Association of the uric acid-to-high-density lipoprotein cholesterol ratio with uric acid stones: a single-center retrospective cohort study

Introduction

Uric acid stones (UAS) are an important subtype of urolithiasis and are tightly linked to metabolic disorders. Evidence consistently indicates that UAS are more prevalent among stone formers with obesity, type 2 diabetes mellitus (DM), and/or metabolic syndrome, and that a persistently acidic urinary milieu (low urinary pH)—rather than uric acid load alone—is the key biochemical driver facilitating uric acid precipitation and crystallization (1,2). Insulin resistance-related defects in renal acid-base handling, including impaired ammoniagenesis, have been proposed as major contributors to low urinary pH in uric acid nephrolithiasis (2,3).

The serum uric acid-to-high-density lipoprotein cholesterol ratio (UHR) is an emerging composite metabolic marker that integrates uric acid burden with high-density lipoprotein cholesterol (HDL-C)-related lipid and anti-inflammatory/anti-oxidative capacity. Recent population studies have linked higher UHR to metabolic syndrome and insulin resistance (4,5), and it has also been investigated in metabolic liver disease such as nonalcoholic fatty liver disease (NAFLD) (6). In addition, higher UHR has been associated with adverse long-term outcomes, including all-cause and cardiovascular mortality, supporting its potential as a pragmatic risk marker (7). However, evidence relating UHR to kidney stone disease remains limited, and available studies have typically relied on self-reported stones without composition confirmation, which precludes a focused evaluation of UAS (8). This limitation is particularly relevant because UAS is highly dependent on urinary pH and diabetic status, and these factors may confound or modify the UHR-UAS relationship (9).

Accordingly, we conducted a single-center retrospective analysis of a hospitalized urolithiasis cohort. Using Fourier-transform infrared spectroscopy (FTIR) analysis to define UAS by the predominant component, we compared clinical and metabolism-related characteristics between patients with UAS and those with non-UAS. We aimed to investigate the association between UHR and UAS with multivariable adjustment including urine pH and DM, and with additional DM-stratified analyses, thereby providing evidence to inform metabolic assessment and targeted interventions in clinical practice. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0462/rc).

Methods

Study design and setting

This was a single-center retrospective cohort study involving hospitalized patients with urolithiasis treated at the Department of Urology, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine. Patients admitted between October 2023 and December 2025 were included. The study dataset comprised demographic and anthropometric characteristics, comorbidities, preoperative laboratory test results, and postoperative stone composition analysis data.

Study population

Inclusion criteria

Patients were eligible if they met all of the following criteria: (I) a confirmed diagnosis of urolithiasis, including renal, ureteral, bladder, or urethral stones; (II) hospitalization at our center during the study period and receipt of stone-related surgical treatment, including but not limited to ureteroscopy and/or flexible ureteroscopic lithotripsy, percutaneous nephrolithotomy, pyelolithotomy/ureterolithotomy, or transurethral cystolithotripsy; and (III) availability of postoperative stone specimens analyzed using FTIR, with complete documentation of stone composition, including the primary component.

Exclusion criteria

Patients were excluded if they met any of the following criteria: (I) missing data for the primary outcome (i.e., incomplete documentation of the primary stone component on compositional analysis); (II) multiple stones or stones located in more than one urinary tract site (e.g., multiple renal stones or concomitant renal and ureteral stones); (III) pregnancy or lactation; or (IV) age <18 years.

Outcome definition

The primary outcome was UAS. Stone composition was determined by FTIR. UAS were defined as stones in which a uric acid-related component was the predominant component. Uric acid-related components were predefined as anhydrous uric acid, uric acid dihydrate, ammonium acid urate, and sodium hydrogen urate monohydrate. Both pure UAS and mixed stones with uric acid predominance were classified as UAS. Stones with a non-uric acid-related predominant component were classified as non-UAS.

Exposure definition

The primary exposure was the UHR. Serum uric acid and HDL-C were converted to mg/dL before calculation: UA (mg/dL) = UA (µmol/L)/59.48, and HDL-C (mg/dL) = HDL-C (mmol/L) ×38.67. UHR was then calculated as UHR (%) = [UA (mg/dL)/HDL-C (mg/dL)] ×100. In the statistical analyses, UHR was modeled as a continuous variable (per 5-unit increase) for group comparisons and trend testing.

Covariates

Covariates were prespecified as clinical variables that may influence UAS formation and metabolic status. Demographic and anthropometric variables included age, sex, height, weight, and body mass index (BMI). Comorbidities included clinically diagnosed DM (type 2 DM in this study) and hypertension. Laboratory variables were obtained from the most recent preoperative tests. Urinary pH was obtained from the first routine automated urinalysis after presentation and before surgical intervention. Blood biochemical variables were measured using a preoperative morning fasting blood sample. These included serum uric acid, the lipid profile (total cholesterol, triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol), and renal function indices (creatinine and urea).

Stone composition analysis

Stone specimens were obtained during surgical management of urolithiasis. Postoperative stone composition was determined by FTIR using an infrared spectrometer (BRUKER TENSOR27, Germany). In brief, stones were rinsed with water and dried in an oven at 70–100 ℃. Approximately 1 mg of stone material was ground into powder and mixed with 200 mg of pre-dried potassium bromide (KBr). The mixture was further homogenized in an agate mortar and then pressed into a semi-transparent pellet. The pellet was placed in the spectrometer sample holder for scanning. Spectra were generated and automatically interpreted by the instrument software, and a stone composition report was produced.

Statistical analysis

Continuous variables were summarized according to their distributions. Normally distributed data are presented as mean ± standard deviation and were compared using the independent-samples t-test. Non-normally distributed data are presented as median (interquartile range) and were compared using the Mann-Whitney U test. Categorical variables are presented as number (percentage) and were compared using the Chi-squared test or Fisher’s exact test, as appropriate.

Patients were grouped as UAS or non-UAS. Binary logistic regression was used to evaluate the association between UHR and UAS, with odds ratios (ORs) and 95% confidence intervals (CIs) reported. UHR was analyzed as a continuous variable per 5-unit increase. The primary multivariable model included age, sex, BMI, DM, hypertension, urinary pH, and urea. Additional and sensitivity analyses included models using serum uric acid or HDL-C instead of UHR, analyses restricted to pure anhydrous UAS or upper urinary tract stones, ROC analyses, DM-stratified analyses, Firth penalized logistic regression, and formal UHR × DM interaction testing.

Patients with missing primary outcome data were excluded before analysis; for other variables, complete-case analysis was applied. Statistical analyses were performed using IBM SPSS Statistics version 26.0, and figures were generated using the Xiantao Academic Tools online platform (https://www.xiantaozi.com/products). All tests were two-sided, and P<0.05 was considered statistically significant.

Ethics statement

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine (approval No. LWEC2024024). Informed consent was waived in this retrospective study.

Results

Study population and analytic cohorts

A total of 485 inpatients with urolithiasis who underwent stone-related surgery at our center between October 2023 and December 2025 and had postoperative stone composition determined by FTIR were included. The patient selection and analytic workflow are shown in Figure 1. According to the predominant stone component, patients were classified into the UAS group (n=60) and the non-UAS group (n=425), and baseline characteristics were compared between groups (Table 1). Stone location distribution by UAS status is shown in Table S1. In the UAS group, stones were located in the kidney in 25 patients (41.7%), ureter in 17 (28.3%), and bladder in 18 (30.0%); no urethral UAS were observed.

Figure 1 Flow diagram of patient inclusion and analytic cohorts. A total of 485 inpatients with surgically treated urolithiasis and postoperative FTIR stone composition were included for baseline comparisons. For regression analyses involving the UHR, 23 patients with missing UHR-related data were excluded, leaving 462 patients for complete-case logistic regression and DM-stratified analyses. DM, diabetes mellitus; FTIR, Fourier-transform infrared spectroscopy; UAS, uric acid stones; UHR, serum uric acid to high-density lipoprotein cholesterol ratio.

For regression analyses, 23 patients were excluded due to missing data required to calculate the UHR, leaving 462 patients for complete-case analysis, including 58 with UAS and 404 with non-UAS. No significant baseline differences were observed between the complete-case cohort and patients with missing UHR-related data (Table S2). DM-stratified analyses were then performed, comprising 374 patients without DM (UAS events =41) and 88 patients with DM (UAS events =17), to evaluate subgroup-specific associations using multivariable logistic regression.

Baseline metabolic and clinical characteristics

As shown in Table 1, patients with UAS were older than those with non-UAS [68.00 (58.50–72.00) vs. 58.00 (48.00–66.00) years, P<0.001] and were more likely to be male (83.3% vs. 65.4%, P=0.009). The prevalence of hypertension (68.3% vs. 47.3%, P=0.004) and DM (30.0% vs. 16.9%, P=0.02) was also higher in the UAS group. Regarding metabolic-related parameters, the UAS group had a lower urinary pH [5.50 (5.00–5.80) vs. 6.00 (5.50–6.50), P<0.001], higher serum uric acid levels [420.50 (354.00–491.50) vs. 370.00 (303.00–438.00) µmol/L, P<0.001], and lower HDL-C [1.12 (0.95–1.25) vs. 1.21 (1.04–1.41) mmol/L, P=0.001], resulting in a higher UHR [15.70% (13.69–19.80%) vs. 13.23% (9.89–17.07%), P<0.001]. Urea levels were also higher in the UAS group [5.95 (4.95–7.55) vs. 5.38 (4.39–6.62) mmol/L, P=0.003]. No significant between-group differences were observed in BMI, total cholesterol, triglycerides, LDL-C, or creatinine (all P>0.05). Lipid profile variables (total cholesterol, triglycerides, HDL-C, and LDL-C) and UHR were available in 462 patients (non-UAS, n=404; UAS, n=58). Figure 2 further illustrates the between-group difference in UHR distribution, showing a right-shifted distribution and higher UHR levels in the UAS group (Mann-Whitney U test, P<0.001).

Figure 2 Distribution of UHR by UAS status. Violin plots show the distribution of UHR in patients with non-UAS (n=404) and UAS (n=58). Points represent individual patients. The central marker indicates the median and the horizontal bars indicate the IQR. Between-group comparisons were performed using the Mann-Whitney U test (P<0.001). IQR, interquartile range; UAS, uric acid stones; UHR, serum uric acid to high-density lipoprotein cholesterol ratio.

Association between UHR and UAS in logistic regression models

As shown in Table 2 and Figure 3, UHR was positively associated with the odds of UAS in the complete-case dataset (n=462; UAS, n=58; non-UAS, n=404). In univariable binary logistic regression, each 5-unit increase in UHR was associated with higher odds of UAS (OR, 1.78; 95% CI: 1.40–2.26; P<0.001). In the multivariable model, covariates were prespecified based on clinical relevance and informed by variables showing between-group differences in Table 1 and included age, sex, BMI, DM, hypertension, urinary pH, and urea. After adjustment, UHR remained independently associated with UAS (per 5-unit increase: OR, 1.64; 95% CI: 1.20–2.24; P=0.002). In additional models using the same covariates, serum uric acid was positively associated with UAS (per 100 µmol/L increase: adjusted OR, 1.64; 95% CI: 1.17–2.29; P=0.004), whereas HDL-C was inversely associated with UAS (per 0.1 mmol/L increase: adjusted OR, 0.86; 95% CI: 0.75–0.98; P=0.03) (Table S3).

Figure 3 Forest plot of multivariable logistic regression for UAS. Adjusted ORs and 95% CIs were estimated using binary logistic regression in the complete-case dataset (n=462). ORs are presented per 5-unit increase in UHR, per 1-year increase in age, per 1-unit increase in urinary pH, per 1 kg/m² increase in BMI, and per 1 mmol/L increase in urea. The vertical dashed line indicates OR =1. BMI, body mass index; CI, confidence interval; OR, odds ratio; UAS, uric acid stones; UHR, serum uric acid to high-density lipoprotein cholesterol ratio.

In addition to UHR, age and urinary pH were independently associated with UAS in the multivariable model. The odds of UAS increased with age (per 1-year increase: OR, 1.06; 95% CI: 1.03–1.10; P<0.001), whereas higher urinary pH was associated with lower odds of UAS (per 1-unit increase: OR, 0.15; 95% CI: 0.08–0.30; P<0.001). Sex, BMI, DM, hypertension, and urea were not statistically significant in the fully adjusted model (all P>0.05).

In a sensitivity analysis restricted to pure anhydrous UAS, UHR remained independently associated with UAS after multivariable adjustment (per 5-unit increase: OR, 1.78; 95% CI: 1.28–2.48; P<0.001) (Table S4). Similarly, in a sensitivity analysis restricted to upper urinary tract stones, UHR remained significantly associated with UAS (n=422; UAS events =42; per 5-unit increase: OR, 1.79; 95% CI: 1.26–2.52; P=0.001) (Table S1).

DM-stratified analyses

As shown in Table 3 and Figure 4, exploratory multivariable analyses adjusted for age and urinary pH showed that UHR remained associated with UAS in both the non-DM subgroup (n=374; UAS events =41; per 5-unit increase: OR, 1.74; 95% CI: 1.26–2.41; P<0.001) and the DM subgroup (n=88; UAS events =17; OR, 2.54; 95% CI: 1.37–4.71; P=0.003). Firth penalized logistic regression yielded consistent results in both subgroups (Table S5). The UHR × DM interaction was not statistically significant (P for interaction =0.62). Given the limited number of UAS events in the DM subgroup, these exploratory findings should be interpreted cautiously.

Figure 4 Stratified multivariable logistic regression for UAS by DM. Adjusted ORs and 95% CIs are shown for the non-DM and DM subgroups. Within each DM stratum, the multivariable model simultaneously included UHR (per 5-unit increase), age (per 1-year increase), and urinary pH (per 1-unit increase). The dashed vertical line indicates OR =1. CI, confidence interval; DM, diabetes mellitus; OR, odds ratio; UAS, uric acid stones; UHR, serum uric acid to high-density lipoprotein cholesterol ratio.

Discriminative performance of UHR and clinical models

As shown in Figure 5, urinary pH alone yielded an area under the curve (AUC) of 0.803, whereas UHR alone showed modest discrimination, with an AUC of 0.688. The conventional clinical model yielded an AUC of 0.851, which increased to 0.865 after the addition of UHR. The increase was not statistically significant (ΔAUC =0.014; paired DeLong P=0.10).

Figure 5 Receiver operating characteristic curves for UAS discrimination. (A) Urinary pH; (B) UHR; (C) conventional clinical model; and (D) conventional clinical model plus UHR. The conventional model included age, sex, BMI, DM, hypertension, urinary pH, and urea. Analyses included 462 patients with 58 UAS events. AUC, area under the curve; BMI, body mass index; CI, confidence interval; DM, diabetes mellitus; FPR, false positive rate; TPR, true positive rate; UAS, uric acid stones; UHR, serum uric acid to high-density lipoprotein cholesterol ratio.

Discussion

In this single-center inpatient surgical cohort, we characterized stone composition using FTIR and defined UAS according to the predominant component, thereby systematically evaluating the association between the UHR and UAS. Our principal findings are threefold. First, compared with non-UAS patients, those with UAS exhibited a more pronounced metabolic-risk profile, including older age, a higher proportion of men, higher prevalences of hypertension and DM, and a more acidic urinary environment (lower urinary pH). UAS patients also had higher serum uric acid and lower HDL-C, resulting in higher UHR levels. Second, in complete-case binary logistic regression, higher UHR was significantly associated with increased odds of UAS; this association remained robust after multivariable adjustment for potential confounders, suggesting that UHR is an independent correlate of UAS. Third, in DM-stratified multivariable analyses, the positive association between UHR and UAS was observed in both non-DM and DM subgroups, although the UHR × DM interaction was not statistically significant and the exploratory subgroup estimates should be interpreted cautiously because of the limited number of UAS events in the DM subgroup. The association also remained consistent when the analysis was restricted to pure anhydrous UAS.

From a mechanistic perspective, UAS formation is highly sensitive to urinary acid-base conditions. Uric acid has a pKa of approximately 5.35 at physiological temperature; when urine pH remains low, a larger fraction of uric acid exists in the non-ionized form, which has markedly reduced solubility and is more prone to precipitation and crystallization. Therefore, persistently acidic urine is considered a central biochemical driver of UAS formation, often outweighing uric acid load alone (10,11). In patients with metabolic syndrome or insulin resistance, impaired renal tubular acid handling, including reduced ammoniagenesis and altered ammonium transport, may decrease urinary buffering capacity and further lower urine pH. This provides a plausible biological link between metabolic dysfunction, low urinary pH, and UAS formation (11-13).

The potential relevance of the UHR to UAS likely stems from its integration of two complementary metabolic signals. In additional analyses, serum uric acid and HDL-C were also independently associated with UAS in separate adjusted models, while UHR showed a more pronounced statistical association. On the one hand, elevated serum uric acid commonly coexists with insulin resistance, oxidative stress, and chronic low-grade inflammation. On the other hand, HDL-C is not only a lipid-metabolic marker, but HDL particles also exert antioxidative and anti-inflammatory functions; in DM and obesity, quantity and functionality of HDL may be reduced or impaired (14,15). Therefore, when higher uric acid co-occurs with lower and/or dysfunctional HDL, a higher UHR may better capture an adverse metabolic-inflammatory milieu that is compatible with a greater propensity toward acidic urine and UAS. The persistence of this association after adjustment for urinary pH further suggests that UHR may reflect systemic metabolic, inflammatory, and oxidative stress-related pathways beyond urinary acidification alone. In recent studies, UHR has been associated with risks of metabolic-related diseases (e.g., NAFLD) and with long-term adverse outcomes (e.g., all-cause and cardiovascular mortality), supporting its potential value as a composite metabolic-risk marker (6,7). In our study, DM-stratified multivariable analyses showed that the positive association between UHR and UAS was present in both subgroups. Mechanistically, this pattern could be related to the higher prevalence of insulin resistance and renal tubular acid-handling abnormalities in DM, which may favor persistently acidic urine and thereby facilitate uric acid precipitation (11-13). However, this explanation remains to be validated in future studies with more detailed urinary chemistry profiling.

Evidence linking the UHR to kidney stone disease remains limited. One of the more representative analyses, based on NHANES 2007–2016, reported a positive association between higher UHR and a history of kidney stones (8). However, the outcome in that study was primarily derived from self-reported stone history, and stone composition was unavailable. Consequently, it was not possible to differentiate UAS—a subtype that is particularly sensitive to metabolic factors and urine pH—from other stone types, nor to provide composition-specific inference (8). In contrast, the key incremental contribution of the present study is the use of an inpatient surgical cohort with retrieved stone specimens and FTIR-based compositional assessment, enabling analyses with composition-confirmed UAS as the endpoint. Moreover, FTIR is widely used for stone analysis and provides reliable compositional information for clinical characterization (16). Together, our findings extend prior population-level observations by focusing specifically on composition-confirmed UAS, thereby offering more targeted clinical evidence for a link between UHR and this metabolically relevant stone subtype.

Although UHR was independently associated with UAS, its standalone discriminative performance was modest. Its potential value may therefore lie in providing complementary metabolic information rather than serving as an isolated diagnostic marker. Adding UHR to the conventional clinical model produced only a modest, non-significant improvement in discrimination. From a clinical perspective, UHR—calculated from routinely available serum uric acid and HDL-C—may serve as a low-cost metabolic signal rather than a diagnostic marker for UAS. It should not replace stone composition analysis or urine chemistry evaluation, which remain central to the assessment of stone former (3,17). However, an elevated UHR may prompt closer assessment of urinary pH, glycemic status, metabolic syndrome features, and recurrence prevention strategies in conjunction with stone composition results.

Several limitations should be acknowledged. First, this was a single-center retrospective study, and selection bias and residual confounding cannot be fully excluded. Second, the study population was restricted to hospitalized patients undergoing surgical stone treatment with available FTIR-based composition results; therefore, generalizability to conservatively managed outpatients or spontaneous stone passers should be made with caution. Excluding patients with multiple stones or stones at multiple urinary tract sites may have further introduced selection bias. Third, urinary pH was based on the first routine urinalysis after presentation rather than repeated or 24-hour measurements. We also lacked systematic data on diet, medication use, including urate-lowering, urinary alkalinizing, lipid-lowering, antidiabetic, and diuretic therapies, 24-hour urine chemistry, and long-term recurrence, and the findings have not been externally validated, which may have introduced residual confounding and limited mechanistic or prognostic interpretation. Fourth, the DM-stratified analyses were exploratory and may remain susceptible to model instability because of the limited number of UAS events, although Firth penalized regression yielded consistent results.

Future multicenter prospective studies with external validation, more complete urinary phenotyping (including dynamic urine pH monitoring where feasible), and longitudinal follow-up are warranted to further evaluate the incremental value of UHR beyond established factors such as urine pH and DM, and to better clarify the key pathophysiological pathways reflected by UHR.

Conclusions

In this single-center retrospective cohort of surgically treated stone formers with FTIR-confirmed stone composition, higher UHR was independently associated with UAS after adjustment for urinary pH and metabolic covariates. UHR may represent a readily available metabolic signal that prompts targeted metabolic evaluation, but its incremental value beyond established predictors such as urinary pH, diabetes status, and serum uric acid requires prospective multicenter validation.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0462/rc

Data Sharing Statement: Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0462/dss

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0462/prf

Funding: The study was supported by the Shanghai Huangpu District Scientific Research Project Fund (No. HLQ202401), and the Shanghai Huangpu District Medical Key Specialty Discipline Construction Project Fund (No. 2023ZDZK01).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0462/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. This study was approved by the Ethics Committee of Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine (approval No. LWEC2024024). Informed consent was waived in this retrospective study.

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/.

References

1. Maalouf NM. Metabolic syndrome and the genesis of uric acid stones. J Ren Nutr 2011;21:128-31. [Crossref] [PubMed]
2. Abate N, Chandalia M, Cabo-Chan AV Jr, et al. The metabolic syndrome and uric acid nephrolithiasis: novel features of renal manifestation of insulin resistance. Kidney Int 2004;65:386-92. [Crossref] [PubMed]
3. Pearle MS, Goldfarb DS, Assimos DG, et al. Medical management of kidney stones: AUA guideline. J Urol 2014;192:316-24. [Crossref] [PubMed]
4. Yu X, Sun F, Ming J, et al. Serum uric acid to high-density lipoprotein cholesterol ratio is a promising marker for identifying metabolic syndrome in nondiabetic Chinese men. Postgrad Med 2023;135:741-9. [Crossref] [PubMed]
5. Zhou X, Xu J. Association between serum uric acid-to-high-density lipoprotein cholesterol ratio and insulin resistance in an American population: A population-based analysis. J Diabetes Investig 2024;15:762-71. [Crossref] [PubMed]
6. Zhu W, Liang A, Shi P, et al. Higher serum uric acid to HDL-cholesterol ratio is associated with onset of non-alcoholic fatty liver disease in a non-obese Chinese population with normal blood lipid levels. BMC Gastroenterol 2022;22:196. [Crossref] [PubMed]
7. Li Z, Liu Q, Yao Z. The serum uric acid-to-high-density lipoprotein cholesterol ratio is a predictor for all-cause and cardiovascular disease mortality: a cross-sectional study. Front Endocrinol (Lausanne) 2024;15:1417485. [Crossref] [PubMed]
8. Yu J, Lu J, Xu Q, et al. Association of the serum uric acid-to-HDL-cholesterol ratio with kidney stones in U.S. adults: A cross-sectional study (NHANES 2007-2016). Medicine (Baltimore) 2025;104:e45376. [Crossref] [PubMed]
9. Shastri S, Patel J, Sambandam KK, et al. Kidney Stone Pathophysiology, Evaluation and Management: Core Curriculum 2023. Am J Kidney Dis 2023;82:617-34. [Crossref] [PubMed]
10. Adomako E, Moe OW. Uric Acid and Urate in Urolithiasis: The Innocent Bystander, Instigator, and Perpetrator. Semin Nephrol 2020;40:564-73. [Crossref] [PubMed]
11. Tamborino F, Cicchetti R, Mascitti M, et al. Pathophysiology and Main Molecular Mechanisms of Urinary Stone Formation and Recurrence. Int J Mol Sci 2024;25:3075. [Crossref] [PubMed]
12. Pricop C, Ivănuță M, Nikolic M, et al. Kidney Stones of Type I vs. Type II Diabetic Patients: Are There Any Differences? J Clin Med 2024;13:6110. [Crossref] [PubMed]
13. Shen Y, Zhu Z, Bi X, et al. Association between insulin resistance indices and kidney stones: results from the 2015-2018 National Health and Nutrition Examination Survey. Front Nutr 2024;11:1444049. [Crossref] [PubMed]
14. Denimal D. Antioxidant and Anti-Inflammatory Functions of High-Density Lipoprotein in Type 1 and Type 2 Diabetes. Antioxidants (Basel) 2023;13:57. [Crossref] [PubMed]
15. Denimal D, Monier S, Bouillet B, et al. High-Density Lipoprotein Alterations in Type 2 Diabetes and Obesity. Metabolites 2023;13:253. [Crossref] [PubMed]
16. Zhang D, Li S, Zhang Z, et al. Urinary stone composition analysis and clinical characterization of 1520 patients in central China. Sci Rep 2021;11:6467. [Crossref] [PubMed]
17. EAU Guidelines. Edn. presented at the EAU Annual Congress. Madrid; 2025.