Bidirectional Regulation of Urinary Stone Composition by Hypothyroidism: A Propensity Score-Matched Analysis of 33,579 Patients

Bidirectional Regulation of Urinary Stone Composition by Hypothyroidism: A Propensity Score-Matched Analysis of 33,579 Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bidirectional Regulation of Urinary Stone Composition by Hypothyroidism: A Propensity Score-Matched Analysis of 33,579 Patients Mengting Wang, Zhenglin Chang, Yiping Lai, Haojie Wu, Ying Liang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7784983/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2026 Read the published version in World Journal of Urology → Version 1 posted 10 You are reading this latest preprint version Abstract Background Urolithiasis affects 14.8% of the global population, with its pathogenesis involving multiple systemic factors. Among patients with thyroid disorders, which affect 5–10% of the population, the risk of stone formation and compositional characteristics may exhibit specific alterations; however, the mechanisms by which different thyroid functional states influence stone composition remain unclear. Methods A retrospective analysis was conducted on 33,579 urinary stone composition data collected from 2014 to 2024 in South China. Propensity score matching (PSM) was employed to evaluate the distribution characteristics of stone composition across different thyroid functional states, establishing three 1:1 matched cohorts: hyperthyroidism group (n = 298), hypothyroidism group (n = 140), and hyperparathyroidism group (n = 82). Multivariable logistic regression, generalized linear models, and interaction analyses were performed to assess the associations between stone composition and thyroid disorders, controlling for confounding factors including age, sex, season, and stone location. Results The urinary stone composition analysis in this study revealed specific effects of different thyroid disorders. Patients with hyperthyroidism showed significantly higher proportions of calcium oxalate dihydrate (COD) stones compared to controls (15.1% vs 7.5%, p = 0.016); patients with hypothyroidism exhibited increased proportions of carbonate apatite (CA) stones (85.2% vs 64.3%, p = 0.043). Multivariable regression confirmed hypothyroidism as an independent risk factor for CA stones (OR > 1.0), while demonstrating a protective effect against calcium oxalate monohydrate (COM) stones (OR < 1.0). Interaction analyses revealed sex-based differences in COM stones among hyperthyroid patients (higher predicted probability in males), and seasonal variations in stone composition among hypothyroid patients. Age-stratified analysis identified increasing magnesium ammonium phosphate stones with age in hyperthyroid patients, with CA stones exhibiting the strongest age dependency. Conclusion Urinary stone composition is specifically regulated by thyroid functional states. Hyperthyroidism is associated with increased COD stones, while hypothyroidism independently promotes CA stone formation but inhibits COM stones, suggesting that endocrine factors participate in the formation of different stone types through regulation of calcium-phosphate metabolism, providing important evidence for individualized prevention strategies based on stone composition. Urolithiasis Stone composition Hyperthyroidism (HT) Hypothyroidism (HT-) Hyperparathyroidism (HPT) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights 1. First application of PSM to analyze 33,579 urolithiasis patients, revealing specific effects of thyroid diseases on stone composition 2. HT- presents unique bidirectional effects: promoting CA formation while inhibiting COM formation 3. Female patients with HT- demonstrate the highest stone risk and exhibit significant age-sex-season interaction effects 4. HT selectively promotes COD formation Introduction Urolithiasis is a relatively typical urological disease, including kidney stones, ureteral stones, bladder or urethral stones, which is affecting up to 14.8% of the global population and continues to increase, with recurrence rates as high as 50% within the first 5 years after initial stone episode[ 1 – 4 ]. Stone formation is a complex multifactorial process involving genetics, diet, metabolic abnormalities, and endocrine diseases[ 5 ]. Among endocrine factors, the relationship between hyperparathyroidism (HPT) and stones has been widely recognized, with kidney stone incidence in primary hyperparathyroidism (PHPT) patients reaching 20–30%, primarily promoting calcium-containing stone formation through hypercalcemia and hypercalciuria[ 6 , 7 ]. Additionally, hypomagnesemia and urinary calcium/magnesium ratio in asymptomatic PHPT are also considered associated with kidney stones[ 8 , 9 ]. However, research on the relationship between thyroid disorders, which affect 5–10% of the population, and urinary stone composition remains extremely limited[ 10 , 11 ]. In recent years, increasing evidence indicates that the endocrine system plays an important regulatory role in urinary stone formation, with the relationship between thyroid functional status and stone formation gradually gaining attention[ 7 , 8 , 12 , 13 ]. Theoretically, thyroid hormones have broad regulatory effects on calcium-phosphate metabolism, renal function, and urinary acid-base balance, all of which are closely associated with stone formation[ 14 ]. Thyroid dysfunction may affect stone formation through mechanisms including influencing glomerular filtration rate, altering urinary composition, and regulating bone metabolism; however, these hypotheses lack validation from large-scale clinical studies. Meanwhile, existing research has obvious limitations: primarily focusing on parathyroid while neglecting thyroid disorders; studying only incidence rates without analyzing stone composition; lacking rigorous control of confounding factors; and failing to explore the modulatory effects of factors such as age, sex, and season[ 15 ]. Urolithiasis is a heterogeneous disease, with different compositions reflecting different pathological processes[ 16 , 17 ]. Calcium-containing stones (accounting for over 80%) are associated with calcium-phosphate metabolic disorders; infection stones indicate urinary tract infections and alkaline urine environment; uric acid stones reflect purine metabolic abnormalities[ 1 , 18 , 19 ]. Considering the metabolic regulatory effects of thyroid hormones, different thyroid functional states may selectively influence specific stone types, but the specific mechanisms remain unclear. To address this knowledge gap, this study, based on 33,579 urinary stone composition data, systematically evaluates for the first time the effects of hyperthyroidism (HT), hypothyroidism (HT-) and HPT on stone composition. Propensity score matching (PSM) was employed to control confounding factors, multivariable logistic regression was used to assess independent effects, and an age-sex-season multidimensional interaction model was established. To our knowledge, this is the first large-scale study exploring the relationship between thyroid disorders and stone composition, potentially providing pioneering evidence for this research field. Materials and Methods Study Design and Sample Collection This retrospective study analyzed 33,579 urinary stone composition data collected at the First Affiliated Hospital of Guangzhou Medical University from April 2014 to December 2024. Variables extracted from the database included age, sex, stone location, stone composition (primary, secondary, and tertiary components), thyroid disease status (HT, HT-, HPT), visit time, age groups, and patient ID. Samples with incomplete data or indeterminate results were excluded. For patients with recurrent stones or multiple stones, each stone sample was analyzed independently. The study received ethics committee exemption (ES-2025-K062) and adhered to the principles of the Declaration of Helsinki. Propensity Score Matching and Cohort Construction Propensity Score Matching (PSM) is a statistical technique used to mitigate selection bias in observational studies[ 20 , 21 ]. Based on data from 33,579 urolithiasis patients, PSM was employed to identify stone patients with comorbid thyroid disorders and match them 1:1 with stone-only patients (Fig. 1 ). Three independent matched cohorts were established: the HT group included 149 case-control pairs, totaling 298 patients; the HT- group included 70 pairs, totaling 140 patients; and the HPT group included 41 pairs, totaling 82 patients. Matching controlled for baseline characteristics including age and sex, ensuring comparability between case and control groups while maximizing control of confounding factors. Stone Composition Analysis Stone composition was analyzed using Fourier transform infrared spectroscopy (Thermo) in the laboratory. According to European Association of Urology guidelines, stones were initially classified into seven basic types: calcium oxalate (CaOx), calcium phosphate (CaP), uric acid (UA), magnesium ammonium phosphate (MAP), carbonate apatite (CA), ammonium urate (AU), and cystine (CYS). Stones containing MAP, CA, or AU were classified as infection stones. For further analysis, these components were regrouped into five major categories: calcium-containing stones (including various forms of calcium oxalate monohydrate (COM), calcium oxalate dihydrate (COD), CaP, and calcium carbonate (CC), infection stones (including MAP, CA and AU), uric acid stones (including UA and its derivatives), CYS, and other rare components. Primary, secondary, and tertiary components were analyzed for each stone sample. Statistical Analysis Baseline characteristics were analyzed for both original and matched cohorts. Continuous variables were analyzed using t-test or Mann-Whitney U test, while categorical variables were analyzed using chi-square test or Fisher's exact test. Between-group differences in sex, age, temporal, and seasonal distributions were assessed. Linear regression models were used to analyze temporal trends in calcium-containing stone composition from 2014 to 2024. Stratified analyses were performed to evaluate the effects of thyroid disorders on stone composition across sex, age groups ( 60 years), and seasonal subgroups. Generalized linear models with interaction terms were constructed: glm(y ~ Thyroid_status * Factor + Age + Sex + Season + StoneLoc, family = binomial), where y represents the binary variable for stone composition and Factor represents sex, age group, or season. Interaction effects were visualized through predictive probability heatmaps. Multivariable logistic regression analysis was performed to evaluate the independent effects of thyroid disorders on major stone composition formation. Stone type served as the dependent variable, thyroid disease status as the independent variable, with adjustment for age, sex, season, and stone location. Adjusted odds ratios (OR) with 95% confidence intervals were calculated and results were presented through forest plots. Two-sided tests were used with p < 0.05 considered statistically significant. Graphical visualization was performed using the 'ggplot2' package in R, with subsequent color and layout refinements made in Adobe Illustrator, as previously described[ 22 , 23 ]. Results Distribution Characteristics of Thyroid Disease-Related Stone Composition and Time-Sex Interaction Effect Analysis Figure 2 systematically demonstrates the effects of thyroid disorders on urinary stone composition and their interactions with temporal and sex factors. Analysis of infection stone composition (Fig. 2 A) revealed disease-specific distribution patterns, with HPT exhibiting the highest proportion of infection stones while HT presented the lowest levels, and CA representing the predominant infection stone component across all groups. Non-infection stone analysis (Fig. 2 B) confirmed COM dominance across all thyroid disease groups, though its relative proportion varied significantly between different disease states. Temporal trend analysis revealed dynamic evolutionary characteristics of stone composition. The HT group (Fig. 2 C) demonstrated marked fluctuating changes in COM levels throughout the study period, particularly exhibiting significant time-dependency in recent years. In contrast, the HT- group (Fig. 2 D) maintained low and stable levels of calcium-containing stone components. These differential temporal evolution patterns suggest persistent effects of different thyroid functional states on stone formation. Sex-stratified analysis further revealed complex interaction relationships between disease and sex factors. The HT group (Fig. 2 E) presented subtle sex-based differences between cases and controls. The HT- group (Fig. 2 F) exhibited the most pronounced sexual dimorphism, with female patients in the case group showing higher levels of most stone components than males, while the control group displayed opposite sex distribution patterns. The HPT group (Fig. 2 G) showed relatively minimal sex differences, possibly related to sample size limitations or specific pathophysiological mechanisms of this disease. Predictive probability models quantitatively evaluated interaction effects between disease status and sex. Under HT status (Fig. 2 H), COM composition demonstrated marked sex-specificity, with male patients presenting higher predictive probability. HT- status ( Fig. 2 I) manifested disease-specific effects, with elevated predictive probability of CA composition in the case group and females, while COM composition was more prominent in the control group, revealing differential regulatory mechanisms of disease status on stone composition formation. These findings collectively indicate that thyroid functional states, through interaction with sex factors, exert selective effects on the formation of specific stone components. Age-Dependent Stone Composition Distribution Characteristics and Disease-Specific Interaction Effects Thyroid disease-related stone formation exhibited marked age-dependent characteristics. Age distribution analysis (Fig. 3A) showed that all three thyroid disease patient groups were predominantly middle-aged and elderly populations, mainly concentrated above 40 years of age, establishing the foundation for age-stratified analysis. Age-related analysis of infection stone composition revealed disease-specific patterns. In HT patients (Fig. 3B), MAP composition demonstrated obvious age-dependent increasing trends, particularly in middle-aged and elderly groups. In contrast, HT- (Fig. 3C) and HPT groups (Fig. 3D) did not present similar age-related trends in infection stone composition, suggesting fundamental differences in age-modulating mechanisms of infection stone formation across different thyroid functional states. Calcium-containing stone composition analysis further confirmed the modulatory role of age. COM showed increasing trends with age across all three thyroid diseases, while COD remained relatively stable across age groups. This difference suggests that COM may be a sensitive indicator of age-related metabolic changes. Figure 3. Age-Dependent Distribution and Interaction Effect Analysis of Stone Composition in Thyroid Disease Patients. (A) Age distribution characteristics of patients with three thyroid diseases. (B-D) Distribution of infection stone components across different age groups: (B) HT group; (C) HT- group; (D) HPT group. (E-G) Age distribution patterns of calcium-containing stone components (COD/COM): (E) HT group; (F) HT- group; (G) HPT group. (H-J) Predictive probability heatmaps of age-disease status interaction for stone composition formation: (H) HT group; (I) HT- group; (J) HPT group. Age-disease interaction predictive models quantitatively evaluated complex multidimensional interaction effects. Under HT status (Fig. 3H), young patients presented higher predictive probability with age-related decline, while COM exhibited opposite age-dependent increasing patterns. HT- status (Fig. 3I) displayed unique distribution characteristics, with COM maintaining high predictive probability across all age groups in the control group, while CA increased with age in the case group. HPT status (Fig. 3J) showed relatively mild age effects, with only slight CA component elevation in the middle-aged group (40–60 years). Comprehensive analysis indicates that age factors, through interaction with thyroid functional states, selectively modulate the formation of specific stone components. This age-dependent disease-specific pattern provides important evidence for developing individualized prevention strategies based on age stratification. Seasonal Variation Characteristics of Stone Formation in Thyroid Disease Patients Thyroid disease-related stone formation exhibited distinct seasonal distribution characteristics. Seasonal distribution analysis (Fig. 4 A) showed that all three thyroid disease groups demonstrated a common feature of lowest case numbers in winter, suggesting that cold seasons may have certain inhibitory effects on stone formation. Sex-season interaction analysis revealed complex distribution patterns. Female patients in the HT group exceeded males in most seasons, while the female predominance was more pronounced in the HT- group, with male patients maintaining relatively low levels across all seasons; the HPT group showed relatively balanced sex distribution (Fig. 4 B-D). Seasonal distribution demonstrated that the HT group peaked in summer, the HT- group showed bimodal peaks in spring and autumn, while the HPT group presented relatively mild seasonal fluctuations. Seasonal distribution of stone composition displayed disease-specific patterns. Both HT group (Fig. 4 E) and HT- group (Fig. 4 F) showed obvious case-control differences, with stone composition levels in the case group generally higher than controls across all seasons, among which HT- status presented the most significant season-disease interaction effects. The HPT group (Fig. 4 G) showed relatively minimal differences between cases and controls, possibly related to sample size limitations. COM served as the major component across all groups, demonstrating relatively obvious seasonal fluctuations. Predictive probability heatmap analysis quantified stone composition-season-disease interaction effects. HT group analysis revealed that COM composition presented higher predictive probability in the control group during autumn and winter seasons ( Fig. 4 H). HT- status (Fig. 4 I) exhibited unique patterns, with COM composition maintaining high predictive probability in the control group during spring, summer, and autumn, while the case group maintained relatively low levels, revealing persistent inhibitory effects of HT- on COM formation, with these effects influenced by seasonal factors. HPT status (Fig. 4 J) showed seasonal effects primarily in UA composition, presenting relatively higher predictive probability in autumn. Comprehensive analysis indicates that seasonal factors influence stone composition formation through interaction with thyroid functional states. HT- demonstrated the strongest disease-specific effects, with its inhibitory effect on COM persisting across most seasons, suggesting that endocrine factors may combine with environmental factors to jointly influence the stone formation process. Independent Effect Analysis of Thyroid Diseases on Stone Composition Formation To evaluate the independent influence of thyroid diseases on stone composition formation, this study employed multivariable logistic regression models, controlling for confounding factors including age, sex, season, and stone location, to systematically analyze the effects of three thyroid disease states on major stone components. Multivariable regression analysis of HT and HPT patients revealed no statistically significant associations between these two diseases and formation of any detected stone components. The 95% confidence intervals of adjusted odds ratios for all stone components crossed the null line (OR = 1.0), indicating that after controlling for confounding factors, HT and HPT were not independent influencing factors for these stone composition formations (Figs. 5 A, C). In contrast, HT- presented a unique bidirectional regulatory pattern. The adjusted OR value for CA stones was significantly greater than 1.0, with the 95% confidence interval entirely positioned to the right of the null line, confirming its promotion of CA stone formation. Conversely, the adjusted OR value for COM was significantly less than 1.0, with the confidence interval entirely positioned to the left of the null line, indicating that hypothyroidism inhibits COM stone formation. This bidirectional regulatory effect is reported for the first time in thyroid disease research, suggesting that thyroid hormone deficiency selectively affects specific stone types through different pathophysiological pathways. Baseline Characteristics and Stone Composition Analysis after PSM To precisely evaluate the relationship between different types of thyroid diseases and urinary stone characteristics, this study first analyzed the original cohort data, then employed PSM methodology to eliminate interference from confounding factors such as demographics. Original cohort analysis revealed significant sex distribution differences among thyroid disease patients. For instance, both HT and HT- groups demonstrated extremely significant female predominance (female proportions of 61.7% and 84.3%, respectively, p < 0.001). Additionally, HT- group presented an aging trend (p = 0.039) ( Table S1 -3 ). These baseline differences emphasized the necessity of employing PSM to control confounding factors. Following PSM, we successfully constructed three balanced and comparable study cohorts. First, we found that all thyroid disease cases with stones were concentrated after 2020, while control groups were concentrated around 2014 (all p 80%), while case groups showed more uniform seasonal distribution (all p < 0.001). Subsequently, we found that different thyroid diseases exhibited specific effects on stone composition. These included calcium-containing stones, the COD proportion in the HT group was significantly higher than its control group (15.1% vs 7.5%, p = 0.016), suggesting that hyperthyroidism may promote COD formation through specific metabolic pathways. In infection stones, the CA proportion in the HT- group was significantly higher than its control group (85.2% vs 64.3%, p = 0.043), indicating that hypothyroidism may have a stronger association with infection stone formation (Table 1 – 3 ). These findings indicate that although the three thyroid diseases demonstrated common characteristics in temporal distribution, their effects on stone composition were disease-specific, providing important evidence for understanding the differential mechanisms of the endocrine system in stone formation. Table 1 Baseline Characteristics After PSM in HT Status Variable level Overall No HT- stone formers HT- stone formers p n 298 149 149 Year mean SD 2017.00 (3.71) 2014.00 (0.00) 2020.00 (3.08) < 0.001 Month mean SD 5.88 (2.82) 4.49 (1.04) 7.26 (3.32) < 0.001 Season Fall 38 (12.8) 2 (1.3) 36 (24.2) < 0.001 Spring 165 (55.4) 130 (87.2) 35 (23.5) Summer 62 (20.8) 17 (11.4) 45 (30.2) Winter 33 (11.1) 0 (0.0) 33 (22.1) PATIENTSEX female 184 (61.7) 92 (61.7) 92 (61.7) 1.000 male 114 (38.3) 57 (38.3) 57 (38.3) PATIENTAGE mean SD 52.75 (11.08) 52.75 (11.10) 52.75 (11.10) 1.000 AgeGroup High age (> 60) 78 (26.2) 39 (26.2) 39 (26.2) 1.000 Middle age (40–60) 176 (59.1) 88 (59.1) 88 (59.1) Young age (< 40) 44 (14.8) 22 (14.8) 22 (14.8) Stone location mean SD 1.27 (0.49) 1.25 (0.45) 1.28 (0.52) 0.552 Infection stones_MC MAP 12 (18.8) 5 (16.7) 7 (20.6) 0.421 ACCP 11 (17.2) 7 (23.3) 4 (11.8) CA 40 (62.5) 17 (56.7) 23 (67.6) COM 1 (1.6) 1 (3.3) 0 (0.0) Infection stones_SC1 WK 1 (1.6) 0 (0.0) 1 (2.9) 0.008 COD 3 (4.7) 1 (3.3) 2 (5.9) CaP 1 (1.6) 1 (3.3) 0 (0.0) MAP 24 (37.5) 17 (56.7) 7 (20.6) ACCP 6 (9.4) 4 (13.3) 2 (5.9) CA 12 (18.8) 5 (16.7) 7 (20.6) COM 17 (26.6) 2 (6.7) 15 (44.1) Infection stones_SC2 COD 4 (9.3) 0 (0.0) 4 (19.0) 0.234 CaP 6 (14.0) 4 (18.2) 2 (9.5) MAP 12 (27.9) 8 (36.4) 4 (19.0) AU 1 (2.3) 1 (4.5) 0 (0.0) ACCP 13 (30.2) 6 (27.3) 7 (33.3) CA 6 (14.0) 3 (13.6) 3 (14.3) COM 1 (2.3) 0 (0.0) 1 (4.8) Calcium-containing stones_MC COD 21 (11.3) 7 (7.5) 14 (15.1) 0.016 DCP 1 (0.5) 1 (1.1) 0 (0.0) AU 1 (0.5) 1 (1.1) 0 (0.0) ACCP 2 (1.1) 2 (2.2) 0 (0.0) CA 11 (5.9) 10 (10.8) 1 (1.1) COM 150 (80.6) 72 (77.4) 78 (83.9) Calcium-containing stones_SC1 COD 79 (59.8) 39 (57.4) 40 (62.5) 0.161 CaP 3 (2.3) 3 (4.4) 0 (0.0) UA 6 (4.5) 1 (1.5) 5 (7.8) AU 1 (0.8) 0 (0.0) 1 (1.6) CA 15 (11.4) 8 (11.8) 7 (10.9) COM 28 (21.2) 17 (25.0) 11 (17.2) Calcium-containing stones_SC2 WK 1 (3.0) 0 (0.0) 1 (7.7) 0.515 COD 9 (27.3) 7 (35.0) 2 (15.4) XA 1 (3.0) 1 (5.0) 0 (0.0) CaP 1 (3.0) 1 (5.0) 0 (0.0) DCP 1 (3.0) 1 (5.0) 0 (0.0) UA 3 (9.1) 2 (10.0) 1 (7.7) ACCP 1 (3.0) 0 (0.0) 1 (7.7) CA 9 (27.3) 5 (25.0) 4 (30.8) COM 7 (21.2) 3 (15.0) 4 (30.8) Uric acid stones_MC UA 45 (95.7) 23 (92.0) 22 (100.0) 0.399 MSU 1 (2.1) 1 (4.0) 0 (0.0) COM 1 (2.1) 1 (4.0) 0 (0.0) Uric acid stones_SC1 CaP 1 (3.3) 1 (9.1) 0 (0.0) 0.003 MAP 1 (3.3) 1 (9.1) 0 (0.0) AU 4 (13.3) 4 (36.4) 0 (0.0) DHA 10 (33.3) 0 (0.0) 10 (52.6) COM 14 (46.7) 5 (45.5) 9 (47.4) Uric acid stones_SC2 ACCP 1 (100.0) 1 (100.0) 0 (NaN) NA Table 2 Baseline Characteristics After PSM in HT- Status Variable level Overall No HT- stone formers HT- stone formers p n 140 70 70 Year mean SD 2017.44 (4.06) 2014.00 (0.00) 2020.89 (3.01) < 0.001 Month mean SD 5.71 (2.55) 4.53 (1.25) 6.90 (2.94) < 0.001 Season Fall 24 (17.1) 2 (2.9) 22 (31.4) < 0.001 Spring 83 (59.3) 61 (87.1) 22 (31.4) Summer 25 (17.9) 7 (10.0) 18 (25.7) Winter 8 (5.7) 0 (0.0) 8 (11.4) PATIENTSEX female 118 (84.3) 59 (84.3) 59 (84.3) 1.000 male 22 (15.7) 11 (15.7) 11 (15.7) PATIENTAGE mean SD 54.60 (13.24) 54.60 (13.29) 54.60 (13.29) 1.000 AgeGroup High age (> 60) 48 (34.3) 24 (34.3) 24 (34.3) 1.000 Middle age (40–60) 82 (58.6) 41 (58.6) 41 (58.6) Young age (< 40) 10 (7.1) 5 (7.1) 5 (7.1) Stone location mean SD 1.23 (0.42) 1.23 (0.42) 1.23 (0.42) 1.000 Infection stones_MC MAP 6 (14.6) 2 (14.3) 4 (14.8) 0.043 ACCP 3 (7.3) 3 (21.4) 0 (0.0) CA 32 (78.0) 9 (64.3) 23 (85.2) Infection stones_SC1 WK 1 (2.6) 0 (0.0) 1 (4.0) 0.048 CaP 1 (2.6) 1 (7.1) 0 (0.0) MAP 15 (38.5) 9 (64.3) 6 (24.0) ACCP 9 (23.1) 3 (21.4) 6 (24.0) CA 5 (12.8) 1 (7.1) 4 (16.0) COM 8 (20.5) 0 (0.0) 8 (32.0) Infection stones_SC2 WK 2 (10.0) 0 (0.0) 2 (16.7) 0.201 COD 3 (15.0) 0 (0.0) 3 (25.0) CaP 2 (10.0) 2 (25.0) 0 (0.0) MAP 4 (20.0) 2 (25.0) 2 (16.7) AU 1 (5.0) 1 (12.5) 0 (0.0) ACCP 4 (20.0) 2 (25.0) 2 (16.7) COM 4 (20.0) 1 (12.5) 3 (25.0) Calcium-containing stones_MC COD 11 (13.6) 5 (10.0) 6 (19.4) 0.447 ACCP 2 (2.5) 2 (4.0) 0 (0.0) CA 7 (8.6) 4 (8.0) 3 (9.7) COM 61 (75.3) 39 (78.0) 22 (71.0) Calcium-containing stones_SC1 COD 34 (55.7) 22 (61.1) 12 (48.0) 0.456 UA 2 (3.3) 1 (2.8) 1 (4.0) AU 2 (3.3) 2 (5.6) 0 (0.0) ACCP 2 (3.3) 1 (2.8) 1 (4.0) CA 7 (11.5) 2 (5.6) 5 (20.0) COM 14 (23.0) 8 (22.2) 6 (24.0) Calcium-containing stones_SC2 COD 4 (28.6) 2 (22.2) 2 (40.0) 0.382 DCP 1 (7.1) 1 (11.1) 0 (0.0) UA 2 (14.3) 2 (22.2) 0 (0.0) ACCP 1 (7.1) 0 (0.0) 1 (20.0) CA 2 (14.3) 2 (22.2) 0 (0.0) COM 4 (28.6) 2 (22.2) 2 (40.0) Uric acid stones_MC UA 18 (100.0) 6 (100.0) 12 (100.0) NA Uric acid stones_SC1 COD 1 (7.7) 0 (0.0) 1 (10.0) 0.067 CaP 1 (7.7) 1 (33.3) 0 (0.0) AU 1 (7.7) 1 (33.3) 0 (0.0) DHA 6 (46.2) 0 (0.0) 6 (60.0) COM 4 (30.8) 1 (33.3) 3 (30.0) Uric acid stones_SC2 COM 1 (100.0) 0 (NaN) 1 (100.0) NA Table 3 Baseline Characteristics After PSM in HPT Status Variable level Overall No HT- stone formers HT- stone formers p n 83 42 41 Year mean SD 2017.49 (3.96) 2014.00 (0.00) 2021.07 (2.50) < 0.001 Month mean SD 5.34 (2.31) 4.50 (0.94) 6.20 (2.93) 0.001 Season Fall 11 (13.3) 0 (0.0) 11 (26.8) < 0.001 Spring 52 (62.7) 35 (83.3) 17 (41.5) Summer 16 (19.3) 7 (16.7) 9 (22.0) Winter 4 (4.8) 0 (0.0) 4 (9.8) PATIENTSEX female 42 (50.6) 21 (50.0) 21 (51.2) 1.000 male 41 (49.4) 21 (50.0) 20 (48.8) PATIENTAGE mean SD 54.01 (14.37) 53.88 (14.42) 54.15 (14.49) 0.934 AgeGroup High age (> 60) 30 (36.1) 15 (35.7) 15 (36.6) 0.993 Middle age (40–60) 39 (47.0) 20 (47.6) 19 (46.3) Young age (< 40) 14 (16.9) 7 (16.7) 7 (17.1) Stone location mean SD 1.35 (0.55) 1.38 (0.58) 1.32 (0.52) 0.600 Infection stones_MC MAP 2 (8.3) 0 (0.0) 2 (11.8) 0.432 ACCP 4 (16.7) 2 (28.6) 2 (11.8) CA 18 (75.0) 5 (71.4) 13 (76.5) Infection stones_SC1 WK 2 (8.3) 0 (0.0) 2 (11.8) 0.124 COD 3 (12.5) 1 (14.3) 2 (11.8) CaP 1 (4.2) 1 (14.3) 0 (0.0) MAP 9 (37.5) 5 (71.4) 4 (23.5) ACCP 2 (8.3) 0 (0.0) 2 (11.8) CA 2 (8.3) 0 (0.0) 2 (11.8) COM 5 (20.8) 0 (0.0) 5 (29.4) Infection stones_SC2 WK 2 (11.8) 0 (0.0) 2 (14.3) 0.166 COD 2 (11.8) 0 (0.0) 2 (14.3) CaP 1 (5.9) 1 (33.3) 0 (0.0) MAP 6 (35.3) 2 (66.7) 4 (28.6) ACCP 2 (11.8) 0 (0.0) 2 (14.3) COM 4 (23.5) 0 (0.0) 4 (28.6) Calcium-containing stones_MC COD 5 (10.6) 1 (4.2) 4 (17.4) 0.078 AU 1 (2.1) 1 (4.2) 0 (0.0) CA 4 (8.5) 4 (16.7) 0 (0.0) COM 37 (78.7) 18 (75.0) 19 (82.6) Calcium-containing stones_SC1 COD 21 (58.3) 7 (43.8) 14 (70.0) 0.439 UA 1 (2.8) 1 (6.2) 0 (0.0) AU 1 (2.8) 0 (0.0) 1 (5.0) ACCP 2 (5.6) 1 (6.2) 1 (5.0) CA 5 (13.9) 3 (18.8) 2 (10.0) COM 6 (16.7) 4 (25.0) 2 (10.0) Calcium-containing stones_SC2 COD 2 (16.7) 2 (28.6) 0 (0.0) 0.399 XA 1 (8.3) 1 (14.3) 0 (0.0) UA 1 (8.3) 0 (0.0) 1 (20.0) AU 1 (8.3) 1 (14.3) 0 (0.0) CA 4 (33.3) 2 (28.6) 2 (40.0) COM 3 (25.0) 1 (14.3) 2 (40.0) Uric acid stones_MC UA 10 (90.9) 9 (90.0) 1 (100.0) 1.000 MSU 1 (9.1) 1 (10.0) 0 (0.0) Uric acid stones_SC1 CaP 1 (20.0) 0 (0.0) 1 (100.0) 0.082 MAP 1 (20.0) 1 (25.0) 0 (0.0) COM 3 (60.0) 3 (75.0) 0 (0.0) Discussion By analyzing 33,579 urolithiasis patients via large-scale PSM, this study first systematically elucidates the distinct effects of various thyroid disease subtypes on urinary stone composition. Our research made several key innovative contributions: (1) first application of PSM methodology to eliminate confounding factors for precise evaluation of independent effects of thyroid diseases on stone composition; (2) first discovery that hypothyroidism can promote CA formation while inhibiting COM formation; (3) establishment of a multidimensional interaction model of thyroid disease-stone composition-age-sex-season. Unlike previous studies focusing solely on thyroid diseases and stone incidence, our research provided deeper insights through integration of detailed stone composition analysis, temporal trend assessment, and multifactor interaction analysis. Specifically, while confirming the association between HT and elevated COD proportion (15.1% vs 7.5%, p = 0.016), we discovered the HT- group showing significantly elevated CA proportion (85.2% vs 64.3%, p = 0.043) while COM formation was inhibited. Multivariable logistic regression further confirmed HT- as an independent risk factor for CA formation (OR > 1.0) but protective against COM (OR < 1.0). This previously unreported bidirectional regulatory effect provides new perspectives for understanding the role of thyroid hormones in stone pathophysiology. Although the direct molecular mechanisms by which thyroid hormones affect CA and COM formation remain to be elucidated, we speculate based on existing research that multiple levels may be involved. The inhibitory mechanism on COM formation may involve three aspects: urinary pH, oxidative stress, and oxalate metabolism. Under HT- status, the risk of metabolic acidosis is reduced, causing urinary pH to trend toward neutral or alkaline[ 24 ]. Given that acidic environment is a key condition for COM crystallization, elevated pH inhibits its formation[ 25 ]. Decreased metabolic rate similarly leads to reduced reactive oxygen species generation, thereby alleviating oxidative stress damage and inflammatory response in renal tubular epithelial cells, reducing the likelihood of COM formation[ 26 ]. Additionally, hypothyroidism may also affect urinary oxalate excretion and deposition in renal tissue through systemic metabolic changes, thereby reducing raw material supply for COM[ 27 , 28 ]. The promotional mechanism for CA formation may involve two aspects: urinary pH and calcium-phosphate metabolic disorders. Corresponding to the mechanism inhibiting COM formation, studies indicate that elevated urinary pH is a risk factor for CaP formation, and CaP typically transforms into CA over time[ 29 – 31 ]. Murray et al. noted that thyroid dysfunction may interfere with feedback regulation of calcium-phosphate metabolism, potentially leading to altered urinary calcium excretion patterns, thereby creating conditions for CA formation[ 32 ]. Additionally, we observed that HT correlates with elevated COD proportion, possibly opposite to the inhibitory mechanism of HT- on COM, suggesting that hypermetabolic states may exacerbate oxalate metabolic disorders. Meanwhile, we found that various stone component levels in the HT- group were all higher than controls, consistent with other research reports of elevated serum uric acid levels in subclinical hypothyroid patients, collectively pointing to systemic metabolic disorders induced by thyroid dysfunction[ 33 , 34 ]. These findings lay the foundation for deeper exploration of pathophysiological associations between thyroid diseases and urolithiasis. Furthermore, we found that all thyroid disease cases with stones were concentrated after 2020 (p < 0.001), possibly reflecting recent advances in diagnostic technology and the impact of the COVID-19 pandemic[ 35 ]. This finding suggests the need for continuous monitoring of temporal changes in disease patterns. Regarding demographic characteristics, we found important age and sex interaction effects. Age analysis revealed age-dependent growth of MAP in HT patients, with CA showing the strongest age-disease interaction effects. This aligns with Sampath et al.'s research on TSH regulation of bone remodeling[ 36 ]. Considering the close relationship between bone metabolism and calcium-phosphate balance, age-related bone metabolic changes may influence stone formation tendency[ 37 ]. This suggests that the close relationship between age-related bone metabolic changes and calcium-phosphate balance affects stone formation. Sex analysis revealed 84.3% female proportion in the HT- group, with higher CA proportion in females, consistent with Wang et al.'s findings of higher CaP and infection stone probability in female stone patients, and Huang et al.'s findings that sex hormones may modulate the relationship between thyroid function and stone formation[ 38 , 39 ]. Additionally, under HT status, male patients showed significantly higher risk of COM formation, echoing Guerlain et al.'s research findings that sex hormones may regulate thyroid function, tissue microcalcification tendency, and systemic oxalate metabolism[ 40 , 41 ]. Seasonal analysis showed that disease effects in the HT- group persisted across seasons, with stone component levels in the case group significantly higher than controls across all seasons, and COM components maintaining high predictive probability in spring, summer, and autumn. This suggests that endocrine factors' influence on stone formation may be less modulated by seasonal factors, having a dominant role in stone formation[ 42 – 44 ]. Our research has clear clinical guidance value. Scheede-Bergdahl et al.'s research indicated that elevated TSH levels are an independent risk factor for kidney stones, emphasizing the importance of thyroid function monitoring in stone prevention[ 45 ]. Based on our findings, we propose recommendations: (1) for HT patients, focus should be on monitoring COD stone formation; (2) for HT- patients, increased CA stone risk should be monitored; (3) given significant age and sex differences, specific monitoring and prevention protocols should be developed for patients of different ages and sexes. Despite providing important findings, this study has the following limitations: (1) retrospective design cannot establish causal relationships; (2) lack of dose-effect analysis of thyroid hormone levels; (3) single-center study limits generalizability. Future prospective multicenter studies are needed to validate findings and explore specific molecular mechanisms. In conclusion, this study, through large-scale PSM analysis, systematically revealed for the first time the specific effects of different thyroid disease subtypes on urinary stone composition, particularly the bidirectional regulatory effect of HT- on CA and COM formation. This core finding, combined with its complex interaction patterns with age, sex, and season, not only provides novel insights for understanding the role of the endocrine system in stone pathophysiology but also establishes a solid scientific foundation for developing more precise individualized stone prevention and treatment strategies. In conclusion, through large-scale PSM analysis, this study systematically revealed for the first time the specific effects of different thyroid disease subtypes on urinary stone composition, particularly the bidirectional regulatory effect of hypothyroidism on CA and COM formation. This core finding, combined with its complex interaction patterns with age, sex, and season, not only provides novel insights into understanding the role of the endocrine system in stone pathophysiology but also establishes a solid scientific foundation for developing more precise individualized urolithiasis prevention and treatment strategies. Declarations Author Contribution Author contribution Mengting Wang and Zhenglin Chang contributedto conceptualization, data collection, and writing the original draft. Yiping Lai and Haojie Wu participated in data collection preparation. Ying Liang , Qianjun Li, Guohua Zeng, and Baoqing Sun were involved in writing (review& editing). All authors participated in the review of the manuscript. References Peerapen P, Thongboonkerd V (2023) May Kidney Stone Prevention. Adv Nutr. : 14:555 – 69 Khan SR, Pearle MS, Robertson WG et al Kidney stones. Nat Rev Dis Primers 2016 Feb 25: 2:16008 Zhang XZ, Lei XX, Jiang YL et al (2023 Jan) Application of metabolomics in urolithiasis: the discovery and usage of succinate. Signal Transduct Target Ther 21:8 Tan S, Yuan D, Su H et al (2024) Jan Prevalence of urolithiasis in China: a systematic review and meta-analysis. BJU Int. : 133:34–43 Hong SY, Xia QD, Yang YY et al (2023) Mar The role of microbiome: a novel insight into urolithiasis. 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08:47:09","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":164207,"visible":true,"origin":"","legend":"","description":"","filename":"b41981be60ff482c8883d1912d48fa6c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7784983/v1/da32698bdc3ce6edca6bc90f.xml"},{"id":95805961,"identity":"436619b6-22be-4fe3-8bf6-b4dc62538abd","added_by":"auto","created_at":"2025-11-13 08:47:10","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":172050,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7784983/v1/54776aa8a1fe9d605f388d3a.html"},{"id":95806142,"identity":"29c073e6-b29b-4e45-8dbf-6d02e9824214","added_by":"auto","created_at":"2025-11-13 08:47:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":325823,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Design Flowchart:\u003c/strong\u003e From 33,579 urolithiasis patients, three 1:1 matched cohorts were established through propensity score matching: HTgroup (n=298), HT-group (n=140), and HPT group (n=82).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7784983/v1/3210b901a066502fc221a71c.png"},{"id":95806047,"identity":"2f191a53-b62b-4363-88c9-34c1bb7f805b","added_by":"auto","created_at":"2025-11-13 08:47:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":311523,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution Characteristics of Stone Composition in Thyroid Disease Patients and Multifactor Interaction Analysis\u003c/strong\u003e (A) Distribution of major infection stone components across three thyroid disease groups. (B) Distribution of major non-infection stone components across three thyroid disease groups. (C) Temporal trends of calcium-containing stone components (COM/COD) in HT patients (2014-2025). (D) Temporal trends of calcium-containing stone components in HT- patients. (E-G) Sex-specific distribution comparison of stone composition between thyroid disease groups and matched control groups: (E) HT group; (F) HT- group; (G) HPT group. (H) Predictive probability heatmap of sex-disease interaction for stone composition formation under HT status. (I) Predictive probability heatmap of sex-disease interaction for stone composition formation under HT- status.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7784983/v1/c960c14011e3d31610282a89.png"},{"id":95805953,"identity":"1ff18555-be4e-4a9f-bae7-feb6d1624b1e","added_by":"auto","created_at":"2025-11-13 08:47:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":292129,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge-Dependent Distribution and Interaction Effect Analysis of Stone Composition in Thyroid Disease Patients.\u003c/strong\u003e(A) Age distribution characteristics of patients with three thyroid diseases. (B-D) Distribution of infection stone components across different age groups: (B) HT group; (C) HT- group; (D) HPT group. (E-G) Age distribution patterns of calcium-containing stone components (COD/COM): (E) HT group; (F) HT- group; (G) HPT group. (H-J) Predictive probability heatmaps of age-disease status interaction for stone composition formation: (H) HT group; (I) HT- group; (J) HPT group.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7784983/v1/5de46b07eccee1e56b4fe9ef.png"},{"id":95805943,"identity":"bd9c874e-fc84-4941-ab66-7e82b7b70803","added_by":"auto","created_at":"2025-11-13 08:47:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":363631,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSeasonal Distribution Characteristics and Interaction Effect Analysis of Stone Composition in Thyroid Disease Patients\u003c/strong\u003e (A) Seasonal distribution of patients with three thyroid diseases. (B-D) Sex distribution characteristics across different seasons: (B) HT group; (C) HT- group; (D) HPT group. * indicates statistically significant sex differences. (E-G) Seasonal distribution comparison of stone composition between case and control groups: (E) HT group; (F) HT- group; (G) HPT group. (H-J) Predictive probability heatmaps of season-disease status interaction for stone composition formation: (H) HT group; (I) HT- group; (J) HPT group.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7784983/v1/53b6291472edf19171fce33e.png"},{"id":95805934,"identity":"a37623f6-c75f-4c8b-a8c2-68cd331fb890","added_by":"auto","created_at":"2025-11-13 08:47:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":181172,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultivariable Logistic Regression Analysis of Thyroid Disease Effects on Major Stone Components\u003c/strong\u003e(A) Adjusted odds ratios of hyperthyroidism for stone composition formation. (B) Adjusted odds ratios of hypothyroidism for stone composition formation. (C) Adjusted odds ratios of hyperparathyroidism for stone composition formation. The vertical dashed line represents OR=1.0 (null value). Horizontal lines represent 95% confidence intervals. Models were adjusted for confounding factors including age, sex, season, and stone location.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7784983/v1/69de347d43c206dfa5f7a842.png"},{"id":103252478,"identity":"f9b15d46-eb2c-4921-a357-1a9e8a85de2e","added_by":"auto","created_at":"2026-02-23 16:14:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3189245,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7784983/v1/223dd850-a88a-454c-a58b-330b9f6db9ff.pdf"},{"id":95806176,"identity":"59c661f4-cd23-48af-b1d1-9a96e233ec2a","added_by":"auto","created_at":"2025-11-13 08:47:18","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":66289,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7784983/v1/76270d16bb911d0907caa1b7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bidirectional Regulation of Urinary Stone Composition by Hypothyroidism: A Propensity Score-Matched Analysis of 33,579 Patients","fulltext":[{"header":"Highlights","content":"\u003cp\u003e1. First application of PSM to analyze 33,579 urolithiasis patients, revealing specific effects of thyroid diseases on stone composition\u003c/p\u003e\u003cp\u003e2. HT- presents unique bidirectional effects: promoting CA formation while inhibiting COM formation\u003c/p\u003e\u003cp\u003e3. Female patients with HT- demonstrate the highest stone risk and exhibit significant age-sex-season interaction effects\u003c/p\u003e\u003cp\u003e4. HT selectively promotes COD formation\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eUrolithiasis is a relatively typical urological disease, including kidney stones, ureteral stones, bladder or urethral stones, which is affecting up to 14.8% of the global population and continues to increase, with recurrence rates as high as 50% within the first 5 years after initial stone episode[\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Stone formation is a complex multifactorial process involving genetics, diet, metabolic abnormalities, and endocrine diseases[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among endocrine factors, the relationship between hyperparathyroidism (HPT) and stones has been widely recognized, with kidney stone incidence in primary hyperparathyroidism (PHPT) patients reaching 20\u0026ndash;30%, primarily promoting calcium-containing stone formation through hypercalcemia and hypercalciuria[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, hypomagnesemia and urinary calcium/magnesium ratio in asymptomatic PHPT are also considered associated with kidney stones[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, research on the relationship between thyroid disorders, which affect 5\u0026ndash;10% of the population, and urinary stone composition remains extremely limited[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn recent years, increasing evidence indicates that the endocrine system plays an important regulatory role in urinary stone formation, with the relationship between thyroid functional status and stone formation gradually gaining attention[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Theoretically, thyroid hormones have broad regulatory effects on calcium-phosphate metabolism, renal function, and urinary acid-base balance, all of which are closely associated with stone formation[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Thyroid dysfunction may affect stone formation through mechanisms including influencing glomerular filtration rate, altering urinary composition, and regulating bone metabolism; however, these hypotheses lack validation from large-scale clinical studies. Meanwhile, existing research has obvious limitations: primarily focusing on parathyroid while neglecting thyroid disorders; studying only incidence rates without analyzing stone composition; lacking rigorous control of confounding factors; and failing to explore the modulatory effects of factors such as age, sex, and season[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUrolithiasis is a heterogeneous disease, with different compositions reflecting different pathological processes[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Calcium-containing stones (accounting for over 80%) are associated with calcium-phosphate metabolic disorders; infection stones indicate urinary tract infections and alkaline urine environment; uric acid stones reflect purine metabolic abnormalities[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Considering the metabolic regulatory effects of thyroid hormones, different thyroid functional states may selectively influence specific stone types, but the specific mechanisms remain unclear.\u003c/p\u003e\u003cp\u003eTo address this knowledge gap, this study, based on 33,579 urinary stone composition data, systematically evaluates for the first time the effects of hyperthyroidism (HT), hypothyroidism (HT-) and HPT on stone composition. Propensity score matching (PSM) was employed to control confounding factors, multivariable logistic regression was used to assess independent effects, and an age-sex-season multidimensional interaction model was established. To our knowledge, this is the first large-scale study exploring the relationship between thyroid disorders and stone composition, potentially providing pioneering evidence for this research field.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Sample Collection\u003c/h2\u003e\u003cp\u003eThis retrospective study analyzed 33,579 urinary stone composition data collected at the First Affiliated Hospital of Guangzhou Medical University from April 2014 to December 2024. Variables extracted from the database included age, sex, stone location, stone composition (primary, secondary, and tertiary components), thyroid disease status (HT, HT-, HPT), visit time, age groups, and patient ID. Samples with incomplete data or indeterminate results were excluded. For patients with recurrent stones or multiple stones, each stone sample was analyzed independently. The study received ethics committee exemption (ES-2025-K062) and adhered to the principles of the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePropensity Score Matching and Cohort Construction\u003c/h3\u003e\n\u003cp\u003ePropensity Score Matching (PSM) is a statistical technique used to mitigate selection bias in observational studies[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Based on data from 33,579 urolithiasis patients, PSM was employed to identify stone patients with comorbid thyroid disorders and match them 1:1 with stone-only patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Three independent matched cohorts were established: the HT group included 149 case-control pairs, totaling 298 patients; the HT- group included 70 pairs, totaling 140 patients; and the HPT group included 41 pairs, totaling 82 patients. Matching controlled for baseline characteristics including age and sex, ensuring comparability between case and control groups while maximizing control of confounding factors.\u003c/p\u003e\n\u003ch3\u003eStone Composition Analysis\u003c/h3\u003e\n\u003cp\u003eStone composition was analyzed using Fourier transform infrared spectroscopy (Thermo) in the laboratory. According to European Association of Urology guidelines, stones were initially classified into seven basic types: calcium oxalate (CaOx), calcium phosphate (CaP), uric acid (UA), magnesium ammonium phosphate (MAP), carbonate apatite (CA), ammonium urate (AU), and cystine (CYS). Stones containing MAP, CA, or AU were classified as infection stones. For further analysis, these components were regrouped into five major categories: calcium-containing stones (including various forms of calcium oxalate monohydrate (COM), calcium oxalate dihydrate (COD), CaP, and calcium carbonate (CC), infection stones (including MAP, CA and AU), uric acid stones (including UA and its derivatives), CYS, and other rare components. Primary, secondary, and tertiary components were analyzed for each stone sample.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eBaseline characteristics were analyzed for both original and matched cohorts. Continuous variables were analyzed using t-test or Mann-Whitney U test, while categorical variables were analyzed using chi-square test or Fisher's exact test. Between-group differences in sex, age, temporal, and seasonal distributions were assessed. Linear regression models were used to analyze temporal trends in calcium-containing stone composition from 2014 to 2024. Stratified analyses were performed to evaluate the effects of thyroid disorders on stone composition across sex, age groups (\u0026lt;\u0026thinsp;40 years, 40\u0026ndash;60 years, \u0026gt;\u0026thinsp;60 years), and seasonal subgroups. Generalized linear models with interaction terms were constructed: glm(y\u0026thinsp;~\u0026thinsp;Thyroid_status * Factor\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Sex\u0026thinsp;+\u0026thinsp;Season\u0026thinsp;+\u0026thinsp;StoneLoc, family\u0026thinsp;=\u0026thinsp;binomial), where y represents the binary variable for stone composition and Factor represents sex, age group, or season. Interaction effects were visualized through predictive probability heatmaps. Multivariable logistic regression analysis was performed to evaluate the independent effects of thyroid disorders on major stone composition formation. Stone type served as the dependent variable, thyroid disease status as the independent variable, with adjustment for age, sex, season, and stone location. Adjusted odds ratios (OR) with 95% confidence intervals were calculated and results were presented through forest plots. Two-sided tests were used with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. Graphical visualization was performed using the 'ggplot2' package in R, with subsequent color and layout refinements made in Adobe Illustrator, as previously described[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDistribution Characteristics of Thyroid Disease-Related Stone Composition and Time-Sex Interaction Effect Analysis\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e systematically demonstrates the effects of thyroid disorders on urinary stone composition and their interactions with temporal and sex factors. Analysis of infection stone composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) revealed disease-specific distribution patterns, with HPT exhibiting the highest proportion of infection stones while HT presented the lowest levels, and CA representing the predominant infection stone component across all groups. Non-infection stone analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) confirmed COM dominance across all thyroid disease groups, though its relative proportion varied significantly between different disease states. Temporal trend analysis revealed dynamic evolutionary characteristics of stone composition. The HT group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) demonstrated marked fluctuating changes in COM levels throughout the study period, particularly exhibiting significant time-dependency in recent years. In contrast, the HT- group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) maintained low and stable levels of calcium-containing stone components. These differential temporal evolution patterns suggest persistent effects of different thyroid functional states on stone formation.\u003c/p\u003e\u003cp\u003eSex-stratified analysis further revealed complex interaction relationships between disease and sex factors. The HT group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) presented subtle sex-based differences between cases and controls. The HT- group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF) exhibited the most pronounced sexual dimorphism, with female patients in the case group showing higher levels of most stone components than males, while the control group displayed opposite sex distribution patterns. The HPT group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG) showed relatively minimal sex differences, possibly related to sample size limitations or specific pathophysiological mechanisms of this disease.\u003c/p\u003e\u003cp\u003ePredictive probability models quantitatively evaluated interaction effects between disease status and sex. Under HT status (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH), COM composition demonstrated marked sex-specificity, with male patients presenting higher predictive probability. HT- status \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI) manifested disease-specific effects, with elevated predictive probability of CA composition in the case group and females, while COM composition was more prominent in the control group, revealing differential regulatory mechanisms of disease status on stone composition formation. These findings collectively indicate that thyroid functional states, through interaction with sex factors, exert selective effects on the formation of specific stone components.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAge-Dependent Stone Composition Distribution Characteristics and Disease-Specific Interaction Effects\u003c/h3\u003e\n\u003cp\u003eThyroid disease-related stone formation exhibited marked age-dependent characteristics. Age distribution analysis (Fig.\u0026nbsp;3A) showed that all three thyroid disease patient groups were predominantly middle-aged and elderly populations, mainly concentrated above 40 years of age, establishing the foundation for age-stratified analysis. Age-related analysis of infection stone composition revealed disease-specific patterns. In HT patients (Fig.\u0026nbsp;3B), MAP composition demonstrated obvious age-dependent increasing trends, particularly in middle-aged and elderly groups. In contrast, HT- (Fig.\u0026nbsp;3C) and HPT groups (Fig.\u0026nbsp;3D) did not present similar age-related trends in infection stone composition, suggesting fundamental differences in age-modulating mechanisms of infection stone formation across different thyroid functional states. Calcium-containing stone composition analysis further confirmed the modulatory role of age. COM showed increasing trends with age across all three thyroid diseases, while COD remained relatively stable across age groups. This difference suggests that COM may be a sensitive indicator of age-related metabolic changes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 3. Age-Dependent Distribution and Interaction Effect Analysis of Stone Composition in Thyroid Disease Patients.\u003c/b\u003e (A) Age distribution characteristics of patients with three thyroid diseases. (B-D) Distribution of infection stone components across different age groups: (B) HT group; (C) HT- group; (D) HPT group. (E-G) Age distribution patterns of calcium-containing stone components (COD/COM): (E) HT group; (F) HT- group; (G) HPT group. (H-J) Predictive probability heatmaps of age-disease status interaction for stone composition formation: (H) HT group; (I) HT- group; (J) HPT group.\u003c/p\u003e\u003cp\u003eAge-disease interaction predictive models quantitatively evaluated complex multidimensional interaction effects. Under HT status (Fig.\u0026nbsp;3H), young patients presented higher predictive probability with age-related decline, while COM exhibited opposite age-dependent increasing patterns. HT- status (Fig.\u0026nbsp;3I) displayed unique distribution characteristics, with COM maintaining high predictive probability across all age groups in the control group, while CA increased with age in the case group. HPT status (Fig.\u0026nbsp;3J) showed relatively mild age effects, with only slight CA component elevation in the middle-aged group (40\u0026ndash;60 years). Comprehensive analysis indicates\u003c/p\u003e\u003cp\u003ethat age factors, through interaction with thyroid functional states, selectively modulate the formation of specific stone components. This age-dependent disease-specific pattern provides important evidence for developing individualized prevention strategies based on age stratification.\u003c/p\u003e\n\u003ch3\u003eSeasonal Variation Characteristics of Stone Formation in Thyroid Disease Patients\u003c/h3\u003e\n\u003cp\u003eThyroid disease-related stone formation exhibited distinct seasonal distribution characteristics. Seasonal distribution analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) showed that all three thyroid disease groups demonstrated a common feature of lowest case numbers in winter, suggesting that cold seasons may have certain inhibitory effects on stone formation. Sex-season interaction analysis revealed complex distribution patterns. Female patients in the HT group exceeded males in most seasons, while the female predominance was more pronounced in the HT- group, with male patients maintaining relatively low levels across all seasons; the HPT group showed relatively balanced sex distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-D). Seasonal distribution demonstrated that the HT group peaked in summer, the HT- group showed bimodal peaks in spring and autumn, while the HPT group presented relatively mild seasonal fluctuations. Seasonal distribution of stone composition displayed disease-specific patterns. Both HT group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eE) and HT- group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eF) showed obvious case-control differences, with stone composition levels in the case group generally higher than controls across all seasons, among which HT- status presented the most significant season-disease interaction effects. The HPT group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eG) showed relatively minimal differences between cases and controls, possibly related to sample size limitations. COM served as the major component across all groups, demonstrating relatively obvious seasonal fluctuations.\u003c/p\u003e\u003cp\u003ePredictive probability heatmap analysis quantified stone composition-season-disease interaction effects. HT group analysis revealed that COM composition presented higher predictive probability in the control group during autumn and winter seasons \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). HT- status (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eI) exhibited unique patterns, with COM composition maintaining high predictive probability in the control group during spring, summer, and autumn, while the case group maintained relatively low levels, revealing persistent inhibitory effects of HT- on COM formation, with these effects influenced by seasonal factors. HPT status (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ) showed seasonal effects primarily in UA composition, presenting relatively higher predictive probability in autumn. Comprehensive analysis indicates that seasonal factors influence stone composition formation through interaction with thyroid functional states. HT- demonstrated the strongest disease-specific effects, with its inhibitory effect on COM persisting across most seasons, suggesting that endocrine factors may combine with environmental factors to jointly influence the stone formation process.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eIndependent Effect Analysis of Thyroid Diseases on Stone Composition Formation\u003c/h2\u003e\u003cp\u003eTo evaluate the independent influence of thyroid diseases on stone composition formation, this study employed multivariable logistic regression models, controlling for confounding factors including age, sex, season, and stone location, to systematically analyze the effects of three thyroid disease states on major stone components. Multivariable regression analysis of HT and HPT patients revealed no statistically significant associations between these two diseases and formation of any detected stone components. The 95% confidence intervals of adjusted odds ratios for all stone components crossed the null line (OR\u0026thinsp;=\u0026thinsp;1.0), indicating that after controlling for confounding factors, HT and HPT were not independent influencing factors for these stone composition formations (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, C). In contrast, HT- presented a unique bidirectional regulatory pattern. The adjusted OR value for CA stones was significantly greater than 1.0, with the 95% confidence interval entirely positioned to the right of the null line, confirming its promotion of CA stone formation. Conversely, the adjusted OR value for COM was significantly less than 1.0, with the confidence interval entirely positioned to the left of the null line, indicating that hypothyroidism inhibits COM stone formation. This bidirectional regulatory effect is reported for the first time in thyroid disease research, suggesting that thyroid hormone deficiency selectively affects specific stone types through different pathophysiological pathways.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics and Stone Composition Analysis after PSM\u003c/h2\u003e\u003cp\u003eTo precisely evaluate the relationship between different types of thyroid diseases and urinary stone characteristics, this study first analyzed the original cohort data, then employed PSM methodology to eliminate interference from confounding factors such as demographics. Original cohort analysis revealed significant sex distribution differences among thyroid disease patients. For instance, both HT and HT- groups demonstrated extremely significant female predominance (female proportions of 61.7% and 84.3%, respectively, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, HT- group presented an aging trend (p\u0026thinsp;=\u0026thinsp;0.039) (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-3\u003c/b\u003e). These baseline differences emphasized the necessity of employing PSM to control confounding factors.\u003c/p\u003e\u003cp\u003eFollowing PSM, we successfully constructed three balanced and comparable study cohorts. First, we found that all thyroid disease cases with stones were concentrated after 2020, while control groups were concentrated around 2014 (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Seasonal distribution presented similar patterns: control groups were highly concentrated in spring (\u0026gt;\u0026thinsp;80%), while case groups showed more uniform seasonal distribution (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subsequently, we found that different thyroid diseases exhibited specific effects on stone composition. These included calcium-containing stones, the COD proportion in the HT group was significantly higher than its control group (15.1% vs 7.5%, p\u0026thinsp;=\u0026thinsp;0.016), suggesting that hyperthyroidism may promote COD formation through specific metabolic pathways. In infection stones, the CA proportion in the HT- group was significantly higher than its control group (85.2% vs 64.3%, p\u0026thinsp;=\u0026thinsp;0.043), indicating that hypothyroidism may have a stronger association with infection stone formation (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These findings indicate that although the three thyroid diseases demonstrated common characteristics in temporal distribution, their effects on stone composition were disease-specific, providing important evidence for understanding the differential mechanisms of the endocrine system in stone formation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics After PSM in HT Status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003elevel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo HT- stone formers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHT- stone formers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear mean SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2017.00 (3.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2014.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020.00 (3.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonth mean SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.88 (2.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.49 (1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.26 (3.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36 (24.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e165 (55.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e130 (87.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35 (23.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSummer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45 (30.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWinter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePATIENTSEX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e184 (61.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92 (61.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e92 (61.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114 (38.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57 (38.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57 (38.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePATIENTAGE mean SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.75 (11.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.75 (11.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.75 (11.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgeGroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh age (\u0026gt;\u0026thinsp;60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78 (26.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (26.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39 (26.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle age (40\u0026ndash;60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e176 (59.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88 (59.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88 (59.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYoung age (\u0026lt;\u0026thinsp;40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStone location mean SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.27 (0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.25 (0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.28 (0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.552\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfection stones_MC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.421\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (23.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (56.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23 (67.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfection stones_SC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (56.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (26.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (44.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfection stones_SC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (19.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (14.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (18.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (27.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (36.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (19.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (30.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (14.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium-containing stones_MC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e150 (80.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72 (77.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78 (83.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium-containing stones_SC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79 (59.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (57.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40 (62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (21.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium-containing stones_SC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.515\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (21.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid stones_MC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45 (95.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (92.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.399\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMSU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid stones_SC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (36.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDHA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (52.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (46.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (45.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9 (47.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid stones_SC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (NaN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics After PSM in HT- Status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003elevel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo HT- stone formers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHT- stone formers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear mean SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2017.44 (4.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2014.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020.89 (3.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonth mean SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.71 (2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.53 (1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.90 (2.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83 (59.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61 (87.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSummer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18 (25.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWinter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePATIENTSEX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118 (84.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59 (84.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59 (84.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePATIENTAGE mean SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.60 (13.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.60 (13.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.60 (13.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgeGroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh age (\u0026gt;\u0026thinsp;60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (34.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (34.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24 (34.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle age (40\u0026ndash;60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (58.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41 (58.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41 (58.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYoung age (\u0026lt;\u0026thinsp;40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStone location mean SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.23 (0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.23 (0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.23 (0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfection stones_MC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (78.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (64.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23 (85.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfection stones_SC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (38.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (64.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (24.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (24.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfection stones_SC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium-containing stones_MC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.447\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (75.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (78.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (71.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium-containing stones_SC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (55.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (61.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12 (48.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.456\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (23.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (24.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium-containing stones_SC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.382\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid stones_MC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid stones_SC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDHA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (46.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid stones_SC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (NaN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics After PSM in HPT Status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003elevel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo HT- stone formers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHT- stone formers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear mean SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2017.49 (3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2014.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2021.07 (2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonth mean SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.34 (2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.50 (0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.20 (2.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (26.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (62.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (83.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17 (41.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSummer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (19.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9 (22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWinter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePATIENTSEX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (50.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 (51.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (49.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (48.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePATIENTAGE mean SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.01 (14.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.88 (14.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.15 (14.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.934\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgeGroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh age (\u0026gt;\u0026thinsp;60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (36.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.993\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle age (40\u0026ndash;60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (47.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (47.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19 (46.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYoung age (\u0026lt;\u0026thinsp;40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStone location mean SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.35 (0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.38 (0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.32 (0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.600\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfection stones_MC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.432\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (71.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13 (76.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfection stones_SC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (71.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (23.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (29.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfection stones_SC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (35.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (23.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium-containing stones_MC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (78.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19 (82.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium-containing stones_SC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (58.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (43.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (70.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium-containing stones_SC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.399\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid stones_MC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (90.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (90.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMSU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid stones_SC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBy analyzing 33,579 urolithiasis patients via large-scale PSM, this study first systematically elucidates the distinct effects of various thyroid disease subtypes on urinary stone composition. Our research made several key innovative contributions: (1) first application of PSM methodology to eliminate confounding factors for precise evaluation of independent effects of thyroid diseases on stone composition; (2) first discovery that hypothyroidism can promote CA formation while inhibiting COM formation; (3) establishment of a multidimensional interaction model of thyroid disease-stone composition-age-sex-season. Unlike previous studies focusing solely on thyroid diseases and stone incidence, our research provided deeper insights through integration of detailed stone composition analysis, temporal trend assessment, and multifactor interaction analysis.\u003c/p\u003e\u003cp\u003eSpecifically, while confirming the association between HT and elevated COD proportion (15.1% vs 7.5%, p\u0026thinsp;=\u0026thinsp;0.016), we discovered the HT- group showing significantly elevated CA proportion (85.2% vs 64.3%, p\u0026thinsp;=\u0026thinsp;0.043) while COM formation was inhibited. Multivariable logistic regression further confirmed HT- as an independent risk factor for CA formation (OR\u0026thinsp;\u0026gt;\u0026thinsp;1.0) but protective against COM (OR\u0026thinsp;\u0026lt;\u0026thinsp;1.0). This previously unreported bidirectional regulatory effect provides new perspectives for understanding the role of thyroid hormones in stone pathophysiology. Although the direct molecular mechanisms by which thyroid hormones affect CA and COM formation remain to be elucidated, we speculate based on existing research that multiple levels may be involved. The inhibitory mechanism on COM formation may involve three aspects: urinary pH, oxidative stress, and oxalate metabolism. Under HT- status, the risk of metabolic acidosis is reduced, causing urinary pH to trend toward neutral or alkaline[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Given that acidic environment is a key condition for COM crystallization, elevated pH inhibits its formation[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Decreased metabolic rate similarly leads to reduced reactive oxygen species generation, thereby alleviating oxidative stress damage and inflammatory response in renal tubular epithelial cells, reducing the likelihood of COM formation[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, hypothyroidism may also affect urinary oxalate excretion and deposition in renal tissue through systemic metabolic changes, thereby reducing raw material supply for COM[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The promotional mechanism for CA formation may involve two aspects: urinary pH and calcium-phosphate metabolic disorders. Corresponding to the mechanism inhibiting COM formation, studies indicate that elevated urinary pH is a risk factor for CaP formation, and CaP typically transforms into CA over time[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Murray et al. noted that thyroid dysfunction may interfere with feedback regulation of calcium-phosphate metabolism, potentially leading to altered urinary calcium excretion patterns, thereby creating conditions for CA formation[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additionally, we observed that HT correlates with elevated COD proportion, possibly opposite to the inhibitory mechanism of HT- on COM, suggesting that hypermetabolic states may exacerbate oxalate metabolic disorders. Meanwhile, we found that various stone component levels in the HT- group were all higher than controls, consistent with other research reports of elevated serum uric acid levels in subclinical hypothyroid patients, collectively pointing to systemic metabolic disorders induced by thyroid dysfunction[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These findings lay the foundation for deeper exploration of pathophysiological associations between thyroid diseases and urolithiasis.\u003c/p\u003e\u003cp\u003eFurthermore, we found that all thyroid disease cases with stones were concentrated after 2020 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), possibly reflecting recent advances in diagnostic technology and the impact of the COVID-19 pandemic[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This finding suggests the need for continuous monitoring of temporal changes in disease patterns. Regarding demographic characteristics, we found important age and sex interaction effects. Age analysis revealed age-dependent growth of MAP in HT patients, with CA showing the strongest age-disease interaction effects. This aligns with Sampath et al.'s research on TSH regulation of bone remodeling[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Considering the close relationship between bone metabolism and calcium-phosphate balance, age-related bone metabolic changes may influence stone formation tendency[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This suggests that the close relationship between age-related bone metabolic changes and calcium-phosphate balance affects stone formation. Sex analysis revealed 84.3% female proportion in the HT- group, with higher CA proportion in females, consistent with Wang et al.'s findings of higher CaP and infection stone probability in female stone patients, and Huang et al.'s findings that sex hormones may modulate the relationship between thyroid function and stone formation[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Additionally, under HT status, male patients showed significantly higher risk of COM formation, echoing Guerlain et al.'s research findings that sex hormones may regulate thyroid function, tissue microcalcification tendency, and systemic oxalate metabolism[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Seasonal analysis showed that disease effects in the HT- group persisted across seasons, with stone component levels in the case group significantly higher than controls across all seasons, and COM components maintaining high predictive probability in spring, summer, and autumn. This suggests that endocrine factors' influence on stone formation may be less modulated by seasonal factors, having a dominant role in stone formation[\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur research has clear clinical guidance value. Scheede-Bergdahl et al.'s research indicated that elevated TSH levels are an independent risk factor for kidney stones, emphasizing the importance of thyroid function monitoring in stone prevention[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Based on our findings, we propose recommendations: (1) for HT patients, focus should be on monitoring COD stone formation; (2) for HT- patients, increased CA stone risk should be monitored; (3) given significant age and sex differences, specific monitoring and prevention protocols should be developed for patients of different ages and sexes. Despite providing important findings, this study has the following limitations: (1) retrospective design cannot establish causal relationships; (2) lack of dose-effect analysis of thyroid hormone levels; (3) single-center study limits generalizability. Future prospective multicenter studies are needed to validate findings and explore specific molecular mechanisms.\u003c/p\u003e\u003cp\u003eIn conclusion, this study, through large-scale PSM analysis, systematically revealed for the first time the specific effects of different thyroid disease subtypes on urinary stone composition, particularly the bidirectional regulatory effect of HT- on CA and COM formation. This core finding, combined with its complex interaction patterns with age, sex, and season, not only provides novel insights for understanding the role of the endocrine system in stone pathophysiology but also establishes a solid scientific foundation for developing more precise individualized stone prevention and treatment strategies.\u003c/p\u003e\u003cp\u003eIn conclusion, through large-scale PSM analysis, this study systematically revealed for the first time the specific effects of different thyroid disease subtypes on urinary stone composition, particularly the bidirectional regulatory effect of hypothyroidism on CA and COM formation. This core finding, combined with its complex interaction patterns with age, sex, and season, not only provides novel insights into understanding the role of the endocrine system in stone pathophysiology but also establishes a solid scientific foundation for developing more precise individualized urolithiasis prevention and treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor contribution Mengting Wang and Zhenglin Chang contributedto conceptualization, data collection, and writing the original draft. Yiping Lai and Haojie Wu participated in data collection preparation. Ying Liang , Qianjun Li, Guohua Zeng, and Baoqing Sun were involved in writing (review\u0026amp; editing). All authors participated in the review of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePeerapen P, Thongboonkerd V (2023) May Kidney Stone Prevention. 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Int J Urol. : 24:32\u0026thinsp;\u0026ndash;\u0026thinsp;8\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHao X, Shao Z, Zhang N et al Integrative genome-wide analyses identify novel loci associated with kidney stones and provide insights into its genetic architecture. Nat Commun 2023 Nov 18: 14:7498\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhan SR, Canales BK (2015) Unified theory on the pathogenesis of Randall's plaques and plugs. Urolithiasis. Jan: 43 Suppl 1:109\u0026thinsp;\u0026ndash;\u0026thinsp;23\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuzuki A, Hirokawa M, Otsuka I, Miyauchi A, Akamizu T (2001) Calcium oxalate crystals as a cause of multiple punctate echogenic foci in benign thyroid lesions. 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Chempluschem. : 90:e202400712\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurray SL, Wolf M (2024) Feb Calcium and Phosphate Disorders: Core Curriculum 2024. Am J Kidney Dis. : 83:241\u0026thinsp;\u0026ndash;\u0026thinsp;56\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXing Y, Yang L, Liu J, Ma H (2021) The Association with Subclinical Thyroid Dysfunction and Uric Acid. Int J Endocrinol. : 2021:9720618\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDesideri G, Bocale R, D'Amore AM et al (2020) Jan Thyroid hormones modulate uric acid metabolism in patients with recent onset subclinical hypothyroidism by improving insulin sensitivity. Intern Emerg Med. : 15:67\u0026ndash;71\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAli SZ, Baloch ZW, Cochand-Priollet B, Schmitt FC, Vielh P, VanderLaan PA (2023) Sep The 2023 Bethesda System for Reporting Thyroid Cytopathology. 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Thyroid. : 34:442-9\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuerlain J, Perie S, Lefevre M et al (2019) Localization and characterization of thyroid microcalcifications: A histopathological study. PLoS ONE 14:e0224138\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu F, Zhang N, Jiang P et al (2020 Mar) Characteristics of the urinary microbiome in kidney stone patients with hypertension. J Transl Med 17:18:130\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCorbetta S, Gianotti L, Castellano E et al (2024) Skeletal phenotypes in postmenopausal women affected by primary hyperparathyroidism. Front Endocrinol (Lausanne) 15:1475147\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYin G, Zhang W, Zhang J, Sheng T, Chen B Diabetes mediates the relationship between cardiometabolic index and kidney stones: a cross-sectional study. Sci Rep 2024 Dec 28: 14:31075\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu M, Gao M, Wu J et al Metabolic Syndrome and the Risk of Kidney Stones: Evidence from 487 860 UK Biobank Participants. J Clin Endocrinol Metab 2025 Mar 17: 110:e1211-e9\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIqbal S, Scheede-Bergdahl C, Yang D, Rafat Zhand K, Andonian S (2021) POS-228 THE ROLE OF THYROID-STIMULATING HORMONE IN NEPHROLITHIASIS ASSOCIATED WITH CHRONIC KIDNEY DISEASE. Kidney Int Rep 6:S96\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjur","sideBox":"Learn more about [World Journal of Urology](https://link.springer.com/journal/345)","snPcode":"345","submissionUrl":"https://submission.nature.com/new-submission/345/3","title":"World Journal of Urology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Urolithiasis, Stone composition, Hyperthyroidism (HT), Hypothyroidism (HT-), Hyperparathyroidism (HPT)","lastPublishedDoi":"10.21203/rs.3.rs-7784983/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7784983/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eUrolithiasis affects 14.8% of the global population, with its pathogenesis involving multiple systemic factors. Among patients with thyroid disorders, which affect 5\u0026ndash;10% of the population, the risk of stone formation and compositional characteristics may exhibit specific alterations; however, the mechanisms by which different thyroid functional states influence stone composition remain unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective analysis was conducted on 33,579 urinary stone composition data collected from 2014 to 2024 in South China. Propensity score matching (PSM) was employed to evaluate the distribution characteristics of stone composition across different thyroid functional states, establishing three 1:1 matched cohorts: hyperthyroidism group (n\u0026thinsp;=\u0026thinsp;298), hypothyroidism group (n\u0026thinsp;=\u0026thinsp;140), and hyperparathyroidism group (n\u0026thinsp;=\u0026thinsp;82). Multivariable logistic regression, generalized linear models, and interaction analyses were performed to assess the associations between stone composition and thyroid disorders, controlling for confounding factors including age, sex, season, and stone location.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe urinary stone composition analysis in this study revealed specific effects of different thyroid disorders. Patients with hyperthyroidism showed significantly higher proportions of calcium oxalate dihydrate (COD) stones compared to controls (15.1% vs 7.5%, p\u0026thinsp;=\u0026thinsp;0.016); patients with hypothyroidism exhibited increased proportions of carbonate apatite (CA) stones (85.2% vs 64.3%, p\u0026thinsp;=\u0026thinsp;0.043). Multivariable regression confirmed hypothyroidism as an independent risk factor for CA stones (OR\u0026thinsp;\u0026gt;\u0026thinsp;1.0), while demonstrating a protective effect against calcium oxalate monohydrate (COM) stones (OR\u0026thinsp;\u0026lt;\u0026thinsp;1.0). Interaction analyses revealed sex-based differences in COM stones among hyperthyroid patients (higher predicted probability in males), and seasonal variations in stone composition among hypothyroid patients. Age-stratified analysis identified increasing magnesium ammonium phosphate stones with age in hyperthyroid patients, with CA stones exhibiting the strongest age dependency.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eUrinary stone composition is specifically regulated by thyroid functional states. Hyperthyroidism is associated with increased COD stones, while hypothyroidism independently promotes CA stone formation but inhibits COM stones, suggesting that endocrine factors participate in the formation of different stone types through regulation of calcium-phosphate metabolism, providing important evidence for individualized prevention strategies based on stone composition.\u003c/p\u003e","manuscriptTitle":"Bidirectional Regulation of Urinary Stone Composition by Hypothyroidism: A Propensity Score-Matched Analysis of 33,579 Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 07:49:25","doi":"10.21203/rs.3.rs-7784983/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-17T04:51:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T23:53:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203861938831068419655034574438862065113","date":"2025-11-05T22:39:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-03T09:01:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308521954927323107228845716797625084965","date":"2025-11-02T07:15:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38628802040242452181384239299801025072","date":"2025-11-01T23:55:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-30T14:46:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-08T16:46:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-08T05:42:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Urology","date":"2025-10-05T13:35:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjur","sideBox":"Learn more about [World Journal of Urology](https://link.springer.com/journal/345)","snPcode":"345","submissionUrl":"https://submission.nature.com/new-submission/345/3","title":"World Journal of Urology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b0c285f7-3d05-4a36-a8a3-80c9f0e02d7e","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:12:32+00:00","versionOfRecord":{"articleIdentity":"rs-7784983","link":"https://doi.org/10.1007/s00345-026-06251-1","journal":{"identity":"world-journal-of-urology","isVorOnly":false,"title":"World Journal of Urology"},"publishedOn":"2026-02-20 15:58:52","publishedOnDateReadable":"February 20th, 2026"},"versionCreatedAt":"2025-11-13 07:49:25","video":"","vorDoi":"10.1007/s00345-026-06251-1","vorDoiUrl":"https://doi.org/10.1007/s00345-026-06251-1","workflowStages":[]},"version":"v1","identity":"rs-7784983","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7784983","identity":"rs-7784983","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}