 0.002* |
| Serum calcium | Blood chemistry/hormones | 0.267 | 2.929 | 0.004* |
| Serum phosphorus | Blood chemistry/hormones | −0.208 | −2.499 | 0.013* |
| Urinary crystals | Urinalysis | 0.112 | 2.518 | 0.012* |
Notes: *Indicate significance, P <0.05; Statistical analysis performed using linear regression models.
An earlier age of onset was significantly associated with several clinical comorbidities. Significant inverse associations were observed for hypertension (β = −0.374, p < 0.001), diabetes mellitus (β = −0.381, p < 0.001), and heart disease (β = −0.253, p < 0.001). Additional significant associations were identified for renal impairment (β = −0.110, p = 0.014), gout (β = −0.120, p = 0.007), and gallstone disease (β = −0.148, p < 0.001), indicating that these conditions were more frequently observed among patients with earlier disease onset.
A multivariable regression model incorporating blood chemistry and hormonal markers was statistically significant (F = 2.746, p < 0.001). Within this model, serum urea (β = 0.161, p = 0.002) and serum calcium (β = 0.267, p = 0.004) were positively associated with age of onset, whereas serum phosphorus showed a significant inverse association (β = −0.208, p = 0.013). Thyroid-stimulating hormone (TSH) demonstrated a borderline association with age of onset (β = −0.114, p = 0.050). Other biochemical and hormonal variables were not independently associated with age of onset after adjustment.
Urinalysis parameters were evaluated in a separate regression model; however, the overall model was not statistically significant (p = 0.106), suggesting that routine urinalysis findings contribute only a limited amount to explaining variation in age of onset. Only the presence of urinary crystals was independently associated; no other urinalysis variables were significant.
Discussion
This study provides a comprehensive evaluation of factors associated with the age at onset of kidney stone disease, with a focus on the timing of stone formation. By integrating sociodemographic, clinical, laboratory, and hormonal data, our findings indicate that the timing of stone formation is not random but rather reflects a systemic metabolic phenotype that precedes the onset of overt stone disease.
A central observation of this study is the strong association between earlier age of onset and cardiometabolic comorbidities, including hypertension, diabetes mellitus, heart disease, gout, gallstones, and renal impairment. These associations remained robust in regression analyses, underscoring the concept that early stone disease may represent a renal manifestation of broader metabolic dysregulation rather than an isolated urological condition. This aligns with growing evidence linking nephrolithiasis to the metabolic syndrome and supports the hypothesis that stone formation may share upstream inflammatory, endocrine, and vascular pathways with chronic systemic diseases.26 A plausible mechanistic explanation for this association lies in shared metabolic and inflammatory pathways underlying both nephrolithiasis and cardiometabolic diseases. Insulin resistance, a central feature of metabolic syndrome, has been shown to impair renal ammoniagenesis, leading to lower urinary pH and increased risk of stone formation. In addition, chronic low-grade inflammation and oxidative stress contribute to endothelial dysfunction and altered renal tubular handling of calcium, uric acid, and phosphate. These processes may promote supersaturation of lithogenic substances and accelerate stone formation at an earlier age. Furthermore, hormonal and endocrine dysregulation, including abnormalities in parathyroid hormone and thyroid function, may influence calcium metabolism and renal excretion patterns. Collectively, these interconnected mechanisms support the concept that early-onset nephrolithiasis represents a renal manifestation of systemic metabolic dysfunction rather than an isolated urological condition.
Our findings are consistent with previous studies demonstrating a strong association between nephrolithiasis and cardiometabolic disorders. For instance, prior research has shown that hypertension and diabetes mellitus are significantly more prevalent among stone formers, supporting the concept of shared metabolic pathways linking these conditions.26 Similarly, large epidemiological studies have reported that patients with metabolic syndrome exhibit an increased risk of kidney stone formation, likely mediated through insulin resistance, altered urinary composition, and systemic inflammation. The present study extends these observations by demonstrating that these associations are particularly pronounced in patients with earlier disease onset, suggesting that metabolic dysregulation may not only increase risk but also accelerate the timing of stone formation.
Notably, obesity-related indicators and smoking status were significantly associated with earlier disease onset. These findings reinforce the role of modifiable lifestyle factors in accelerating stone initiation, potentially through mechanisms such as insulin resistance, altered renal acid handling, and oxidative stress.11 In contrast, variables traditionally emphasized in stone prevention, such as water intake, exercise, and geographic residence, did not independently predict age of onset.
Routine urinalysis parameters demonstrated limited explanatory power in regression modeling, with only urinary crystalluria showing an independent association with age of onset. This finding is clinically significant because it indicates that although urinalysis is helpful for initial assessment, it may not reflect the systemic biochemical environment that influences stone formation.27 Instead, blood-based markers provided deeper mechanistic insight.
Indeed, multivariable regression analysis identified serum calcium, urea, and phosphorus as significant predictors of age of onset, highlighting the importance of mineral metabolism and renal excretory load in disease timing. The inverse association with serum phosphorus and the positive association with serum calcium support established pathways of calcium-phosphate imbalance in stone pathogenesis.11 Furthermore, the borderline association with thyroid-stimulating hormone suggests a potential endocrine influence on stone initiation, warranting further exploration, particularly given emerging links between thyroid function and renal calcium handling.
Collectively, these findings suggest that early-onset kidney stone disease represents a high-risk metabolic phenotype, characterized by systemic comorbidities and biochemical abnormalities rather than isolated urinary derangements. From a clinical perspective, stratifying patients by age at onset may enhance early risk identification and facilitate preventive interventions targeting metabolic health, rather than focusing exclusively on preventing stone recurrence.
Significantly, this study contributes to the evolving conceptualization of nephrolithiasis as a chronic systemic disease with renal expression, rather than a purely urological disorder. Recognizing age of onset as a meaningful clinical marker may therefore refine patient counseling, guide metabolic screening, and inform personalized preventive strategies.
The observed associations between serum calcium, urea, and phosphorus and the timing of disease onset are also supported by existing literature. Previous studies have highlighted the role of calcium-phosphate imbalance and renal excretory load in stone pathogenesis.11 Elevated serum calcium has been consistently associated with increased urinary supersaturation, whereas abnormalities in phosphorus metabolism may reflect underlying disturbances in mineral homeostasis. Our findings align with these reports and further suggest that these biochemical alterations may influence not only the occurrence but also the timing of stone formation.
Strengths and Limitations
Strengths
This study benefits from a relatively large, well-characterized cohort with a comprehensive assessment of sociodemographic, clinical, laboratory, and hormonal variables. The focus on age at onset provides a novel perspective on kidney stone disease and enables the identification of early systemic risk factors. The use of regression analyses enabled adjustment for multiple covariates and strengthened the identification of independent associations. At the same time, the inclusion of both clinical comorbidities and biochemical markers offers mechanistic insight into stone pathophysiology.
Limitations
Several limitations of this study should be acknowledged. First, the retrospective–prospective design may introduce inherent biases related to data completeness and variability in clinical documentation. In addition, missing laboratory data for certain biochemical and hormonal parameters reduced the effective sample size for some analyses.
Second, several variables, particularly dietary and lifestyle factors, were based on patient self-report, which may introduce recall bias and potential misclassification. This is particularly relevant for variables such as daily water intake, which may not accurately reflect long-term hydration status and could partly explain the lack of a significant association observed.
Third, although key dietary factors such as animal protein, sugar, and salt intake were evaluated, a detailed assessment of dietary oxalate consumption was not performed. Given the established role of oxalate-rich foods—such as spinach, nuts, and tea—in calcium oxalate stone formation, the absence of this variable may have resulted in residual confounding in the observed associations between dietary patterns and disease onset.
Fourth, information regarding calcium intake, particularly the use of calcium supplements, was not collected. While serum calcium levels were analyzed, the distinction between dietary calcium and supplemental calcium was not assessed. This is important, as calcium supplementation—especially when taken independently of meals—has been associated with an increased risk of kidney stone formation and may influence the timing of disease presentation.
Finally, although a broad range of clinical and laboratory variables was included, certain emerging biochemical markers associated with nephrolithiasis—such as osteopontin and fetuin-A—were not evaluated. The absence of these biomarkers may limit the ability to fully characterize the underlying biological mechanisms and their relationship to disease onset. Future studies incorporating longitudinal designs, detailed dietary assessment, and advanced biomarker profiling are warranted to validate and extend these findings.
Conclusion
The timing of nephrolithiasis onset emerges as a clinically meaningful marker that reflects underlying systemic metabolic processes rather than a purely urological event. In this cohort, earlier disease manifestation was strongly associated with cardiometabolic comorbidities and specific biochemical alterations, supporting the concept that kidney stone disease may represent a renal expression of broader metabolic dysfunction.
These findings suggest that integrating the timing of disease presentation into clinical assessment may enhance risk stratification and support more targeted preventive strategies focused on metabolic health. However, given the observational nature of the study and limitations in data collection and the presence of unmeasured variables, the observed associations should be interpreted with caution.
Future prospective and mechanistic studies incorporating longitudinal follow-up and advanced biomarker profiling are needed to further elucidate causal pathways and validate the role of disease-onset timing in guiding personalized management of nephrolithiasis.
Future Directions
Future research should prioritize prospective, multicenter cohort studies to validate the identified associations between the age at onset of kidney stones and systemic metabolic factors. Incorporating longitudinal follow-up would allow assessment of temporal relationships and causal pathways linking early metabolic dysfunction to stone formation. Detailed dietary assessment, objective measures of fluid intake, and metabolic profiling (including urinary supersaturation indices) may further clarify modifiable risk factors. In addition, integration of genetic, epigenetic, and metabolomic analyses could help distinguish inherited predisposition from environmental influences in early-onset disease. Such approaches may ultimately support the development of personalized prevention strategies and earlier interventions for high-risk individuals.
Funding Statement
This research received no external funding.
Data Sharing Statement
The datasets generated and analyzed during the current study are not publicly available due to ethical and institutional restrictions on patient confidentiality. However, they are available from the corresponding author upon reasonable request, subject to approval by the relevant institutional review boards.
Ethical Approval
The study protocol was reviewed and approved by the Al-Balqa Applied University Institutional Review Board (IRB) (Approval No. 26/3/1/1766), issued on 11 September 2023, and by the Institutional Review Board of The University of Jordan (IRB-JUH) (Approval No. 10/2024/23361), convened on 29 September 2024.
All participants provided informed consent prior to inclusion in the study. Patients interviewed face-to-face signed a written informed consent form before participating, while participants interviewed by telephone provided verbal informed consent before answering any study-related questions. The study was conducted in accordance with the principles of the Declaration of Helsinki and applicable institutional and national ethical guidelines.
Disclosure
The authors declare no conflicts of interest regarding the publication of this study.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and analyzed during the current study are not publicly available due to ethical and institutional restrictions on patient confidentiality. However, they are available from the corresponding author upon reasonable request, subject to approval by the relevant institutional review boards.