ZJU index and prevalence of kidney stones in US adults: evidence for a threshold association from NHANES 2007–2018

Introduction

Kidney stones, resulting from abnormal crystallization and deposition of minerals within the renal system, represent the most prevalent form of urinary tract stone disease worldwide (1). In the United States, the lifetime prevalence is approximately 1 in 11 individuals, with reported rates of 19.1% in men and 9.4% in women by age 70 years (2). Without timely and effective management, kidney stones can lead to serious complications, including irreversible renal impairment and an elevated risk of advancing to end-stage renal disease (3). Nephrolithiasis is increasingly seen as a chronic systemic disorder that places a considerable challenge on healthcare delivery and public health management (4). Identifying modifiable risk factors and implementing screening and risk factor modification strategies are key to improving management and reducing long-term complications.

Urinary tract stone disease is now widely regarded as a systemic metabolic condition closely linked to metabolic disorders such as obesity and diabetes (2,4). Studies suggest that in many cases, the risk of kidney stone formation in obese individuals is largely mediated by obesity-related metabolic syndrome, which triggers multiple metabolic abnormalities that promote stone development (5). Obesity, characterized by lipid metabolism dysfunction and ectopic fat accumulation, alters urinary composition—affecting calcium, oxalate, and pH levels—and creates a pro-lithogenic environment (6). Individuals with higher body mass index (BMI) levels are at greater risk for developing nephrolithiasis. Weight loss through dietary and lifestyle modifications can effectively reduce BMI and lower stone risk (7). Diabetes mellitus is another key contributor to stone formation. Diabetic patients often present with insulin resistance (IR) and hyperinsulinemia, both of which impair renal acid excretion and favor the formation of uric acid stones (8). Given the strong connection between metabolic dysfunction and kidney stone formation, reliable composite indicators are needed to assess metabolic risk. Zhejiang University (ZJU) index represents a novel composite marker, incorporating BMI, blood glucose, triglycerides (TG), and liver function to reflect overall metabolic health (9). Compared to single biomarkers, the ZJU index offers a more comprehensive and sensitive evaluation of metabolic status. Previous research has demonstrated that the ZJU index is strongly associated with dyslipidemia, IR, and levels of obesity in nonalcoholic fatty liver disease (NAFLD) (10). Moreover, the ZJU index has also been significantly linked to a higher likelihood of sarcopenia and gallstone disease among individuals with glucose and lipid metabolism disorders (11,12). All these components are key metabolic drivers of nephrolithiasis, making the ZJU index a theoretically reasonable marker for assessing kidney stone risk by reflecting the overall metabolic status of the body.

Recently, a retrospective study has explored the association between the ZJU index and kidney stone prevalence in the Chinese adult population (13), laying a preliminary foundation for investigating the link between this metabolic index and nephrolithiasis. Although the ZJU index has been investigated in various metabolic diseases, its association with kidney stone formation has only been preliminarily explored in the Chinese population, and relevant evidence in Western populations remains lacking, particularly in large, population-based studies. As an integrated marker of metabolic health, the ZJU index may offer a novel approach to assessing the risk of nephrolithiasis. Compared to single biomarkers, the ZJU index offers a more comprehensive evaluation by integrating parameters that individually reflect distinct lithogenic pathways: obesity (BMI), IR and hyperglycemia [fasting plasma glucose (FPG)], dyslipidemia (TG), and hepatic dysfunction as a proxy for systemic inflammation and metabolic syndrome [alanine aminotransferase (ALT)/aspartate aminotransferase (AST) ratio]. Therefore, this study aims to (I) assess the association between the ZJU and kidney stone prevalence; (II) compare the predictive utility of the ZJU index with other commonly used metabolic markers; and (III) assess potential nonlinear and threshold effects. These findings could provide important perspectives on the early identification of individuals at risk of metabolism-related kidney stones. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-1-989/rc).

Methods

Study population

This research utilizes data from the 2007–2018 National Health and Nutrition Examination Survey (NHANES) surveys and employs a cross-sectional investigative approach. Data were collected through face-to-face interviews and comprehensive physical examinations, including assessments of physiological status and relevant laboratory markers. Initially, 59,842 individuals were enrolled in the study. The exclusion criteria included the following: (I) participants under the age of 20 years (n=25,072); (II) missing data of ZJU index (n=20,031); (III) missing data of kidney stones (n=38); (IV) lack of key covariates information (n=738). Ultimately, 13,963 participants were involved, and all participants fulfilled the requirements for complete conditions, as shown in Figure 1. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Figure 1 The participant flow diagram. ZJU, Zhejiang University.

Assessment of ZJU

The ZJU index was calculated as follows (9):

Male:

ZJU=FPG(FastingPlasmaGlucose)(mmol/L)+BMI(kg/m2)+TG(triglycerides)(mmol/L)+3×ALT(alanineaminotransferase)(U/L)AST(aspartateaminotransferase)(U/L)[1]

Female:

ZJU=FPG(FastingPlasmaGlucose)(mmol/L)+BMI(kg/m2)+TG(triglycerides)(mmol/L)+3×ALT(alanineaminotransferase)(U/L)AST(aspartateaminotransferase)(U/L)+2[2]

NHHR=TC−HDLHDL[3]

NHHR, non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio.

TyG(triglyceride-glucose)=ln(TG(mg/dL)∗FPG(mg/dL)2)[4]

Assessment of kidney stones

NHANES evaluates the epidemiology of kidney stones through self-reported survey data. Data were gathered from the Kidney Conditions Urology section of the NHANES questionnaire (KIQ026). The questionnaire included the question, “Have you ever had kidney stones?” Participants responding “yes” were classified as having had kidney stones. This dataset reflects the lifetime prevalence of kidney stones, showing whether participants had a prior diagnosis rather than tracking new occurrences during the study.

Covariates

Multiple potential confounders were included as covariates to improve the model’s robustness. The main variables evaluated were age, gender, race/ethnicity, education level, family income-to-poverty ratio (PIR), marital status, smoking status, alcohol use, recreational activity, and BMI. Disease-related data included hypertension, diabetes, gout, coronary heart disease, and stroke. Laboratory markers included fasting Glucose, TG, estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), albumin, uric acid, serum total calcium, creatinine, AST, ALT, total cholesterol, high-density lipoprotein (HDL), and low-density lipoprotein (LDL). All data for the variables included in this study were obtained from NHANES, providing a consistent and comprehensive foundation.

Statistical analysis

The complex survey design of NHANES necessitated specialized analytical techniques accounting for sampling weights and clustering effects. Descriptive statistics for continuous measures were calculated as weighted means ± standard deviation (SD), with categorical variables reported as weighted percentages. The association between ZJU and kidney stone formation was examined through stratified regression models: an unadjusted base model (Model I), a demographic-adjusted model (Model II: sex, age, race/ethnicity), and a fully adjusted model (Model III: all covariates). Given the considerable variability of the ZJU index in clinical practice, we expressed the effect size per 10-unit increase in the ZJU index in the regression model. This approach was adopted to reduce the risk of numerical instability and enhance the interpretability of the effect size without altering the underlying statistical inference.

Additionally, subgroup analyses and interaction tests were performed to investigate subgroups’ heterogeneity and verify the findings’ robustness. A segmented regression model was applied to explore threshold effects, while nonlinear regression analysis was used to elucidate potential nonlinear relationships. Finally, receiver operating characteristic (ROC) analysis was applied to determine the diagnostic value of ZJU, triglyceride-glucose (TyG), BMI, and non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) in predicting kidney stones. Statistical computations leveraged the survey package in R software (v4.4.2) and EmpowerStats platform (v2.0), adopting a conventional 5% significance level.

Results

Characteristics of the participants

This study comprised 13,963 participants, whose mean age was 50.17±17.59 years. Participants were categorized based on their kidney stone history, with Table 1 displaying the specific baseline characteristics for each group. Among the participants, 1,284 self-reported a history of kidney stones. Participants with kidney stones were older, on average, predominantly non-Hispanic White, above high school, and were more frequently married or living with a partner. This group also exhibited higher levels of BMI, TyG, NHHR, fasting glucose, TG, and LDL cholesterol and had higher incidences of gout, hypertension, diabetes (19.08% of stone formers), and cardiovascular diseases, a clinical feature that is closely associated with the pathogenesis of uric acid kidney stones in clinical practice.

Association between ZJU and kidney stones

Multivariable weighted logistic regression analyses were conducted to explore the associations between ZJU and kidney stones, with results shown in Table 2. According to the model results, ZJU is consistently and significantly positively associated with kidney stones, whether represented as a continuous or categorical variable. In the unadjusted Model 1, an increase in ZJU [odds ratio (OR) =1.48, 95% confidential interval (CI): 1.38–1.59; P<0.001] was primarily associated with a higher incidence of kidney stones. This suggests that for each unit increment in ZJU, the likelihood of kidney stone development is increased by 48%. With the gradual adjustment of covariates, statistical significance was still maintained in Model 3 (OR =1.53, 95% CI: 1.38–1.70; P<0.001). Additional analysis showed that individuals in the highest total ZJU quartile (Q4) exhibited a significantly increased risk of developing kidney stones (OR =2.17; 95% CI: 1.74–2.70; P<0.001). The risk of kidney stones in the highest quartile of total ZJU (Q4) was 117% higher than in the lowest quartile (Q1). In all three models, the P values were consistently <0.01, indicating a significant trend across all models, with increasing ZJU positively linked to the likelihood of developing kidney stones.

Subgroup analysis

Subgroup analyses and interaction tests were performed to determine whether the relationship between ZJU and kidney stones remained consistent across various populations. The results, shown in Figure 2, indicate that the strongest correlation was observed in patients with a BMI >30 kg/m² (OR =2.21, 95% CI: 1.19–4.12). A strong correlation was also found in individuals with hypertension (OR =1.71, 95% CI: 1.51–1.94) and other races (OR =2.05, 95% CI: 1.53–2.76). Notably, significant interactions were observed in the subgroup analysis based on gender (interaction P<0.05), suggesting that these factors may influence the link between ZJU and kidney stone risk, highlighting the complexity of these relationships. In the stratified analysis, except for the aforementioned gender factors, the relationship between ZJU and kidney stone risk remained stable, with no significant interactions observed (P>0.05), suggesting that these factors do not affect the positive correlation between ZJU and kidney stones.

Figure 2 Subgroup analysis of the association between ZJU index and kidney stones. BMI, body mass index; CI, confidential interval; GED, General Educational Development; OR, odds ratio; PIR, income-to-poverty ratio; ZJU, Zhejiang University.

Analysis of the nonlinear and saturation effects of ZJU on kidney stones

The nonlinear regression analysis revealed a nonlinear relationship between ZJU and kidney stones, as presented in Figure 3. To gain further insight into this relationship, a threshold effect assessment was conducted through a piecewise regression model (Table 3). The grid search method was applied iteratively to compute the log-likelihood ratio and determine the inflection point corresponding to the maximum likelihood value. The stability of the results was validated through 1,000 bootstrap resamples. The log-likelihood ratio test (P=0.002) revealed a marked difference between the two segmented linear models. The relationship between ZJU and kidney stones showed a turning point at 44.15. Below this threshold, the OR was 1.90 (95% CI: 1.61–2.25; P<0.001), suggesting a significant positive correlation. However, above this threshold, the OR was 1.25 (95% CI: 1.05–1.48; P=0.01), indicating that the impact of ZJU on kidney stone risk has weakened, but it still shows a strong correlation. This finding supports the validity of the nonlinear regression model used in the primary analysis. It suggests that the effect of ZJU on kidney stone risk remains consistent and predictable within the observed ZJU range.

Figure 3 The nonlinear associations between the ZJU and kidney stones. ZJU, Zhejiang University.

Interestingly, based on the results of the subgroup analysis, further nonlinear regression analysis was conducted on the gender subgroups, as shown in Figure 4. Compared to women, men exhibited a more rapid increase in kidney stone risk with the rise in ZJU. Throughout the increase in ZJU, the incidence of kidney stones in men remained higher than in women. This differential association suggests that the relationship between metabolic factors and stone risk may vary by sex, highlighting an area for future research to explore the underlying biological mechanisms.

Figure 4 The association between ZJU index and kidney stones by sex. ZJU, Zhejiang University.

Predictive value of ZJU for the kidney stone

ROC analysis was implemented to estimate whether ZJU outperforms BMI, TyG, and NHHR in assessing kidney stone risk (Figure 5). Compared to the other indices, ZJU showed a stronger association with kidney stone risk [area under curve (AUC): ZJU =0.734; TyG =0.604; BMI =0.555; NHHR =0.501]. The AUC values for all four indices were more significant than 0.5, indicating that they have some diagnostic predictive value for kidney stone occurrence. Notably, the ZJU index demonstrated a numerically higher AUC compared to BMI, TyG, and NHHR. While this suggests that the ZJU index may capture a broader aspect of metabolic risk, its discriminative ability remains modest, indicating it is not a standalone diagnostic tool but could potentially contribute to multi-factorial risk stratification models.

Figure 5 ROC curve analysis compares the predictive power between ZJU and BMI, TyG, and NHHR for kidney stones. AUC, area under curve; BMI, body mass index; NHHR, non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio; ROC, receiver operating characteristic; TyG, triglyceride-glucose; ZJU, Zhejiang University.

Discussion

This research utilized NHANES data from 2007 to 2018, revealing an independent link between the ZJU index and a higher incidence of kidney stones. We hypothesize that the ZJU index could serve as a predictive factor for kidney stone risk in the US adult population. According to the findings from the adjusted model, a notable positive connection between ZJU and the probability of kidney stones was found across three models with continuous and categorical variables. Furthermore, the robustness and reliability of our conclusions were reinforced through nonlinear regression and stratified analyses, demonstrating methodological rigor. Subgroup analyses suggest that sex may mediate the link between ZJU and the risk of kidney stones. Finally, ROC curve analysis demonstrated that, compared to BMI, TyG, and NHHR, ZJU outperformed these metrics in assessing kidney stone risk. These results have significant implications for the risk stratification and clinical assessment of kidney stones.

Obesity, as a hallmark of metabolic dysfunction, has been consistently associated with an increased risk of kidney stone formation. Duffey et al. demonstrated that higher BMI correlates with elevated urinary excretion of lithogenic substances, thereby facilitating stone development (14). Excess adiposity may impair renal function through structural and hemodynamic alterations and aggravate comorbid conditions such as hypertension and diabetes (15,16). Furthermore, obesity is recognized as a major modifiable risk factor for stone recurrence, underscoring the importance of weight management as a preventative measure (17). In parallel, lipid metabolism disorders—frequently observed in obese individuals—are closely linked to IR and elevated levels of serum TG and cholesterol, all contributing to alterations in urinary composition that favor lithogenesis (18-21). Prior studies have indicated that increased TG and cholesterol levels may enhance the urinary excretion of calcium, oxalate, and sodium, thereby elevating the risk of kidney stone formation (22,23). Mechanistically, this process may be driven by the interplay between endoplasmic reticulum (ER) stress, oxidative stress, and inflammation, collectively regulated through elevated lipid levels. Dysregulated lipids may activate signaling pathways such as NF-κB and MAPK, intensifying inflammatory responses and disrupting metabolic homeostasis, thus creating a pro-lithogenic environment (24). Additionally, hyperlipidemia has been shown to impair renal tubular epithelial cell survival and repair by modulating the expression of apoptosis-related proteins, including members of the heat shock protein family. This apoptotic activity may contribute to tubular injury and facilitate the nucleation and aggregation of crystals (25).

Beyond lipid abnormalities, obesity is known to induce a state of chronic low-grade inflammation, which disrupts adipocyte function and facilitates the infiltration of immune cells into insulin-sensitive tissues, ultimately contributing to the development of IR (26). This inflammatory cascade is now widely recognized as a central mechanism driving IR in obese individuals and has a critical role in the pathogenesis of type 2 diabetes mellitus (T2DM). T2DM, a key component of metabolic syndrome, has been independently associated with an increased risk of both incident and recurrent kidney stones (27). Inadequate glycemic control in diabetic patients may result in renal tubular dysfunction, diminished mineral reabsorption, and increased urinary excretion of lithogenic substances such as oxalate and uric acid (28,29). Moreover, IR—a hallmark of T2DM—has been implicated in impaired urinary acidification and disturbed calcium-phosphate homeostasis, promoting stone formation (30). Specifically, IR reduces renal ammonium excretion and citrate reabsorption, leading to elevated urinary uric acid levels and decreased urinary citrate concentrations. These changes significantly heighten the risk of uric acid and calcium stone formation (31), though the differential associations between metabolic disturbances such as IR and various kidney stone phenotypes cannot be further explored in this study due to the lack of phenotypic classification data. Therefore, comprehensive risk assessment and optimal diabetes management remain important components of overall health in these patients, given the established association between poorly controlled diabetes and an increased likelihood of stone formation.

Emerging evidence has highlighted the relevance of various metabolic indicators—such as the TyG index and the non-high-density lipoprotein to high-density lipoprotein cholesterol ratio (NHHR)—in kidney stone formation. The TyG index, widely used as a surrogate marker for IR, has enhanced predictive accuracy for nephrolithiasis when incorporated into clinical models with related metabolic indicators (32,33). Mechanistically, this association may stem from the TyG index’s ability to reflect IR and hyperglycemia, which contribute to renal tubular dysfunction and altered urinary mineral excretion, promoting crystal adhesion and stone development (33). Similarly, the NHHR—a lipid metabolism-based marker—has demonstrated a significant association with an elevated risk of kidney stones, particularly among individuals with dyslipidemia. As an indicator of cholesterol homeostasis, NHHR may influence lithogenesis through mechanisms involving lipid-driven alterations in renal hemodynamics and vascular calcification (34).

However, different from the TyG index, which is primarily a surrogate for IR, or the NHHR, which reflects cholesterol homeostasis, the ZJU index may offer a more integrative assessment of the metabolic milieu by simultaneously capturing information on adiposity, glycemic control, lipid metabolism, and liver function. While the improvement in AUC was modest, this integration suggests the ZJU could be a more holistic, albeit still imperfect, marker of the complex metabolic dysregulation that underlies stone pathogenesis.

Originally developed to assess NAFLD, the ZJU index comprehensively reflects an individual’s metabolic status, particularly obesity and related metabolic dysfunctions (10). This study underscores the potential utility of the ZJU index as a practical and informative biomarker for assessing the likelihood of kidney stone development. Notably, the individual components of the ZJU index are strongly associated with metabolic abnormalities implicated in the pathogenesis of nephrolithiasis. Given the evidence supporting a positive association between NAFLD and kidney stone formation (35) and considering the common metabolic disturbances underlying both conditions, applying the ZJU index in this context appears highly relevant. Both NAFLD and kidney stones share several overlapping risk factors, such as obesity, type 2 diabetes, and dyslipidemia (36). Obesity and IR, in particular, play a central role in the early accumulation of lipids—especially fatty acids and TG—within hepatocytes, thereby linking hepatic steatosis to systemic metabolic dysregulation (37).

Furthermore, experimental studies have demonstrated that patients with NAFLD exhibit elevated levels of reactive oxygen species and products of lipid peroxidation. Emerging evidence further suggests that mitochondrial reactive oxygen species (ROS)-mediated ferroptosis, regulated via the NRF2 pathway, plays a key role in NAFLD progression (38,39). Under oxidative stress, compounds such as bilirubin may react with free radicals to generate oxidized bilirubin polymers, which act as nucleation sites and enhance stone crystallization and deposition (40,41). This pro-inflammatory and oxidative milieu may contribute to systemic metabolic disturbances, including those potentially relevant to renal tubular injury and stone formation, although direct evidence linking these pathways to nephrolithiasis remains to be established.

Notably, the NHANES database used in this study does not contain detailed classification information on kidney stone phenotypes, such as uric acid stones, calcium oxalate stones (including those growing on Randall’s plaque), apatite stones, and brushite stones, which prevents us from analyzing the association between the ZJU index and different kidney stone subtypes separately. Given that 19.08% of stone formers in this study had diabetes—a key risk factor for uric acid stone formation—the significant correlation observed between the ZJU index and kidney stone prevalence in this study is most likely to be concentrated in uric acid stones. In addition, the association between the ZJU index and other common kidney stone phenotypes has not been verified in this study and remains unclear. Consequently, our findings may not be generalizable to non-metabolic stone phenotypes, such as those caused by infection, genetic disorders, or anatomical abnormalities. Future studies incorporating stone composition analysis are essential to determine the specific lithogenic pathways associated with an elevated ZJU index.

In summary, although the ZJU index has recently been linked to kidney stones in a Chinese cohort (13), its association with nephrolithiasis in a representative US adult population has not been characterized. By leveraging the NHANES database, our study not only corroborates this relationship in a distinct ethnic and geographic cohort but also reveals a nonlinear threshold effect. The metabolic components represented by this index—particularly obesity, dyslipidemia, and IR—are established risk factors for urolithiasis. The present study’s findings provide novel evidence supporting the utility of the ZJU index as a metabolic indicator for kidney stone risk assessment in the US population. Accordingly, optimal levels of blood glucose, lipid profiles, and body weight, alongside a metabolically healthy lifestyle, are associated with favorable metabolic status, which correlates with a lower likelihood of kidney stone development in this population. Further longitudinal and mechanistic studies are warranted to elucidate the clinical utility and predictive value of the ZJU index in nephrolithiasis.

Several strengths are present in this study. First, with a large sample size and comprehensive statistical approaches, this study represents the first exploration of the relationship between ZJU and kidney stones, deepening our understanding of the connection between metabolic health and the risk of kidney stones. Second, the results’ consistency was verified through stratified analysis across participant subgroups. Additionally, the study adjusted for many important confounding factors to strengthen the reliability of the results.

However, several inherent limitations should be recognized. First, as a cross-sectional study, this research cannot establish a causal relationship between the ZJU index and kidney stone prevalence. It is possible that both the components of the ZJU index and the propensity for kidney stone formation are jointly driven by unmeasured genetic factors, and thus modifying metabolic indicators related to the ZJU index may not directly alter the risk of kidney stone development. Future longitudinal and interventional studies are needed to verify the potential causal associations and the clinical utility of monitoring ZJU-related indicators. Second, apart from laboratory-based biochemical measurements, kidney stone history was self-reported, which introduces the potential for recall bias. More critically, NHANES does not include systematic imaging [e.g., ultrasound or computed tomography (CT)] to screen for asymptomatic kidney stones. As a result, our study captures only symptomatic stone events and likely underestimates the true prevalence of nephrolithiasis. The association we observed is therefore with a history of symptomatic stones, and its generalizability to the broader population of all stone formers, including those with asymptomatic disease, remains unclear. Additionally, the discriminative ability of the ZJU index for kidney stone risk was only moderate (AUC =0.734), indicating that it is insufficient for standalone clinical diagnosis or prediction. Nephrolithiasis is a complex, multifactorial process, and despite extensive adjustment for potential confounders, residual confounding from unmeasured variables cannot be fully excluded. Key factors such as detailed dietary intake (e.g., fluid consumption, oxalate, sodium, and animal protein), medication use (e.g., thiazide diuretics, allopurinol, vitamin D and calcium supplements), and a family history of kidney stones are not captured in this dataset but could significantly influence both metabolic status and stone risk. Therefore, the ZJU index should be considered an auxiliary metabolic marker for kidney stone risk stratification, and its clinical application should be integrated with other clinical information rather than used in isolation. Finally, since this study primarily consists of US participants, further research is required to assess the applicability of these findings to diverse population cohorts.

Conclusions

This study reveals a strong and significant positive association between the ZJU index and kidney stone prevalence in US adults, and the ZJU index exhibits superior discriminative ability for metabolic risk stratification compared to conventional single or partial metabolic indicators (BMI, TyG, NHHR). Notably, the ZJU index has only fair standalone predictive power, and its clinical application should be combined with other clinical factors (e.g., dietary history, urinary stone risk assessment). Integrating the ZJU index into population-based metabolic risk screening could support the early identification of adults at high metabolic risk of kidney stones, providing a reference for clinical risk stratification and comprehensive management of nephrolithiasis.

Acknowledgments

We would like to thank the NHANES database for granting access to these valuable data.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-1-989/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-1-989/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-2025-1-989/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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