Calcium-Magnesium-Phosphate-Potassium Metabolic Network Dysregulation in Nephrolithiasis: Predictive Model Development and Mechanistic Insights

Calcium-Magnesium-Phosphate-Potassium Metabolic Network Dysregulation in Nephrolithiasis: Predictive Model Development and Mechanistic Insights | 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 Calcium-Magnesium-Phosphate-Potassium Metabolic Network Dysregulation in Nephrolithiasis: Predictive Model Development and Mechanistic Insights Yunhan Wang, Caitao Dong, Wenbiao Liao, Sixing Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6849577/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To explore the pathophysiological interplay of calcium-magnesium-phosphate-potassium (Ca-Mg-P-K) metabolic networks in nephrolithiasis and establish an integrated risk prediction framework incorporating homeostatic biomarkers. Methods This hospital-based case-control study enrolled 611 nephrolithiasis patients and 624 gender-matched controls. Multivariable logistic regression identified core risk determinants, with model performance rigorously validated through ROC analysis, calibration curves, and decision curve analysis (DCA). A nomogram was constructed to enable dynamic risk visualization. Results The metabolic network-based model demonstrated exceptional discriminative capacity (AUC = 0.812, 95%CI: 0.789–0.836) and calibration accuracy (Brier score = 0.177, Hosmer-Lemeshow p = 0.852). Key predictors included calcium-magnesium ratio (Ca/Mg, aOR = 5.50), calcium-phosphate product (Ca×IP, aOR = 1.82), hypokalemia (aOR = 0.13), BMI ≥ 24, and hemoglobin reduction. The nomogram quantified individualized risk through synergistic scoring, with a score of 240 points identifying high-risk patients (sensitivity = 78.9%, specificity = 73.6%). Subgroup analyses revealed amplified risks in females (Ca/Mg OR = 10.01 vs 6.98) and those in the renal compensatory phase (eGFR > 90 group OR = 10.45). DCA validated the model's clinical utility, revealing net benefit superiority over traditional approaches across 0.3–0.8 risk thresholds. Conclusion This study establishes calcium-magnesium ratio (Ca/Mg) and calcium-phosphate product (Ca×IP) as key indicators of subclinical calcium dysregulation. The nomogram integrates metabolic network interactions to overcome single-marker limitations, with hypokalemia identified as a critical risk amplifier. This model enables early detection of network-level imbalance despite normal individual parameters, offering a clinically actionable tool for personalized nephrolithiasis prevention. Nephrolithiasis Calcium-magnesium ratio Calcium-phosphate product Multivariate predictive model Hypokalemia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Nephrolithiasis represents a global health challenge, characterized by a 5-year recurrence rate surpassing 50% and imposing substantial clinical and economic burdens[ 1 , 2 ]. Calcium-based calculi dominate the epidemiological landscape, comprising 75%-80% of all stone types. Of these, calcium oxalate (CaOx) stones constitute the majority (> 60%), while calcium phosphate (CaP) variants account for 15%-20%[ 3 , 4 ]. Despite diagnostic advances in stone composition analysis and 24-hour urinary metabolic profiling, 30%-40% of cases exhibit no identifiable metabolic derangements, creating critical barriers to personalized prevention protocols[ 5 , 6 ]. Traditional research has predominantly focused on isolated risk factors (e.g., hypercalciuria, hypocitraturia), while neglecting the synergistic effects of dynamic electrolyte networks involving calcium (Ca), magnesium (Mg), phosphate (P), and potassium (K) in the pathogenesis of nephrolithiasis[ 7 – 9 ]. These ions exhibit intricate regulatory crosstalk: magnesium competitively inhibits calcium oxalate crystallization[ 10 ], and high phosphate intake reduces intestinal calcium-phosphate binding, thereby enhancing calcium absorption and urinary calcium excretion. Low-phosphate diet combined with potassium citrate therapy effectively decreases urinary phosphate excretion and inhibits stone formation[ 11 ]. Potassium imbalance indirectly disrupts calcium-magnesium transport through modulation of Na⁺/K⁺-ATPase activity[ 12 , 13 ]. Critically, current risk stratification frameworks fail to systematically incorporate these multidimensional interactions, suggesting that conventional calcium-centric models inadequately capture the pathophysiological complexity of stone formation. This hospital-based case-control study aims to evaluate the predictive power of composite indices—specifically, the calcium-magnesium ratio (Ca/Mg) and the calcium-phosphate product (Ca×IP)—in comparison to traditional single-ion measurements. Our objective is to determine if dysregulation within the calcium-magnesium-phosphate-potassium network is a fundamental mechanism in stone formation, thereby establishing a risk prediction model based on multi-element equilibrium. The outcomes of this research are expected to lay a foundation for tailored nutritional intervention strategies. Method Study Design and Data Sources We conducted a hospital-based case-control study at Renmin Hospital of Wuhan University from 2023 to 2025, incorporating participants from both case and control groups. Sociodemographic and laboratory data were retrospectively acquired from the hospital's electronic medical records (EMR). Ethical approval for this retrospective study was granted by the Ethics Committee of Renmin Hospital of Wuhan University (approval number: WDRY2023-K183), which also waived the requirement for written informed consent. Participant Selection Inclusion Criteria for Cases: ① Age > 18 years; ② Radiologically confirmed nephrolithiasis (stone diameter ≥ 2 mm) by KUB plain film. Exclusion Criteria: History of primary hyperoxaluria, hyperparathyroidism, gastrointestinal disorders/surgery, autoimmune diseases, chronic kidney disease, urinary tract abnormalities/obstruction, malignancies, or other conditions potentially interfering with calcium-phosphate metabolism. Sample Size As illustrated in Fig. 1 , out of the 892 initially eligible nephrolithiasis patients, 281 were excluded due to various factors: metabolic comorbidities (n = 148), urological abnormalities (n = 83), and other confounders (n = 50), yielding 611 patients (405 males, 206 females). A gender-matched control group (624 subjects: 413 males, 211 females) was recruited to minimize sex-related bias. Statistical Analysis Data processing was conducted using Excel 2021 and SPSS 26.0. The normality of continuous variables was assessed using the Shapiro-Wilk test. Normally distributed data are presented as mean ± SD, while non-normally distributed data are expressed as median (IQR). Intergroup comparisons were performed using independent t-tests for normally distributed data and the Kolmogorov-Smirnov test for non-normal data. Categorical variables are reported as frequency (%) with differences assessed using χ² tests. Binary logistic regression modeling proceeded in two phases: univariate screening of significant variables (P < 0.05), followed by forward stepwise multivariate adjustment to calculate adjusted odds ratios (aORs) with 95% confidence intervals (CIs). Statistical analyses were conducted utilizing R software (4.4.2) and its integrated development environment, incorporating specialized packages for predictive modeling and visualization. The analytical workflow comprised three sequential phases: model construction, performance validation, and clinical application. For nomogram development, multivariate regression coefficients were proportionally scaled to establish variable-specific scoring thresholds. Predictor variables were vertically aligned to facilitate point summation. The aggregated score was then mapped to probability estimates through coordinate projection onto the outcome risk axis. Model discrimination was assessed using receiver operating characteristic (ROC) analysis, yielding conventional diagnostic performance metrics, including sensitivity, specificity, and area under the curve (AUC). Predictive calibration was rigorously evaluated through comparisons of observed versus predicted probabilities, employing bootstrap-corrected calibration curves. Clinical utility was assessed using threshold-dependent net benefit quantification across clinically relevant probability ranges through decision curve analysis. Subgroup analyses examined risk heterogeneity based on sex and renal function. All statistical tests were two-tailed, with significance defined at P < 0.05. Result This study involved 611 patients with nephrolithiasis and 624 matched healthy controls. As presented in Table 1, a comparison of baseline characteristics indicated no statistically significant differences in gender distribution (males: 66.3% in the stone group versus 66.2% in the control group) or median age (both groups: 55 years) (all P > 0.05). Body mass index (BMI) stratification demonstrated a significantly higher proportion of overweight/obesity in the stone group (32.6% vs. 22.8%, χ² = 18.632, P 0.05). Serum biochemical analysis identified significantly lower Mg levels in the stone group (0.81 vs. 0.85 mmol/L, P < 0.001) with elevated calcium-magnesium ratio (Ca/Mg: 2.74 vs. 2.63, P < 0.001). Parameters indicative of hepatic function showed reduced ALT (18.00 vs. 20.16 U/L) and AST (20.00 vs. 21.21 U/L) in stone formers (both P < 0.01), though ALT/AST ratio remained comparable. Notably, serum albumin (ALB) was significantly decreased in the stone group (41.90 vs. 43.50 g/L, P < 0.001), while renal function markers demonstrated significant abnormalities: elevated creatinine (Cr: 79.00 vs. 66.00 μmol/L), increased uric acid (UA: 358.00 vs. 337.25 μmol/L), and reduced estimated glomerular filtration rate (eGFR: 91.64 vs. 101.58 mL/min/1.73 m²) (all P < 0.001). Electrolyte profiling revealed significantly lower potassium (K: 3.93 vs. 4.10 mmol/L) alongside elevated sodium (140.60 vs. 140.20 mmol/L) and chloride (106.80 vs. 106.10 mmol/L) inthe stone group (all P < 0.05). The calcium-phosphate product (Ca×IP: 2.42 vs. 2.26 mmol²/L², P < 0.001) was markedly increased in the stone group. Hematological parameters showed decreased hemoglobin (Hb: 137.14 vs. 140.97 g/L) and hematocrit (HCT: 0.41 vs. 0.42) in the stone group (both P 0.05). Both univariate and multivariate logistic regression analyses revealed independent associations between overweight status, elevated calcium-magnesium ratio (Ca/Mg), and nephrolithiasis risk (Table 2). Univariate analysis identified 13 significant predictors including overweight (OR = 1.578), reduced Mg (OR = 0.001), elevated Ca/Mg (OR = 7.983), elevated ALB (OR = 0.871), elevated Cr (OR = 1.037), elevated UA (OR = 1.002), reduced K (OR = 0.166), elevated Cl (OR = 1.103), elevated calcium-phosphate product (Ca×IP, OR = 2.128), and reduced Hb (OR = 0.986), all demonstrating statistical significance (P < 0.05). Multivariate logistic regression analysis was performed using forward stepwise selection, incorporating variables that demonstrated significant associations (P<0.05) in preliminary univariate screening. Following adjustments for these significant predictors, seven variables retained statistical significance: overweight (aOR = 1.585, 95% CI: 1.160-2.166), elevated Ca/Mg (aOR = 5.501, 95% CI: 3.105-9.745), elevated Cr (aOR = 1.048, 95% CI: 1.040-1.057), reduced K (aOR = 0.128, 95% CI: 0.083-0.197), elevated Cl (aOR = 1.064, 95% CI: 1.007-1.124), elevated Ca×IP (aOR = 1.822, 95% CI: 1.347-2.466), and reduced Hb (aOR = 0.985, 95% CI: 0.976-0.994), with all P < 0.05. Notably, four parameters demonstrated particularly strong clinical associations: Ca/Mg elevation, K reduction, Ca×IP elevation, and overweight status exhibited particularly strong risk correlations. No significant associations were observed for obesity (aOR = 1.757, P = 0.158) or underweight status (aOR = 0.630, P = 0.197). This study developed a clinical prediction nomogram (Fig. 2) for visual risk assessment, demonstrating strong discriminative power (C-index = 0.812) and good calibration (Brier score = 0.177). Six key predictors were integrated into the model: calcium-magnesium ratio imbalance, overweight status (BMI ≥24 kg/m²), elevated serum creatinine, increased calcium-phosphate product, hypokalemia, and decreased hemoglobin levels. The nomogram enables rapid estimation of individualized risk probabilities by summing scores assigned to each variable. A total score of 240 corresponds to a high-risk probability, providing a practical tool for early identification of high-risk patients and formulation of clinical intervention strategies. As shown in Figure 3, the multivariate model demonstrated robust discriminative capacity with an AUC of 0.812 (95% CI: 0.789-0.836), significantly superior to random classification (P < 0.001). At the Youden index-optimized cutoff, sensitivity and specificity reached 78.9% and 73.6%, respectively. The AUC standard error (0.012) and exclusion of the null value (0.5) from the CI confirmed model robustness, supporting its clinical applicability for risk assessment. This study validated the predictive accuracy of the model through calibration curves (Fig. 4), which revealed a high degree of concordance between predicted probabilities and observed outcomes (Brier score = 0.177, calibration slope = 1.000) with no significant systematic deviation (Hosmer-Lemeshow test, p = 0.852). Coupled with excellent discriminative ability (C-index = 0.812), the model exhibited robust performance in risk stratification. Future work should further optimize calibration characteristics through external cohort validation and explore dynamic adjustment mechanisms to enhance clinical applicability. The risk prediction model developed in this study underwent validation for clinical utility through decision curve analysis (DCA, Fig. 5). Within the threshold probability range of 0.3 to 0.8, the model consistently demonstrated substantial positive net benefits and exhibited a clear distinction from the "ALL" and "NONE" reference lines. This suggests its capability to effectively balance false-positive and false-negative risks, thereby enhancing clinical decision-making. Additionally, with an impressive discrimination (C-index = 0.812) and calibration performance (Brier score = 0.177), this model serves as a dependable tool for personalized risk assessment. Future investigations should focus on validating its applicability in external cohorts and examining dynamic threshold adjustment mechanisms to tailor the model to various clinical contexts. Subgroup analyses revealed population heterogeneity in the effects of both Ca/Mg ratio and Ca×IP product (Table 3). Gender stratification demonstrated stronger associations in females for Ca/Mg (OR = 10.012 vs. males: 6.983; both P 90 mL/min/1.73 m² subgroup: Ca/Mg reached OR = 10.452 (95% CI: 5.402–20.223), significantly higher than in the 60–90 mL/min/1.73 m² group (OR = 4.599; P = 0.001) and non-significant in the 30–60 mL/min/1.73 m² group (P = 0.072). Similarly, Ca×IP exhibited maximal effect in the eGFR >90 group (OR = 2.208, P < 0.001), with diminishing significance in lower eGFR subgroups (OR = 2.363, P = 0.293 in 30-60 mL/min/1.73 m²). This dose-response attenuation suggests that calcium metabolism abnormalities predominantly influence stone formation during the renal compensatory phase (eGFR >60 mL/min/1.73 m²). Discussion This case-control study systematically elucidates the complex association between serum electrolyte imbalance and nephrolithogenesis. The multifactorial predictive model reveals that Ca/Mg, Ca×IP, and serum potassium constitute the core metabolic network of stone formation, with risk associations substantially exceeding those of traditional isolated indicators (e.g., serum calcium/phosphate). These findings provide critical insights for identifying "metabolically imbalanced but biochemically normal" individuals in clinical practice. The multivariate-adjusted OR for Ca/Mg reached 5.501 (95% CI: 3.105–9.745), exceeding the modest single-factor association of serum calcium (OR = 2.453, P = 0.187). This highlights the superior pathological relevance of Ca/Mg equilibrium over absolute ion concentrations. Supporting these results, molecular dynamics simulations conducted by Julie M. Riley's research team demonstrate that magnesium can destabilize calcium oxalate ion pairs and reduce the size of calcium phosphate aggregates in a concentration-dependent manner[ 14 , 15 ]. Interestingly, although hypomagnesemia showed strong protective effects in univariate analysis (OR = 0.001), its influence diminished when examined within the multivariate framework. This suggests that magnesium deficiency primarily disrupts calcium-magnesium homeostasis rather than acting as an independent risk factor. Consequently, there is a pressing need to reconsider magnesium supplementation approaches. Strategies that integrate monitoring of calcium metabolism alongside targeted interventions in TRPM6/7-mediated Ca/Mg cotransport could enhance therapeutic effectiveness[ 16 – 20 ]. Despite comparable serum phosphate levels between groups (1.14 vs. 1.11 mmol/L, P = 0.103), elevated Ca×IP in the stone group (2.42 vs. 2.26 mmol²/L²) demonstrated independent risk significance (aOR = 1.822). This implies that even with normal-range phosphate, calcium levels > 2.35 mmol/L may drive Ca×IP beyond crystallization thresholds. Compensatory phosphate reduction under hyperparathyroidism or vitamin D hyperactivity likely maintains elevated Ca×IP through calcium-dominated effects, underscoring the clinical value of routine Ca×IP monitoring, particularly for individuals with high-normal calcium[ 21 – 24 ]. Hypokalemia (aOR = 0.128) synergized with Ca/Mg/P dysregulation through triple mechanisms: (1) Metabolic acidosis induction via osteoclast RANKL/OPG signaling activation, increasing bone calcium release[ 25 , 26 ]; (2) Renal calcium handling disruption, elevating urinary calcium excretion[ 6 , 27 , 28 ]; (3) Mg²⁺-K⁺ vicious cycle: magnesium deficiency impairs Na⁺/K⁺-ATPase, exacerbating renal potassium wasting[ 29 – 31 ]. This "hypomagnesemia-hypokalemia" synergy reduces calcium-sensing receptor sensitivity, triggering uncontrolled PTH secretion and establishing a self-reinforcing "hypercalcemia-hypercalciuria" loop, explaining potassium's amplified risk effect in multivariate modeling. The model integrates metabolic balance indices (Ca/Mg, Ca×IP, K⁺) with traditional parameters (BMI, Cl⁻, Cr, Hb) to effectively identify "covert imbalance" in calcium metabolism through compensatory mechanisms. Despite comparable serum calcium between groups (2.23 vs. 2.23 mmol/L, P = 0.996), Ca/Mg and Ca×IP sensitively detected subclinical dysregulation—minor Mg/IP fluctuations amplified calcium's pathological impact. These composite biomarkers overcome the limitations of single-ion testing, serving as early clinical indicators of metabolic decompensation. Persistent BMI significance (overweight aOR = 1.585) may reflect adipose-endocrine disruption. Specifically, leptin upregulates renal NHE3 (promoting acid load) while suppressing vitamin D activation via PPARγ. This interplay may establish an "obesity-acidosis-mineral disorder" axis[ 32 ]. Hyperchloremia (aOR = 1.064) and anemia (Hb aOR = 0.985) further suggest acid-base imbalance and chronic inflammation indirectly modulate crystallization[ 33 ]. Although elevated creatinine retained significance (aOR = 1.048), its effect attenuated with declining eGFR (eGFR > 90: OR = 10.452 vs. eGFR 30–60: P = 0.072). This "compensatory-phase predominance" can be attributed to contrasting mechanisms at work. In the early stages of CKD, the kidneys retain their ability to concentrate tubular fluids, leading to a higher risk of calcium-phosphate supersaturation. In contrast, during the later stages of CKD, the development of secondary hyperparathyroidism obscures the influence of primary metabolic factors[ 21 , 34 ]. This observation highlights the critical importance of the metabolic network model, particularly for early interventions aimed at leveraging the renal compensatory phase. The model achieved an AUC of 0.812 (95% CI: 0.789–0.836). Its effectiveness stems from two key factors: biological integration (Ca/Mg and Ca×IP capture cross-pathway interactions) and mathematical amplification (nonlinear terms enhance early metabolic signals). In this study, the nomogram we developed combines essential indicators from the metabolic network (Ca/Mg, Ca×IP) with traditional metrics like BMI and creatinine levels, allowing for a dynamic visual assessment of nephrolithiasis risk (C-index = 0.812). One of the standout features of this nomogram is its scoring system for potassium and hemoglobin, which serves to highlight the clinical importance of the "hypokalemia-anemia-acidosis" metabolic axis. This provides a practical framework for applying the "metabolic network theory" in clinical settings. Calibration curves and decision curve analysis further validated the model’s reliability and revealed its clinical net benefit. With this tool, clinicians are empowered to adopt preventive measures for high-risk individuals (score ≥ 240) by utilizing interventions informed by metabolic network insights. The real breakthrough of this model is its unique ability to uncover hidden metabolic imbalances. For example, it identifies high-risk individuals with normal serum calcium but abnormal Ca/Mg and Ca×IP through synergistic interactions within the metabolic network—a capability unattainable in traditional single-marker systems. This "individual-normalized but network-dysregulated" phenomenon may explain recurrent stone formation in patients with "normal" conventional test results. Limitations This study has several limitations. Although Ca/Mg, Ca×IP, and serum potassium are core components of the multivariate model, the diagnostic accuracy of this approach still falls short when compared to imaging techniques, particularly urinary tract ultrasonography, which is widely regarded as the gold standard for detecting nephrolithiasis. Moreover, the retrospective nature of this study introduces potential selection bias, which could skew our findings. Furthermore, the absence of 24-hour urinary biochemical data limited in-depth analysis of serum-urine metabolic correlations. Despite these limitations, this research signals a significant shift in the study of nephrolithiasis, moving from a focus on individual factors to a more comprehensive, multifactorial approach, and from isolated metabolic pathways to their complex interconnections. Future prospective cohort studies should integrate metabolomics and ionomics to establish dynamic Ca-Mg-P-K monitoring systems, develop stone recurrence prediction models integrating machine learning algorithms, and clarify spatiotemporal causality in metabolic network dysregulation. Declarations Declaration of competing interest The authors declare that none of the work reported in this study could have been influenced by any known competing financial interests or personal relationships. Funding The National Natural Science Foundation of China supported this research (No. 82270797). Author Contribution The manuscript was developed by YW. Data collecting was done by WL. Data analysis was done by CD. YW drafted the manuscript. SY provided critical review. All authors evaluated and approved the final version. Data availability statement Data are available onrequest to the authors. 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Diabetes Obes Metab 24(12):2283-2296. http://doi.org/10.1111/dom.14829 Lindberg JS (2005) Calcimimetics: a new tool for management of hyperparathyroidism and renal osteodystrophy in patients with chronic kidney disease. Kidney Int Suppl (95):S33-6. http://doi.org/10.1111/j.1523-1755.2005.09505.x Tables Table 1. Baseline Characteristics of Nephrolithiasis Patients and Healthy Controls (n=1235) Case (n=611) Control (n=624) t/χ 2 /Z * P Gender, n (%) 0.001 0.971 Male 405 (66.3) 413 (66.2) Female 206 (33.7) 211 (33.8) Age, year, Median (IQR) 55 (19) 55 (20) 0.787 0.565 BMI, n (%) 18.632 ＜0.001 Underweight 18 (2.9) 36 (5.3) Normal 394 (64.5) 446 (71.5) Overweight 177 (29.0) 127 (20.4) Obesity 22 (3.6) 15 (2.4) Smoke, n (%) 162 (26.5) 145 (23.2) 1.775 0.183 Drink, n (%) 103 (16.9) 117 (18.8) 0.755 0.385 Hypertension, n (%) 181 (29.6) 176 (28.2) 0.302 0.582 Diabetes, n (%) 71 (11.6) 64 (10.3) 0.590 0.442 Ca, mmol/L 2.23 (0.10) 2.23 (0.10) 0.410 0.996 Mg, mmol/L 0.81 (0.09) 0.85 (0.09) 3.414 ＜0.001 Ca/Mg 2.74 (0.31) 2.63 (0.28) 3.693 ＜0.001 ALT, U/L 18.00 (14.00) 20.16 (15.00) 1.900 0.001 AST, U/L 20.00 (7.00) 21.21 (7.38) 3.025 ＜0.001 ALT/AST 0.94 (0.52) 0.94 (0.46) 0.766 0.600 ALB, g/L 41.90 (5.00) 43.50 (4.60) 3.596 ＜0.001 Cr, μmol/L 79.00 (31.00) 66.00 (22.00) 5.401 ＜0.001 UA, μmol/L 358.00 (148.00) 337.25 (124.49) 2.042 ＜0.001 GLU, mmol/L 4.91 (1.07) 4.91 (0.96) 0.675 0.753 K, mmol/L 3.93 (0.43) 4.10 (0.37) 4.382 ＜0.001 Na, mmol/L 140.60 (2.70) 140.20 (2.70) 1.528 0.019 Cl, mmol/L 106.80 (3.10) 106.10 (3.20) 2.178 ＜0.001 IP, mmol/L 1.14 (0.25) 1.11 (0.24) 1.219 0.103 Ca×IP, mmol2/L2, Mean±SD 2.42±0.47 2.26±0.44 5.972 ＜0.001 eGFR, mL/min 91.64 (31.15) 101.58 (20.20) 5.189 ＜0.001 Hb, g/L 137.14±22.00 140.97±16.91 -4.082 ＜0.001 HCT, L/L 0.41±0.04 0.42±0.05 -4.722 ＜0.001 * Kolmogorov-Smirnov test Table 2. Univariate and Multivariate Binary Logistic Regression Analyses of Nephrolithiasis Univariate Multivariate Crude OR 95% CI P-value Adjust OR 95% CI P-value Gender, n (%) Male 1.000 1.000 - Female 0.996 0.786-1.260 0.971 Age, year, Median (IQR) 1.002 0.994-1.011 0.564 BMI, n (%) Underweight 0.566 0.346-1.013 0.055 0.630 0.312-1.272 0.197 Normal 1.000 1.000 - 1.000 1.000 - Overweight 1.578 1.210-2.057 ＜0.001 1.585 1.160-2.166 0.004 Obesity 1.660 0.849-3.245 0.138 1.757 0.803-3.841 0.158 Smoke, n (%) No 1 1 - Yes 1.192 0.920-1.543 0.183 Drink, n (%) No 1 1 - Yes 0.879 0.656-1.177 0.385 Hypertension, n (%) No 1 1 - Yes 1.071 0.838-1.370 0.583 Diabetes, n (%) No 1 1 - Yes 1.150 0.804-1.646 0.443 Ca, mmol/L 2.453 0.647-9.304 0.187 Mg, mmol/L 0.001 0.001-0.003 ＜0.001 Ca/Mg 7.983 4.851-13.136 ＜0.001 5.501 3.105-9.745 ＜0.001 ALT, U/L 0.997 0.993-1.002 0.298 AST, U/L 0.995 0.986-1.003 0.202 ALT/AST 0.880 0.666-1.162 0.366 ALB, g/L 0.871 0.842-0.901 ＜0.001 Cr, μmol/L 1.037 1.030-1.043 ＜0.001 1.048 1.040-1.057 ＜0.001 UA, μmol/L 1.002 1.001-1.004 ＜0.001 GLU, mmol/L 1.045 0.965-1.131 0.279 K, mmol/L 0.166 0.114-0.242 ＜0.001 0.128 0.083-0.197 ＜0.001 Na, mmol/L 1.059 1.006-1.115 0.028 Cl, mmol/L 1.103 1.055-1.154 ＜0.001 1.064 1.007-1.124 0.027 IP, mmol/L 1.528 0.840-2.779 0.165 Ca×IP, mmol2/L2, Mean±SD 2.128 1.649-2.746 ＜0.001 1.822 1.347-2.466 ＜0.001 eGFR, mL/min 0.964 0.957-0.970 ＜0.001 Hb, g/L 0.986 0.979-0.993 ＜0.001 0.985 0.976-0.994 ＜0.001 HCT, L/L 0.003 0.001-0.032 ＜0.001 Table 3. Subgroup Analyses of Calcium-Magnesium Ratio and Calcium-Phosphate Product Stratified by Gender and eGFR Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6849577","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530550770,"identity":"d7a430dd-892f-4e48-902d-0923ea142e9b","order_by":0,"name":"Yunhan Wang","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Yunhan","middleName":"","lastName":"Wang","suffix":""},{"id":530550772,"identity":"66ce8970-eec6-4463-a8d5-1e26dc14f4e8","order_by":1,"name":"Caitao Dong","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Caitao","middleName":"","lastName":"Dong","suffix":""},{"id":530550774,"identity":"79b728c7-8524-4760-bb05-9fba252ab920","order_by":2,"name":"Wenbiao Liao","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Wenbiao","middleName":"","lastName":"Liao","suffix":""},{"id":530550777,"identity":"8cca6fd1-0277-4b28-be7b-98e05c3d3dce","order_by":3,"name":"Sixing Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYPACGwYDEMVDgpY00rUcJkGL/Iwcw88Fv84nbpdIYHzwto1B3pyQFoMzZ4ylZ/bdTtw5I4HZcG4bg+HOBkJa2HsMpHl7buduuJHAJs3bxpBgcICQw5p5jH/z9pwDaWH/TZQWhuM9ZtI8Pw6AbWEmSovBmWNl1rwNyfUbzjxslpxzTsJwA0GHzUjefJvnj52xwfHkgx/elNnIE3YYA4cBA2MbiMHYACQkCKoHAvYHDAx/iFE4CkbBKBgFIxYAAINfQdKbZ18OAAAAAElFTkSuQmCC","orcid":"","institution":"Renmin Hospital of Wuhan 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1","display":"","copyAsset":false,"role":"figure","size":269761,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of case and control selection process with gender matching.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6849577/v1/eebc652417392e73d0a1c24d.jpeg"},{"id":93794640,"identity":"b1b63773-cd7a-4b9c-8503-93cf3e3a42c3","added_by":"auto","created_at":"2025-10-17 15:39:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12880,"visible":true,"origin":"","legend":"\u003cp\u003eThe risk prediction nomogram developed in this study integrates clinical parameters derived from a multivariable logistic regression model, including BMI, Ca/Mg ratio, serum creatinine (Cr), blood potassium (K), blood chloride (Cl), calcium-phosphate product (Ca×IP), and hemoglobin (Hb). Variable-specific scores are standardized based on regression coefficients, with the total score mapped to a risk probability axis (0.01–0.9). The model exhibits robust discrimination (C-index = 0.812).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6849577/v1/50e5df0bf3f8ea90a9861668.png"},{"id":93795128,"identity":"95cd7f2f-1bf9-488e-afcb-7f1f0eae3c0d","added_by":"auto","created_at":"2025-10-17 15:47:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20311,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve derived from the multivariate logistic regression model for predicting nephrolithiasis risk. The area under the curve (AUC) was 0.812 (95% CI: 0.789-0.836, ( P \u0026lt; 0.001 ), indicating a substantial discriminative ability to differentiate between cases and controls. The dotted diagonal line represents the reference line for random chance (AUC=0.5).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6849577/v1/4938cbca1e0967b4c77dfe61.png"},{"id":93794644,"identity":"e525dfb6-b50e-487d-a403-b9585a71f9aa","added_by":"auto","created_at":"2025-10-17 15:39:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7828,"visible":true,"origin":"","legend":"\u003cp\u003eThe calibration curve illustrates strong agreement between model-predicted probabilities and actual probabilities, with a Brier score of 0.177 indicating low overall prediction error. The curve closely approximates the ideal diagonal line (Slope = 1.000, Emax = 0.006), and no significant deviation was observed in the Hosmer-Lemeshow test (p = 0.852). The model demonstrates superior discrimination (C-index = 0.812) and a Dxy value of 0.625, further supporting its clinical utility.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6849577/v1/d3eb3d73456c313c6b8989c1.png"},{"id":93794641,"identity":"33957be1-dbbe-47f4-ad1b-e7c2664b4c7b","added_by":"auto","created_at":"2025-10-17 15:39:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":8630,"visible":true,"origin":"","legend":"\u003cp\u003eThe DCA curve demonstrates acceptable model performance within the threshold probability range of 0.3–0.8, where it lies above both the \"None\" and \"All\" reference lines. The \"All\" line, representing the scenario where all samples are considered positive and receive intervention, displays a net benefit curve with a negative slope. The \"None\" line, indicating no intervention for any sample, yields a net benefit of zero.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6849577/v1/087afbd3d6dbf28fd7e92c43.png"},{"id":98776149,"identity":"e58aa887-23c2-4f76-be70-540f07e71bbe","added_by":"auto","created_at":"2025-12-22 12:22:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1313638,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6849577/v1/15c52ba4-08cd-4b87-a13d-08a1cdfe7892.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Calcium-Magnesium-Phosphate-Potassium Metabolic Network Dysregulation in Nephrolithiasis: Predictive Model Development and Mechanistic Insights","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNephrolithiasis represents a global health challenge, characterized by a 5-year recurrence rate surpassing 50% and imposing substantial clinical and economic burdens[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Calcium-based calculi dominate the epidemiological landscape, comprising 75%-80% of all stone types. Of these, calcium oxalate (CaOx) stones constitute the majority (\u0026gt;\u0026thinsp;60%), while calcium phosphate (CaP) variants account for 15%-20%[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite diagnostic advances in stone composition analysis and 24-hour urinary metabolic profiling, 30%-40% of cases exhibit no identifiable metabolic derangements, creating critical barriers to personalized prevention protocols[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTraditional research has predominantly focused on isolated risk factors (e.g., hypercalciuria, hypocitraturia), while neglecting the synergistic effects of dynamic electrolyte networks involving calcium (Ca), magnesium (Mg), phosphate (P), and potassium (K) in the pathogenesis of nephrolithiasis[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These ions exhibit intricate regulatory crosstalk: magnesium competitively inhibits calcium oxalate crystallization[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and high phosphate intake reduces intestinal calcium-phosphate binding, thereby enhancing calcium absorption and urinary calcium excretion. Low-phosphate diet combined with potassium citrate therapy effectively decreases urinary phosphate excretion and inhibits stone formation[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Potassium imbalance indirectly disrupts calcium-magnesium transport through modulation of Na⁺/K⁺-ATPase activity[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Critically, current risk stratification frameworks fail to systematically incorporate these multidimensional interactions, suggesting that conventional calcium-centric models inadequately capture the pathophysiological complexity of stone formation.\u003c/p\u003e\u003cp\u003eThis hospital-based case-control study aims to evaluate the predictive power of composite indices\u0026mdash;specifically, the calcium-magnesium ratio (Ca/Mg) and the calcium-phosphate product (Ca\u0026times;IP)\u0026mdash;in comparison to traditional single-ion measurements. Our objective is to determine if dysregulation within the calcium-magnesium-phosphate-potassium network is a fundamental mechanism in stone formation, thereby establishing a risk prediction model based on multi-element equilibrium. The outcomes of this research are expected to lay a foundation for tailored nutritional intervention strategies.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Data Sources\u003c/h2\u003e\u003cp\u003e We conducted a hospital-based case-control study at Renmin Hospital of Wuhan University from 2023 to 2025, incorporating participants from both case and control groups. Sociodemographic and laboratory data were retrospectively acquired from the hospital's electronic medical records (EMR). Ethical approval for this retrospective study was granted by the Ethics Committee of Renmin Hospital of Wuhan University (approval number: WDRY2023-K183), which also waived the requirement for written informed consent.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eParticipant Selection\u003c/h3\u003e\n\u003cp\u003eInclusion Criteria for Cases: ① Age\u0026thinsp;\u0026gt;\u0026thinsp;18 years; ② Radiologically confirmed nephrolithiasis (stone diameter\u0026thinsp;\u0026ge;\u0026thinsp;2 mm) by KUB plain film.\u003c/p\u003e\u003cp\u003eExclusion Criteria: History of primary hyperoxaluria, hyperparathyroidism, gastrointestinal disorders/surgery, autoimmune diseases, chronic kidney disease, urinary tract abnormalities/obstruction, malignancies, or other conditions potentially interfering with calcium-phosphate metabolism.\u003c/p\u003e\n\u003ch3\u003eSample Size\u003c/h3\u003e\n\u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, out of the 892 initially eligible nephrolithiasis patients, 281 were excluded due to various factors: metabolic comorbidities (n\u0026thinsp;=\u0026thinsp;148), urological abnormalities (n\u0026thinsp;=\u0026thinsp;83), and other confounders (n\u0026thinsp;=\u0026thinsp;50), yielding 611 patients (405 males, 206 females). A gender-matched control group (624 subjects: 413 males, 211 females) was recruited to minimize sex-related bias.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData processing was conducted using Excel 2021 and SPSS 26.0. The normality of continuous variables was assessed using the Shapiro-Wilk test. Normally distributed data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, while non-normally distributed data are expressed as median (IQR). Intergroup comparisons were performed using independent t-tests for normally distributed data and the Kolmogorov-Smirnov test for non-normal data. Categorical variables are reported as frequency (%) with differences assessed using χ\u0026sup2; tests. Binary logistic regression modeling proceeded in two phases: univariate screening of significant variables (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), followed by forward stepwise multivariate adjustment to calculate adjusted odds ratios (aORs) with 95% confidence intervals (CIs).\u003c/p\u003e\u003cp\u003eStatistical analyses were conducted utilizing R software (4.4.2) and its integrated development environment, incorporating specialized packages for predictive modeling and visualization. The analytical workflow comprised three sequential phases: model construction, performance validation, and clinical application.\u003c/p\u003e\u003cp\u003eFor nomogram development, multivariate regression coefficients were proportionally scaled to establish variable-specific scoring thresholds. Predictor variables were vertically aligned to facilitate point summation. The aggregated score was then mapped to probability estimates through coordinate projection onto the outcome risk axis. Model discrimination was assessed using receiver operating characteristic (ROC) analysis, yielding conventional diagnostic performance metrics, including sensitivity, specificity, and area under the curve (AUC). Predictive calibration was rigorously evaluated through comparisons of observed versus predicted probabilities, employing bootstrap-corrected calibration curves. Clinical utility was assessed using threshold-dependent net benefit quantification across clinically relevant probability ranges through decision curve analysis. Subgroup analyses examined risk heterogeneity based on sex and renal function. All statistical tests were two-tailed, with significance defined at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003eThis study involved 611 patients with nephrolithiasis and 624 matched healthy controls. As presented in Table 1, a comparison of baseline characteristics indicated no statistically significant differences in gender distribution (males: 66.3% in the stone group versus 66.2% in the control group) or median age (both groups: 55 years) (all P \u0026gt; 0.05). Body mass index (BMI) stratification demonstrated a significantly higher proportion of overweight/obesity in the stone group (32.6% vs. 22.8%, \u0026chi;\u0026sup2; = 18.632, P \u0026lt; 0.001), particularly notable in overweight individuals (29.0% vs. 20.4%). Both groups exhibited comparable smoking rates, alcohol consumption, and prevalence of hypertension/diabetes (all P \u0026gt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSerum biochemical analysis identified significantly lower Mg levels in the stone group (0.81 vs. 0.85 mmol/L, P \u0026lt; 0.001) with elevated calcium-magnesium ratio (Ca/Mg: 2.74 vs. 2.63, P \u0026lt; 0.001). Parameters indicative of hepatic function showed reduced ALT (18.00 vs. 20.16 U/L) and AST (20.00 vs. 21.21 U/L) in stone formers (both P \u0026lt; 0.01), though ALT/AST ratio remained comparable. Notably, serum albumin (ALB) was significantly decreased in the stone group (41.90 vs. 43.50 g/L, P \u0026lt; 0.001), while renal function markers demonstrated significant abnormalities: elevated creatinine (Cr: 79.00 vs. 66.00 \u0026mu;mol/L), increased uric acid (UA: 358.00 vs. 337.25 \u0026mu;mol/L), and reduced estimated glomerular filtration rate (eGFR: 91.64 vs. 101.58 mL/min/1.73 m\u0026sup2;) (all P \u0026lt; 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eElectrolyte profiling revealed significantly lower potassium (K: 3.93 vs. 4.10 mmol/L) alongside elevated sodium (140.60 vs. 140.20 mmol/L) and chloride (106.80 vs. 106.10 mmol/L) inthe stone group (all P \u0026lt; 0.05). The calcium-phosphate product (Ca\u0026times;IP: 2.42 vs. 2.26 mmol\u0026sup2;/L\u0026sup2;, P \u0026lt; 0.001) was markedly increased in the stone group. Hematological parameters showed decreased hemoglobin (Hb: 137.14 vs. 140.97 g/L) and hematocrit (HCT: 0.41 vs. 0.42) in the stone group (both P \u0026lt; 0.001). No significant intergroup differences were observed in total serum calcium (Ca), fasting glucose (GLU), or inorganic phosphate (IP) levels (all P \u0026gt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBoth univariate and multivariate logistic regression analyses revealed independent associations between overweight status, elevated calcium-magnesium ratio (Ca/Mg), and nephrolithiasis risk (Table 2). Univariate analysis identified 13 significant predictors including overweight (OR = 1.578), reduced Mg (OR = 0.001), elevated Ca/Mg (OR = 7.983), elevated ALB (OR = 0.871), elevated Cr (OR = 1.037), elevated UA (OR = 1.002), reduced K (OR = 0.166), elevated Cl (OR = 1.103), elevated calcium-phosphate product (Ca\u0026times;IP, OR = 2.128), and reduced Hb (OR = 0.986), all demonstrating statistical significance (P \u0026lt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultivariate logistic regression analysis was performed using forward stepwise selection, incorporating variables that demonstrated significant associations (P\u0026lt;0.05) in preliminary univariate screening. Following adjustments for these significant predictors, seven variables retained statistical significance: overweight (aOR = 1.585, 95% CI: 1.160-2.166), elevated Ca/Mg (aOR = 5.501, 95% CI: 3.105-9.745), elevated Cr (aOR = 1.048, 95% CI: 1.040-1.057), reduced K (aOR = 0.128, 95% CI: 0.083-0.197), elevated Cl (aOR = 1.064, 95% CI: 1.007-1.124), elevated Ca\u0026times;IP (aOR = 1.822, 95% CI: 1.347-2.466), and reduced Hb (aOR = 0.985, 95% CI: 0.976-0.994), with all P \u0026lt; 0.05. Notably, four parameters demonstrated particularly strong clinical associations: Ca/Mg elevation, K reduction, Ca\u0026times;IP elevation, and overweight status exhibited particularly strong risk correlations. No significant associations were observed for obesity (aOR = 1.757, P = 0.158) or underweight status (aOR = 0.630, P = 0.197).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study developed a clinical prediction nomogram (Fig. 2) for visual risk assessment, demonstrating strong discriminative power (C-index = 0.812) and good calibration (Brier score = 0.177). Six key predictors were integrated into the model: calcium-magnesium ratio imbalance, overweight status (BMI \u0026ge;24 kg/m\u0026sup2;), elevated serum creatinine, increased calcium-phosphate product, hypokalemia, and decreased hemoglobin levels. The nomogram enables rapid estimation of individualized risk probabilities by summing scores assigned to each variable. A total score of 240 corresponds to a high-risk probability, providing a practical tool for early identification of high-risk patients and formulation of clinical intervention strategies. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 3, the multivariate model demonstrated robust discriminative capacity with an AUC of 0.812 (95% CI: 0.789-0.836), significantly superior to random classification (P \u0026lt; 0.001). At the Youden index-optimized cutoff, sensitivity and specificity reached 78.9% and 73.6%, respectively. The AUC standard error (0.012) and exclusion of the null value (0.5) from the CI confirmed model robustness, supporting its clinical applicability for risk assessment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study validated the predictive accuracy of the model through calibration curves (Fig. 4), which revealed a high degree of concordance between predicted probabilities and observed outcomes (Brier score = 0.177, calibration slope = 1.000) with no significant systematic deviation (Hosmer-Lemeshow test, p = 0.852). Coupled with excellent discriminative ability (C-index = 0.812), the model exhibited robust performance in risk stratification. Future work should further optimize calibration characteristics through external cohort validation and explore dynamic adjustment mechanisms to enhance clinical applicability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe risk prediction model developed in this study underwent validation for clinical utility through decision curve analysis (DCA, Fig. 5). Within the threshold probability range of 0.3 to 0.8, the model consistently demonstrated substantial positive net benefits and exhibited a clear distinction from the \u0026quot;ALL\u0026quot; and \u0026quot;NONE\u0026quot; reference lines. This suggests its capability to effectively balance false-positive and false-negative risks, thereby enhancing clinical decision-making. Additionally, with an impressive discrimination (C-index = 0.812) and calibration performance (Brier score = 0.177), this model serves as a dependable tool for personalized risk assessment. Future investigations should focus on validating its applicability in external cohorts and examining dynamic threshold adjustment mechanisms to tailor the model to various clinical contexts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubgroup analyses revealed population heterogeneity in the effects of both Ca/Mg ratio and Ca\u0026times;IP product (Table 3). Gender stratification demonstrated stronger associations in females for Ca/Mg (OR = 10.012 vs. males: 6.983; both \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and Ca\u0026times;IP (OR = 2.570 vs. 2.009). Renal function stratification identified the strongest risk associations in the eGFR \u0026gt;90 mL/min/1.73 m\u0026sup2; subgroup: Ca/Mg reached OR = 10.452 (95% CI: 5.402\u0026ndash;20.223), significantly higher than in the 60\u0026ndash;90 mL/min/1.73 m\u0026sup2; group (OR = 4.599; \u003cem\u003eP\u003c/em\u003e = 0.001) and non-significant in the 30\u0026ndash;60 mL/min/1.73 m\u0026sup2; group (P = 0.072). Similarly, Ca\u0026times;IP exhibited maximal effect in the eGFR \u0026gt;90 group (OR = 2.208, P \u0026lt; 0.001), with diminishing significance in lower eGFR subgroups (OR = 2.363, P = 0.293 in 30-60 mL/min/1.73 m\u0026sup2;). This dose-response attenuation suggests that calcium metabolism abnormalities predominantly influence stone formation during the renal compensatory phase (eGFR \u0026gt;60 mL/min/1.73 m\u0026sup2;).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis case-control study systematically elucidates the complex association between serum electrolyte imbalance and nephrolithogenesis. The multifactorial predictive model reveals that Ca/Mg, Ca\u0026times;IP, and serum potassium constitute the core metabolic network of stone formation, with risk associations substantially exceeding those of traditional isolated indicators (e.g., serum calcium/phosphate). These findings provide critical insights for identifying \"metabolically imbalanced but biochemically normal\" individuals in clinical practice.\u003c/p\u003e\u003cp\u003eThe multivariate-adjusted OR for Ca/Mg reached 5.501 (95% CI: 3.105\u0026ndash;9.745), exceeding the modest single-factor association of serum calcium (OR\u0026thinsp;=\u0026thinsp;2.453, P\u0026thinsp;=\u0026thinsp;0.187). This highlights the superior pathological relevance of Ca/Mg equilibrium over absolute ion concentrations. Supporting these results, molecular dynamics simulations conducted by Julie M. Riley's research team demonstrate that magnesium can destabilize calcium oxalate ion pairs and reduce the size of calcium phosphate aggregates in a concentration-dependent manner[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Interestingly, although hypomagnesemia showed strong protective effects in univariate analysis (OR\u0026thinsp;=\u0026thinsp;0.001), its influence diminished when examined within the multivariate framework. This suggests that magnesium deficiency primarily disrupts calcium-magnesium homeostasis rather than acting as an independent risk factor. Consequently, there is a pressing need to reconsider magnesium supplementation approaches. Strategies that integrate monitoring of calcium metabolism alongside targeted interventions in TRPM6/7-mediated Ca/Mg cotransport could enhance therapeutic effectiveness[\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite comparable serum phosphate levels between groups (1.14 vs. 1.11 mmol/L, P\u0026thinsp;=\u0026thinsp;0.103), elevated Ca\u0026times;IP in the stone group (2.42 vs. 2.26 mmol\u0026sup2;/L\u0026sup2;) demonstrated independent risk significance (aOR\u0026thinsp;=\u0026thinsp;1.822). This implies that even with normal-range phosphate, calcium levels\u0026thinsp;\u0026gt;\u0026thinsp;2.35 mmol/L may drive Ca\u0026times;IP beyond crystallization thresholds. Compensatory phosphate reduction under hyperparathyroidism or vitamin D hyperactivity likely maintains elevated Ca\u0026times;IP through calcium-dominated effects, underscoring the clinical value of routine Ca\u0026times;IP monitoring, particularly for individuals with high-normal calcium[\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHypokalemia (aOR\u0026thinsp;=\u0026thinsp;0.128) synergized with Ca/Mg/P dysregulation through triple mechanisms: (1) Metabolic acidosis induction via osteoclast RANKL/OPG signaling activation, increasing bone calcium release[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]; (2) Renal calcium handling disruption, elevating urinary calcium excretion[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]; (3) Mg\u0026sup2;⁺-K⁺ vicious cycle: magnesium deficiency impairs Na⁺/K⁺-ATPase, exacerbating renal potassium wasting[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This \"hypomagnesemia-hypokalemia\" synergy reduces calcium-sensing receptor sensitivity, triggering uncontrolled PTH secretion and establishing a self-reinforcing \"hypercalcemia-hypercalciuria\" loop, explaining potassium's amplified risk effect in multivariate modeling.\u003c/p\u003e\u003cp\u003eThe model integrates metabolic balance indices (Ca/Mg, Ca\u0026times;IP, K⁺) with traditional parameters (BMI, Cl⁻, Cr, Hb) to effectively identify \"covert imbalance\" in calcium metabolism through compensatory mechanisms. Despite comparable serum calcium between groups (2.23 vs. 2.23 mmol/L, P\u0026thinsp;=\u0026thinsp;0.996), Ca/Mg and Ca\u0026times;IP sensitively detected subclinical dysregulation\u0026mdash;minor Mg/IP fluctuations amplified calcium's pathological impact. These composite biomarkers overcome the limitations of single-ion testing, serving as early clinical indicators of metabolic decompensation.\u003c/p\u003e\u003cp\u003ePersistent BMI significance (overweight aOR\u0026thinsp;=\u0026thinsp;1.585) may reflect adipose-endocrine disruption. Specifically, leptin upregulates renal NHE3 (promoting acid load) while suppressing vitamin D activation via PPARγ. This interplay may establish an \"obesity-acidosis-mineral disorder\" axis[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Hyperchloremia (aOR\u0026thinsp;=\u0026thinsp;1.064) and anemia (Hb aOR\u0026thinsp;=\u0026thinsp;0.985) further suggest acid-base imbalance and chronic inflammation indirectly modulate crystallization[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough elevated creatinine retained significance (aOR\u0026thinsp;=\u0026thinsp;1.048), its effect attenuated with declining eGFR (eGFR\u0026thinsp;\u0026gt;\u0026thinsp;90: OR\u0026thinsp;=\u0026thinsp;10.452 vs. eGFR 30\u0026ndash;60: P\u0026thinsp;=\u0026thinsp;0.072). This \"compensatory-phase predominance\" can be attributed to contrasting mechanisms at work. In the early stages of CKD, the kidneys retain their ability to concentrate tubular fluids, leading to a higher risk of calcium-phosphate supersaturation. In contrast, during the later stages of CKD, the development of secondary hyperparathyroidism obscures the influence of primary metabolic factors[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This observation highlights the critical importance of the metabolic network model, particularly for early interventions aimed at leveraging the renal compensatory phase.\u003c/p\u003e\u003cp\u003eThe model achieved an AUC of 0.812 (95% CI: 0.789\u0026ndash;0.836). Its effectiveness stems from two key factors: biological integration (Ca/Mg and Ca\u0026times;IP capture cross-pathway interactions) and mathematical amplification (nonlinear terms enhance early metabolic signals). In this study, the nomogram we developed combines essential indicators from the metabolic network (Ca/Mg, Ca\u0026times;IP) with traditional metrics like BMI and creatinine levels, allowing for a dynamic visual assessment of nephrolithiasis risk (C-index\u0026thinsp;=\u0026thinsp;0.812). One of the standout features of this nomogram is its scoring system for potassium and hemoglobin, which serves to highlight the clinical importance of the \"hypokalemia-anemia-acidosis\" metabolic axis. This provides a practical framework for applying the \"metabolic network theory\" in clinical settings. Calibration curves and decision curve analysis further validated the model\u0026rsquo;s reliability and revealed its clinical net benefit. With this tool, clinicians are empowered to adopt preventive measures for high-risk individuals (score\u0026thinsp;\u0026ge;\u0026thinsp;240) by utilizing interventions informed by metabolic network insights.\u003c/p\u003e\u003cp\u003eThe real breakthrough of this model is its unique ability to uncover hidden metabolic imbalances. For example, it identifies high-risk individuals with normal serum calcium but abnormal Ca/Mg and Ca\u0026times;IP through synergistic interactions within the metabolic network\u0026mdash;a capability unattainable in traditional single-marker systems. This \"individual-normalized but network-dysregulated\" phenomenon may explain recurrent stone formation in patients with \"normal\" conventional test results.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThis study has several limitations. Although Ca/Mg, Ca\u0026times;IP, and serum potassium are core components of the multivariate model, the diagnostic accuracy of this approach still falls short when compared to imaging techniques, particularly urinary tract ultrasonography, which is widely regarded as the gold standard for detecting nephrolithiasis. Moreover, the retrospective nature of this study introduces potential selection bias, which could skew our findings. Furthermore, the absence of 24-hour urinary biochemical data limited in-depth analysis of serum-urine metabolic correlations. Despite these limitations, this research signals a significant shift in the study of nephrolithiasis, moving from a focus on individual factors to a more comprehensive, multifactorial approach, and from isolated metabolic pathways to their complex interconnections. Future prospective cohort studies should integrate metabolomics and ionomics to establish dynamic Ca-Mg-P-K monitoring systems, develop stone recurrence prediction models integrating machine learning algorithms, and clarify spatiotemporal causality in metabolic network dysregulation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e\u003cp\u003eThe authors declare that none of the work reported in this study could have been influenced by any known competing financial interests or personal relationships.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe National Natural Science Foundation of China supported this research (No. 82270797).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe manuscript was developed by YW. Data collecting was done by WL. Data analysis was done by CD. YW drafted the manuscript. SY provided critical review. All authors evaluated and approved the final version.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e\u003cp\u003eData are available onrequest to the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbufaraj M, Xu T, Cao C, Waldhoer T, Seitz C, D\u0026apos;Andrea D, Siyam A, Tarawneh R, Fajkovic H, Schernhammer E, Yang L and Shariat SF (2021) Prevalence and Trends in Kidney Stone Among Adults in the USA: Analyses of National Health and Nutrition Examination Survey 2007-2018 Data. Eur Urol Focus 7(6):1468-1475. http://doi.org/10.1016/j.euf.2020.08.011\u003c/li\u003e\n\u003cli\u003eTan S, Yuan D, Su H, Chen W, Zhu S, Yan B, Sun F, Jiang K and Zhu J (2024) Prevalence of urolithiasis in China: a systematic review and meta-analysis. BJU Int 133(1):34-43. http://doi.org/10.1111/bju.16179\u003c/li\u003e\n\u003cli\u003eCoe FL, Evan A and Worcester E (2005) Kidney stone disease. 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Acta Cir Bras 38:e380223. http://doi.org/10.1590/acb380223\u003c/li\u003e\n\u003cli\u003eHodeify R, Chakkour M, Rida R and Kreydiyyeh S (2021) PGE2 upregulates the Na+/K+ ATPase in HepG2 cells via EP4 receptors and intracellular calcium. PLoS One 16(1):e0245400. http://doi.org/10.1371/journal.pone.0245400\u003c/li\u003e\n\u003cli\u003ePierre SV and Xie Z (2006) The Na,K-ATPase receptor complex: its organization and membership. Cell Biochem Biophys 46(3):303-16. http://doi.org/10.1385/cbb:46:3:303\u003c/li\u003e\n\u003cli\u003eKhan SR, Pearle MS, Robertson WG, Gambaro G, Canales BK, Doizi S, Traxer O and Tiselius HG (2016) Kidney stones. Nat Rev Dis Primers 2:16008. http://doi.org/10.1038/nrdp.2016.8\u003c/li\u003e\n\u003cli\u003eMassey L (2005) Magnesium therapy for nephrolithiasis. Magnes Res 18(2):123-6. \u003c/li\u003e\n\u003cli\u003eLi Q, Krieger NS, Yang L, Asplin J and Bushinsky DA (2024) Magnesium Decreases Urine Supersaturation but Not Calcium Oxalate Stone Formation in Genetic Hypercalciuric Stone-Forming Rats. Nephron 148(7):480-486. http://doi.org/10.1159/000534495\u003c/li\u003e\n\u003cli\u003eSchlingmann KP and Gudermann T (2005) A critical role of TRPM channel-kinase for human magnesium transport. J Physiol 566(Pt 2):301-8. http://doi.org/10.1113/jphysiol.2004.080200\u003c/li\u003e\n\u003cli\u003eSchlingmann KP, Waldegger S, Konrad M, Chubanov V and Gudermann T (2007) TRPM6 and TRPM7--Gatekeepers of human magnesium metabolism. Biochim Biophys Acta 1772(8):813-21. http://doi.org/10.1016/j.bbadis.2007.03.009\u003c/li\u003e\n\u003cli\u003eTavasoli S, Taheri M, Taheri F, Basiri A and Bagheri Amiri F (2019) Evaluating the associations between urinary excretion of magnesium and that of other components in calcium stone-forming patients. Int Urol Nephrol 51(2):279-284. http://doi.org/10.1007/s11255-018-2036-1\u003c/li\u003e\n\u003cli\u003eVittori M, Bove P, Signoretti M, Cipriani C, Gasparoli C, Antonucci M, Carilli M, Maiorino F, Iacovelli V, Petta F, Travaglia S, Panei M, Russo P and Bertolo R (2024) Oral supplementation with probiotics, potassium citrate, and magnesium in reducing crystalluria in stone formers: A phase II study. Urologia 91(4):681-686. http://doi.org/10.1177/03915603241272146\u003c/li\u003e\n\u003cli\u003eIslam AK, Holt S, Reisch J, Nwariaku F, Antonelli J and Maalouf NM (2020) What Predicts Recurrent Kidney Stone after Parathyroidectomy in Patients with Primary Hyperparathyroidism? J Am Coll Surg 231(1):74-82. http://doi.org/10.1016/j.jamcollsurg.2020.04.015\u003c/li\u003e\n\u003cli\u003eKetha H, Singh RJ, Grebe SK, Bergstralh EJ, Rule AD, Lieske JC and Kumar R (2015) Altered Calcium and Vitamin D Homeostasis in First-Time Calcium Kidney Stone-Formers. PLoS One 10(9):e0137350. http://doi.org/10.1371/journal.pone.0137350\u003c/li\u003e\n\u003cli\u003eTaylor EN, Hoofnagle AN and Curhan GC (2015) Calcium and phosphorus regulatory hormones and risk of incident symptomatic kidney stones. Clin J Am Soc Nephrol 10(4):667-75. http://doi.org/10.2215/CJN.07060714\u003c/li\u003e\n\u003cli\u003eZhu Z, Liu M, Zhang Y, Wu J, Gao M, Lei T, Huang F, Chen H and Wu M (2023) Risk factors for the comorbidity of osteoporosis/osteopenia and kidney stones: a cross-sectional study. Arch Osteoporos 18(1):128. http://doi.org/10.1007/s11657-023-01338-3\u003c/li\u003e\n\u003cli\u003eKhalaf RM and Almudhi AA (2022) The effect of vitamin D deficiency on the RANKL/OPG ratio in rats. J Oral Biol Craniofac Res 12(2):228-232. http://doi.org/10.1016/j.jobcr.2022.02.004\u003c/li\u003e\n\u003cli\u003eLiu L, Luo P, Wen P and Xu P (2024) The role of magnesium in the pathogenesis of osteoporosis. Front Endocrinol (Lausanne) 15:1406248. http://doi.org/10.3389/fendo.2024.1406248\u003c/li\u003e\n\u003cli\u003eCaudarella R and Vescini F (2009) Urinary citrate and renal stone disease: the preventive role of alkali citrate treatment. Arch Ital Urol Androl 81(3):182-7. \u003c/li\u003e\n\u003cli\u003eMagni G, Unwin RJ and Moochhala SH (2021) Renal tubular acidosis (RTA) and kidney stones: Diagnosis and management. Arch Esp Urol 74(1):123-128. \u003c/li\u003e\n\u003cli\u003eAronsen JM, Skogestad J, Lewalle A, Louch WE, Hougen K, Stokke MK, Swift F, Niederer S, Smith NP, Sejersted OM and Sjaastad I (2015) Hypokalaemia induces Ca(2)(+) overload and Ca(2)(+) waves in ventricular myocytes by reducing Na(+),K(+)-ATPase alpha(2) activity. J Physiol 593(6):1509-21. http://doi.org/10.1113/jphysiol.2014.279893\u003c/li\u003e\n\u003cli\u003ePirkmajer S and Chibalin AV (2019) Hormonal regulation of Na(+)-K(+)-ATPase from the evolutionary perspective. Curr Top Membr 83:315-351. http://doi.org/10.1016/bs.ctm.2019.01.009\u003c/li\u003e\n\u003cli\u003eWalker V (2019) Phosphaturia in kidney stone formers: Still an enigma. Adv Clin Chem 90:133-196. http://doi.org/10.1016/bs.acc.2019.01.004\u003c/li\u003e\n\u003cli\u003eLovegrove CE, Besevic J, Wiberg A, Lacey B, Littlejohns TJ, Allen NE, Goldsworthy M, Kim J, Hannan FM, Curhan GC, Turney BW, McCarthy MI, Mahajan A, Thakker RV, Holmes MV, Furniss D and Howles SA (2023) Central Adiposity Increases Risk of Kidney Stone Disease through Effects on Serum Calcium Concentrations. J Am Soc Nephrol 34(12):1991-2011. http://doi.org/10.1681/ASN.0000000000000238\u003c/li\u003e\n\u003cli\u003eKadowaki T, Maegawa H, Watada H, Yabe D, Node K, Murohara T and Wada J (2022) Interconnection between cardiovascular, renal and metabolic disorders: A narrative review with a focus on Japan. Diabetes Obes Metab 24(12):2283-2296. http://doi.org/10.1111/dom.14829\u003c/li\u003e\n\u003cli\u003eLindberg JS (2005) Calcimimetics: a new tool for management of hyperparathyroidism and renal osteodystrophy in patients with chronic kidney disease. Kidney Int Suppl (95):S33-6. http://doi.org/10.1111/j.1523-1755.2005.09505.x \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline Characteristics of Nephrolithiasis Patients and Healthy Controls (n=1235)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"110%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003eCase (n=611)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eControl (n=624)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003et/\u0026chi;\u003csup\u003e2\u003c/sup\u003e/Z\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e405 (66.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e413 (66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e206 (33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e211 (33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eAge, year, Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e55 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e55 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eBMI, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e18.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e18 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e36 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e394 (64.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e446 (71.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e177 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e127 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e22 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e15 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSmoke, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e162 (26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e145 (23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eDrink, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e103 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e117 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e181 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e176 (28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e71 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e64 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eCa, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2.23 (0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.23 (0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMg, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.81 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.85 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eCa/Mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2.74 (0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.63 (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eALT, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e18.00 (14.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e20.16 (15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eAST,\u0026nbsp;U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e20.00 (7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e21.21 (7.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eALT/AST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.94 (0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.94 (0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eALB,\u0026nbsp;g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e41.90 (5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e43.50 (4.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eCr, \u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e79.00 (31.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e66.00 (22.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e5.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eUA, \u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e358.00 (148.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e337.25 (124.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eGLU, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4.91 (1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e4.91 (0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eK, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e3.93 (0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e4.10 (0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eNa, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e140.60 (2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e140.20 (2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eCl, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e106.80 (3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e106.10 (3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eIP, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1.14 (0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1.11 (0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eCa\u0026times;IP,\u0026nbsp;mmol2/L2,\u0026nbsp;Mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2.42\u0026plusmn;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.26\u0026plusmn;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e5.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eeGFR,\u0026nbsp;mL/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e91.64 (31.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e101.58 (20.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e5.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eHb,\u0026nbsp;g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e137.14\u0026plusmn;22.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e140.97\u0026plusmn;16.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-4.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eHCT,\u0026nbsp;L/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.41\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.42\u0026plusmn;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-4.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e* Kolmogorov-Smirnov test\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Univariate and Multivariate Binary Logistic Regression Analyses of Nephrolithiasis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"682\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eUnivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eMultivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eCrude OR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003eAdjust OR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.786-1.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eAge, year, Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.994-1.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eBMI, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.346-1.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.312-1.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.210-2.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.160-2.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.849-3.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.803-3.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eSmoke, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.920-1.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eDrink, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.656-1.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.838-1.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.804-1.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eCa, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e2.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.647-9.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eMg, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.001-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eCa/Mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e7.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e4.851-13.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e5.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3.105-9.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eALT, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.993-1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eAST,\u0026nbsp;U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.986-1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eALT/AST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.666-1.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eALB,\u0026nbsp;g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.842-0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eCr, \u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.030-1.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.040-1.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eUA, \u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.001-1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eGLU, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.965-1.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eK, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.114-0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.083-0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eNa, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.006-1.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eCl, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.055-1.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.007-1.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eIP, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.840-2.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eCa\u0026times;IP, mmol2/L2, Mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e2.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.649-2.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.347-2.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eeGFR,\u0026nbsp;mL/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.957-0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eHb,\u0026nbsp;g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.979-0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.976-0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eHCT,\u0026nbsp;L/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.001-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Subgroup Analyses of Calcium-Magnesium Ratio and Calcium-Phosphate Product Stratified by Gender and eGFR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1760708923.png\" width=\"755\" height=\"727\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Nephrolithiasis, Calcium-magnesium ratio, Calcium-phosphate product, Multivariate predictive model, Hypokalemia","lastPublishedDoi":"10.21203/rs.3.rs-6849577/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6849577/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo explore the pathophysiological interplay of calcium-magnesium-phosphate-potassium (Ca-Mg-P-K) metabolic networks in nephrolithiasis and establish an integrated risk prediction framework incorporating homeostatic biomarkers.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis hospital-based case-control study enrolled 611 nephrolithiasis patients and 624 gender-matched controls. Multivariable logistic regression identified core risk determinants, with model performance rigorously validated through ROC analysis, calibration curves, and decision curve analysis (DCA). A nomogram was constructed to enable dynamic risk visualization.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe metabolic network-based model demonstrated exceptional discriminative capacity (AUC\u0026thinsp;=\u0026thinsp;0.812, 95%CI: 0.789\u0026ndash;0.836) and calibration accuracy (Brier score\u0026thinsp;=\u0026thinsp;0.177, Hosmer-Lemeshow p\u0026thinsp;=\u0026thinsp;0.852). Key predictors included calcium-magnesium ratio (Ca/Mg, aOR\u0026thinsp;=\u0026thinsp;5.50), calcium-phosphate product (Ca\u0026times;IP, aOR\u0026thinsp;=\u0026thinsp;1.82), hypokalemia (aOR\u0026thinsp;=\u0026thinsp;0.13), BMI\u0026thinsp;\u0026ge;\u0026thinsp;24, and hemoglobin reduction. The nomogram quantified individualized risk through synergistic scoring, with a score of 240 points identifying high-risk patients (sensitivity\u0026thinsp;=\u0026thinsp;78.9%, specificity\u0026thinsp;=\u0026thinsp;73.6%). Subgroup analyses revealed amplified risks in females (Ca/Mg OR\u0026thinsp;=\u0026thinsp;10.01 vs 6.98) and those in the renal compensatory phase (eGFR\u0026thinsp;\u0026gt;\u0026thinsp;90 group OR\u0026thinsp;=\u0026thinsp;10.45). DCA validated the model's clinical utility, revealing net benefit superiority over traditional approaches across 0.3\u0026ndash;0.8 risk thresholds.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study establishes calcium-magnesium ratio (Ca/Mg) and calcium-phosphate product (Ca\u0026times;IP) as key indicators of subclinical calcium dysregulation. The nomogram integrates metabolic network interactions to overcome single-marker limitations, with hypokalemia identified as a critical risk amplifier. This model enables early detection of network-level imbalance despite normal individual parameters, offering a clinically actionable tool for personalized nephrolithiasis prevention.\u003c/p\u003e","manuscriptTitle":"Calcium-Magnesium-Phosphate-Potassium Metabolic Network Dysregulation in Nephrolithiasis: Predictive Model Development and Mechanistic Insights","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 15:39:12","doi":"10.21203/rs.3.rs-6849577/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fdd9c29d-4bf5-4889-8474-95ed8a485034","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-20T18:38:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 15:39:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6849577","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6849577","identity":"rs-6849577","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}