The negative association between C-reactive protein-albumin-lymphocyte (CALLY) index and kidney stone: a cross‑sectional study

The negative association between C-reactive protein-albumin-lymphocyte (CALLY) index and kidney stone: a cross‑sectional study | 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 The negative association between C-reactive protein-albumin-lymphocyte (CALLY) index and kidney stone: a cross‑sectional study Jiaqing Yang, Yuanzhuo Du, Ju Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5865840/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 Background The C-reactive protein-albumin-lymphocyte (CALLY) index is a novel composite biomarker that reflects the body's immune response, nutritional state, and inflammatory response. However, no studies have reported the correlation between CALLY and kidney stones. This study aims to determine the correlation between CALLY and kidney stones. Methods Data from the 2007-2010 NHANES surveys were analyzed in this cross-sectional study. A weighted multivariable logistic regression model and smooth curve fitting were employed to examine the correlation between CALLY and kidney stones. Subgroup analyses and interaction assessments were subsequently performed to confirm the robustness of the results. Results Among 10,938 participants aged 18 years and older, 9.14% were diagnosed with kidney stones. The results demonstrated a notable inverse relationship between elevated CALLY and the prevalence of kidney stones. Specifically, after performing a natural logarithmic transformation of the CALLY index, the adjusted model showed that with each one-unit rise in lnCALLY, the risk of kidney stones decreased by 21.5% (OR = 0.785; 95% CI: 0.643-0.959; P = 0.01756). Subgroup analyses confirmed the consistency of this relationship across all cohorts, unaffected by stratifying variables. Curve fitting and threshold effect analysis revealed a U-shaped association between CALLY and the risk of kidney stones, with the inflection point at -0.48, showing a significant P-value (< 0.001). Conclusion This study identifies a negative correlation between CALLY and the prevalence of kidney stones, characterized by a U-shaped curve. These results indicate the potential of CALLY as a valuable mark for identifying kidney stones. NHANES C-reactive protein-albumin-lymphocyte index CALLY Inflammation Nutrition Kidney stone Cross-sectional study Figures Figure 1 Figure 2 Figure 3 Background Kidney stones result from the abnormal accumulation of crystallized substances in the kidneys and have become one of the most common urinary tract stone diseases worldwide[ 1 ]. Estimates suggest the worldwide prevalence of kidney stones ranges from 7.2–7.7%, with a steady increase observed annually. In North America, the prevalence ranges from 7–13%, while in South America, it is approximately 4%. In Europe, the prevalence is between 5% and 10%, and in Asia, it varies widely, ranging from 1–19%[ 2 ]. Suppose kidney stones are not treated promptly and effectively. In that case, they may lead to severe consequences, such as permanent renal damage and even a greater risk of progression to end-stage renal disease[ 3 ]. Kidney stones are increasingly recognized as a systemic chronic disease, imposing significant economic and public health burdens [ 4 ]. Therefore, identifying potential risk factors for kidney stones and implementing early clinical interventions and treatments have become essential for improving kidney stone management. The formation of kidney stones is closely related to various factors, with their pathogenesis considered to involve multiple interrelated mechanisms, including chronic inflammation and immune responses. These factors significantly elevate the risk of stone formation[ 5 ]. Recently, there has been growing interest in the impact of nutritional factors on kidney stone formation, with different nutrients playing vital roles in preventing and managing kidney stones[ 6 ]. Serum albumin, a clinical indicator reflecting nutritional status, is closely associated with renal metabolic function and often participates in kidney stone formation by interacting with calcium oxalate crystals[ 7 ]. Beyond its role in evaluating nutrition, albumin indirectly indicates systemic inflammatory response[ 8 ]. Previous clinical studies have also emphasized specific Inflammatory markers, including C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR), whose elevated levels may indicate local chronic or acute inflammation in the kidneys[ 9 ]. Inflammatory responses promote the deposition of minerals in urine, thereby increasing the probability of kidney stone occurrence by inducing calcium salt deposition or calcium oxalate crystal formation, accompanied by the activation of reactive oxygen species (ROS) and inflammasomes[ 10 ]. At the same time, peripheral blood lymphocytes are generally regarded as key components of immune responses, and excessive immune activation may result in a reduced lymphocyte count [ 11 ]. In routine clinical practice, albumin, lymphocytes, and CRP indicate inflammation and immune status. Therefore, combining these biomarkers should offer better reproducibility and accuracy compared to individual biomarkers. The CALLY index is a composite biomarker that includes albumin (which represents the state of nutrition), lymphocyte count (which indicates immune function), and CRP (a classic marker of inflammation)[ 12 ]. There is considerable evidence suggesting that CALLY, as a novel clinical biomarker, can predict the prognosis and diagnosis of various diseases. For example, CALLY has been linked to the prognosis of individuals with colorectal cancer and hepatocellular carcinoma, outperforming other inflammation-based biomarkers[ 13 , 14 ]. CALLY has also been identified as a prognostic factor for lung cancer. Patients with a low CALLY index demonstrate markedly worse overall survival than those with a high index[ 15 ]. Furthermore, CALLY is crucial in developing cytokine storms and the progression of systemic inflammatory diseases in patients with severe fever and thrombocytopenia[ 16 ]. Although existing studies have clearly revealed the correlation between CALLY and multiple diseases, research on the relationship between CALLY and kidney stones remains scarce. As a comprehensive assessment tool, CALLY may offer an innovative perspective for monitoring kidney stones. Therefore, this research seeks to investigate the potential application of CALLY in kidney stones, offering a reference for developing clinical intervention strategies. Methods Study Population The data for this research were obtained from the 2007–2010 NHANES surveys, as these two cycles are the only ones that include data on kidney stones and CRP. A cross-sectional study design was employed. The study included 20,686 participants in total. Exclusion criteria included the following: (A) participants under 18 years of age (n = 7,931); (B) missing CALLY data (n = 1,293); (C) missing kidney stone data (n = 524). Ultimately, data from 10,938 participants were analyzed, meeting the inclusion criteria for complete case analysis, as shown in Fig. 1 . Assessment of CALLY The exposure variables in this study were derived from three biomarkers: albumin, CRP, and lymphocyte count. CRP levels from NHANES 2007–2010, not high-sensitivity CRP, were quantified using the latex-enhanced turbidimetric method on a Behring turbidity meter. Lymphocyte count was determined using the Beckman Coulter® counting method, and serum albumin levels were determined by the Roche Cobas 6000 analyzer with the bromocresol purple reagent. Detailed information is available on the NHANES website. The CALLY index was determined by the formula [(Albumin in g/L) * (Lymphocyte count in 1000 cells/µL)] / (CRP in mg/dL), offering a thorough assessment of both inflammatory and nutritional health[ 12 ]. The CALLY was log-transformed in regression analysis to improve the interpretability of the odds ratios (ORs). Assessment of kidney stones Kidney stones were determined based on self-reported data from participants. Data were collected from the kidney condition urology file, specifically the KIQ026 questionnaire. In the questionnaire, participants were asked, "Have you ever had kidney stones?" Participants who responded with "yes" were categorized as having a past history of renal stones. The data obtained represent the prevalence of kidney stones in participants, revealing whether they had ever experienced kidney stones during their lifetime instead of the incidence of newly diagnosed cases during the study period. Covariates Several potential confounders were selected as covariates in this study to enhance the reliability of the model. The key variables assessed include age, gender, race/ethnicity, an education level (less than high school, high school, and beyond), marital status (categorized as single, married, cohabitating, widowed, divorced, or separated), family income-to-poverty ratio (PIR: 3.5), BMI (BMI, kg/m²), smoking status (Yes/ No, or ever smoked ≥ 100 cigarettes), alcohol consumption (yes, no, or had at least 12 alcoholic drinks per year), diabetes (FPG ≥ 7.0 mmol/L or HbA1c ≥ 6.5%), hypertension (defined as an average systolic blood pressure > 130 mmHg or average diastolic blood pressure > 80 mmHg), gout (Yes/ No), coronary heart disease (Yes/No), and stroke (Yes/No). Laboratory indicators include albumin, creatinine, uric acid, HDL cholesterol, C-reactive protein, lymphocytes, monocytes, neutrophils, and platelets. Data for all variables in this research utilized the NHANES database, ensuring comprehensiveness and consistency. Statistical Analysis Considering the intricate, multi-stage sampling procedure design of NHANES. Correspondingly, the statistical description included continuous (mean ± standard deviation) and categorical (percentages) variables, employing weighted t -tests and weighted chi-square tests. Multivariable logistic regression models evaluated the independent association between the CALLY and kidney stones. Different adjustment levels were incorporated for potential confounding factors: Model 1 was unadjusted for covariates, Model 2 had adjustments for sex, age, and race, and Model 3 was designed considering all the enrolled variables. Threshold analysis identified key turning points, while smooth curve fitting was applied to assess the nonlinear link between CALLY and kidney stones. The variability across various subgroups was examined through subgroup analyses and interaction tests to confirm the findings' robustness. The statistical analyses used R software (version 4.4.2) and EmpowerStats(version 2.0). P -values < 0.05 were considered to be statistically significant. Results Characteristics of the Participants This study included 10,938 participants. The average age was 50.01 ± 17.77 years. Among the participants, 48.82% were male and 51.18% were female. Participants were grouped based on whether or not they had a history of kidney stones. Table 1 presents the baseline characteristics for different groups. Among the participants, 1,000 reported a personal history of kidney stones. Significant differences in baseline characteristics were observed between the kidney stone and non-kidney stone groups, except for lymphocyte levels (P > 0.05). Characteristics of kidney stone participants generally included a higher likelihood of being male, older, non-Hispanic white, married or living with a partner, and having a higher PIR. Table 1 Baseline Characteristics of the Study Population Characteristics Total (N = 10,938) Normal (N = 9,938) Kidney stones (N = 1000) P -value Age (years) 50.01 ± 17.77 46.36 ± 16.61 53.55 ± 15.63 < 0.0001 Gender, (%) < 0.0001 Male 48.82 47.33 59.02 Female 51.18 52.67 40.98 Race/ethnicity, (%) < 0.0001 Mexican American 18.05 8.86 5.05 Other Hispanic 10.76 5.06 4.60 Non-Hispanic White 48.39 68.35 81.22 Non-Hispanic Black 17.92 10.94 5.22 Other Races 4.88 6.79 3.91 Education level, (%) 0.0019 Less than high school 29.56 19.53 19.49 High school or GED 50.55 53.14 58.16 Above high school 19.89 27.33 22.34 Marital status, (%) < 0.0001 Married or living with partners 60.43 63.96 69.34 Widowed, divorced, or separated 22.96 18.04 21.51 Never married 16.61 18.00 9.16 PIR, (%) 0.0049 3.5 27.13 40.42 38.01 Smoking status, (%) 0.0086 Yes 46.93 45.54 49.91 No 53.07 54.46 50.09 Alcohol use, (%) 0.0007 Yes 12.92 11.24 14.87 No 87.08 88.76 85.13 Hypertension, (%) < 0.0001 Yes 35.05 28.53 46.19 No 64.95 71.47 53.81 Diabetes, (%) < 0.0001 Yes 11.90 7.56 15.88 No 88.10 92.44 84.12 Gout, (%) < 0.0001 Yes 4.66 3.22 9.02 No 95.34 96.78 90.98 Coronary heart disease, (%) < 0.0001 Yes 4.15 2.86 7.27 No 95.85 97.14 92.73 Stroke, (%) < 0.0001 Yes 3.79 2.54 5.75 No 96.21 97.46 94.25 Creatinine, mg/dL 0.90 ± 0.42 0.87 ± 0.31 0.94 ± 0.50 < 0.0001 Uric acid, mg/dL 5.50 ± 1.46 5.44 ± 1.41 5.78 ± 1.50 < 0.0001 Albumin, g/dL 4.23 ± 0.34 4.27 ± 0.34 4.22 ± 0.34 < 0.0001 Lymphocyte (1000 cells/dL) 2.15 ± 1.11 2.14 ± 0.93 2.09 ± 0.79 0.1190 Platelet (1000 cells/dL) 252.83 ± 68.14 254.13 ± 65.95 249.72 ± 71.32 0.0466 Monocyte (1000 cells/dL) 0.55 ± 0.19 0.55 ± 0.19 0.57 ± 0.18 0.0006 Neutrophils (1000 cells/dL) 4.30 ± 1.93 4.28 ± 1.69 4.50 ± 2.09 0.0001 BMI, kg/m2 29.04 ± 6.65 28.50 ± 6.58 30.04 ± 6.47 < 0.0001 HDL, mg/dL 52.25 ± 16.12 52.95 ± 16.41 48.65 ± 14.73 < 0.0001 C-reactive protein, mg/dL 0.43 ± 0.82 0.38 ± 0.75 0.45 ± 0.78 0.0093 CALLY 10.68 ± 23.96 12.15 ± 21.69 9.15 ± 14.77 < 0.0001 lnCALLY 1.50 ± 1.34 1.65 ± 1.34 1.39 ± 1.31 < 0.0001 CALLY, C-reactive protein-albumin-lymphocyte ; PIR, Income-to-Poverty ratio; BMI, Body mass index; HDL, High-density lipoprotein; CRP, C-reactive protein Association between CALLY and Kidney Stones Table 2 presents the results from the weighted multivariable logistic regression models. Since CALLY is not normally distributed, a logarithmic transformation (ln) was applied during the analysis. According to the model results, a marked negative correlation was observed between the CALLY and the occurrence of kidney stones. The negative association was consistently validated across three adjusted models using lnCALLY as a continuous variable. This negative association remained significant in Model 3 after full adjustment (OR = 0.946; 95% CI: 0.898–0.997; P = 0.03874). This indicates that with each one-unit rise in lnCALLY, the likelihood of having kidney stones decreases by 5.4%. Furthermore, when lnCALLY was categorized into quartiles, the positive correlation with kidney stones remained. Further analysis revealed that participants in the highest quartile (Q4) of CALLY had a markedly lower risk of kidney stones (OR = 0.785; 95% CI: 0.643–0.959; P = 0.01756), with a 21.5% lower risk of kidney stone than participants in the lowest quartile. The trend p-values for all three models were less than 0.01. Table 2 Associations between CALLY and kidney stones lnCALLY OR (95% CI), P -value Model 1 Model 2 Model 3 Continuous 0.894 (0.852, 0.938) < 0.00001 0.918 (0.872, 0.965) 0.00090 0.946 (0.898, 0.997) 0.03874 Categories Q1 1.0(Ref.) 1.0(Ref.) 1.0(Ref.) Q2 0.932 (0.783, 1.109) 0.42502 0.919 (0.770, 1.097) 0.34947 0.972 (0.813, 1.163) 0.75861 Q3 0.776 (0.647, 0.930) 0.00595 0.767 (0.638, 0.922) 0.00478 0.830 (0.688, 1.002) 0.05239 Q4 0.653 (0.541, 0.788) < 0.0001 0.703 (0.578, 0.853) 0.00037 0.785 (0.643, 0.959) 0.01756 P for trend < 0.001 < 0.001 < 0.001 OR: odds ratio, 95%CI: 95% confidence interval Model 1: unadjusted Model 2: adjusted for age, gender, and race/ethnicity Model 3: adjusted for all variables, including age, gender, race/ethnicity, educational level, marital status, ratio of family income to poverty, smoking status, alcohol use, hypertension, diabetes, creatinine, uric acid, gout, coronary heart disease, stroke Nonlinear and Saturation Effect Analysis of CALLY and Kidney Stones Smoothing curve fitting results confirmed a nonlinear relationship between lnCALLY and kidney stones, revealing a statistically significant inverted U-shape, as shown in Fig. 2 . To further clarify this relationship, threshold effect analysis was conducted (Table 3 ). The likelihood ratio test (P = 0.011) indicated significant differences between two segmented linear models. The turning point for the relationship between CALLY and kidney stones was − 0.48. Above this threshold, the OR was 0.91 (95% CI: 0.86–0.97; P = 0.0026), while below this threshold, the OR was 1.40 (95% CI: 1.00-1.95; P = 0.0506), the association between CALLY and kidney stones was no longer reach statistical significance below this threshold. These findings indicate that the effects of CALLY on the prevalence of kidney stones differ above and below this threshold. Table 3 Threshold effect analysis of CALLY on kidney stone using a linear regression model Threshold effect analysis Kidney stones OR (95%CI) P -value lnCALLY Fitting by a standard linear model 0.95 (0.90, 1.00) 0.0387 Fitting by two-piecewise linear model The inflection point of CALLY(K) -0.48 CALLY K 0.91 (0.86, 0.97) 0.0026 P for log-likelihood ratio 0.011 Adjusted for all variables, including age, gender, race/ethnicity, educational level, marital status, ratio of family income to poverty, smoking status, alcohol use, hypertension, diabetes, creatinine, uric acid, gout, coronary heart disease, stroke OR, odds ratio; CI, confidence interval Subgroup Analysis Subgroup analyses and interaction tests were conducted to explore whether the relationship between CALLY and kidney stones was consistent across different population groups. The results in Fig. 3 show that none of the interaction tests yielded significant results (P for interaction > 0.05), suggesting that the link between the CALLY index and kidney stones remains consistent across the specified stratifying variables. This indicates that no significant interactions were found between these factors, and they did not influence the negative relationship between CALLY and kidney stones. These differences were not statistically significant. Discussion This research utilized NHANES data from 2007 to 2010, establishing a negative correlation between the CALLY and the rising occurrence of kidney stones in individuals aged 18 and above. It is hypothesized that CALLY may serve as a predictor for kidney stone formation, and modulating the factors associated with CALLY could potentially reduce the incidence of kidney stones. First, the results of the adjusted models for relevant confounders demonstrated a statistically significant inverse relationship between CALLY and the likelihood of kidney stones in both continuous and categorical variable models. Lower levels of CALLY were connected to an elevated risk, a relationship consistent in the fully adjusted model. Subgroup analyses confirmed this association's consistency and the findings' validity, with no significant interactions from potential confounding factors. Finally, smoothing curve fitting analysis revealed an inverted U-shaped correlation between CALLY and kidney stones. The risk of kidney stones was highest at the turning point of -0.48. At the right side of the turning point, the OR was 0.91 (95% CI: 0.86–0.97; P = 0.0026), indicating a continued negative correlation between CALLY and kidney stones; however, to the left of the turning point, the OR was 1.40 (95% CI: 1.00-1.95; P = 0.0506), where the correlation became statistically insignificant. These findings have profound implications for clinical interventions and management strategies for kidney stones. This study demonstrates a close relationship between CALLY and kidney stones, with CALLY serving as an innovative nutritional-inflammatory immune score system that reflects factors related to inflammation, immune function, and nutritional status in kidney stone patients. Although limited research has examined the link between CALLY and kidney stones, similar results have been reported in prior research examining CALLY and other diseases. Initially, CALLY was developed as an index combining multiple clinical parameters, and early studies used it to assess prognosis in various malignancies, including colorectal cancer, which supplements the limitations of TNM staging in prognostic prediction.[ 13 ]. The study found that lower CALLY scores were associated with a higher incidence of postoperative complications. Our study further validated this conclusion with a larger sample size. Furthermore, CALLY has been shown to have significant clinical value in the perioperative and tumor management of patients with a diagnosis of non-small cell lung cancer and gastric cancer. Malnutrition, chronic inflammation, and dysfunction of the immune system are major contributing factors driving tumor progression and impacting patient outcomes[ 15 , 17 ]. Additionally, a significant negative correlation has been found between CALLY and all-cause mortality and cardiovascular-related deaths in the elderly, where lower CALLY scores are associated with worse survival outcomes in older adults [ 18 ]. Studies have also demonstrated a notable link between CALLY and the likelihood of developing cardiorenal syndrome, highlighting the crucial role of inflammation and immune deficiencies in exacerbating kidney damage and contributing to renal insufficiency[ 19 ]. While these studies confirm the association between CALLY and various diseases, the specific mechanisms linking CALLY with the incidence of kidney stones remain unclear. Several potential mechanisms have been proposed to explain the connection between the formation of kidney stones and a range of contributing factors. Extensive research has established a strong relationship between kidney stone formation and systemic disturbances, with inflammation, oxidative stress, and immune response playing complex roles in kidney stone pathogenesis[ 4 , 20 ]. Increased expression of genes associated with inflammation, immunity, and complement activation pathways has been observed in the renal tissue of kidney stone patients, where inflammation not only facilitates the onset of the disease but also actively participates in its progression[ 5 ]. C-reactive protein (CRP), a pattern recognition molecule in the inflammatory process, indicates the degree of inflammation[ 21 ]. As a biomarker of local or systemic inflammation, high CRP levels have been shown to correlate with clinical symptoms of kidney stones and stone size, with serum CRP levels significantly associated with self-reported kidney stone history[ 22 ]. Moreover, in multiple studies examining CRP as a novel inflammatory biomarker, CRP is elevated in chronic inflammatory diseases such as rheumatoid arthritis, certain cardiovascular diseases, and infections[ 23 ]. Additionally, CRP is a known predictor for chronic kidney disease (CKD), with elevated serum CRP levels correlating with the incidence and mortality of CKD. High CRP levels are likely to promote the infiltration of inflammatory cells and the release of cytokines from diseased kidneys, leading to progressive renal inflammation and fibrosis[ 24 ]. Additionally, increasing evidence suggests that oxidative stress often accompanies the inflammatory response. Elevated levels of oxalate in the kidneys can induce oxidative stress, and during crystal deposition, inflammation is typically localized to the renal tissue surrounding the crystal deposits[ 25 ]. Systemic oxidative stress and inflammatory factors can damage endothelial cells, increasing the risk of atherosclerosis leading to endothelial dysfunction and the progression of atherosclerosis[ 26 ]. Patients often develop disturbances in calcium-phosphate metabolism as atherosclerosis progresses, leading to vascular calcification and calcium salt deposition through renal interstitial tissues, which may play a crucial role in kidney stone formation[ 27 ]. Therefore, as clinically relevant inflammatory indicators, identifying and monitoring inflammation biomarkers such as CRP can offer a broader perspective for evaluating the inflammatory burden of patients, providing early insights into stone formation and related complications. Earlier research has demonstrated that, in addition to the effects of inflammation on kidney stone formation, immune balance also has a crucial and intricate role in kidney stone development. Lymphocytes, as key components of the immune response, mainly govern the immune system's ability to respond specifically to infections and foreign agents and to execute immune defense mechanisms[ 28 ]. Mao et al. found significant correlations between kidney stones and several lymphocyte-related indices, such as SII, NLR, and MLR. Only NLR remained significantly associated with the occurrence and the number of stones passed after controlling for other confounders [ 29 ]. NLR, the ratio of neutrophils to lymphocytes, has emerged as a novel inflammatory and immune-related measure with established value in predicting the risk of kidney diseases and metabolic syndromes[ 30 ]. An elevated NLR primarily reflects the imbalance between increased neutrophils and decreased lymphocytes, indicating immune dysregulation and ongoing systemic inflammation. Higher NLR is strongly associated with uric acid stones and their accompanying CKD complications, playing a significant role in CKD related to uric acid stones[ 31 ]. The formation and progression of kidney stones are often driven by the interaction of specific immune activation and inflammatory responses, which shape the stone microenvironment. Excessive immune activation leads to a reduction in lymphocyte count, which in turn lowers the CALLY index. In the presence of inflammation, CRP, lymphocytes, and neutrophil concentrations fluctuate[ 32 ]. Furthermore, some studies indicate that lymphocytes contribute significantly to the development of various types and stages of acute kidney injury through immune responses triggered by the recognition of specific antigens[ 33 ]. In conclusion, future research should concentrate on the biochemical and molecular dynamics of the immune system with urolithiasis to help develop new preventive and therapeutic strategies and create more precise intervention measures. Serum albumin is a key marker for assessing nutritional status. It is primarily synthesized in the liver and plays an essential role in transporting nutrients in the blood and maintaining blood volume balance[ 34 , 35 ]. Previous studies have indicated that malnutrition is a significant risk factor for kidney stones, and nutritional influences are crucial in stone formation. Proper dietary management can help regulate urinary tract risks and reduce the likelihood of stone formation[ 6 ]. Malnutrition and systemic inflammation response can impair serum albumin production, which is why malnourished patients typically exhibit low serum albumin levels[ 36 ]. The Prognostic Nutritional Index (PNI) is determined by serum albumin levels, and lymphocyte counts in peripheral blood serve as a marker reflecting both nutritional and immune status in patients[ 37 ]. Initially used to assess cancer prognosis, PNI has since been recognized for its role in preventing and managing urological diseases, advancing a more comprehensive understanding of the multifactorial origins of chronic kidney disease and kidney stones[ 38 , 39 ]. A study by Cardoso demonstrated a significant correlation between PNI and inflammatory markers such as CRP[ 40 ]. The CALLY index, combining PNI and CRP, provides a more comprehensive assessment of nutritional status and inflammatory condition, offering a more precise clinical evaluation of patient health. Therefore, we propose that the CALLY could serve as a comprehensive clinical marker, as the components required to calculate it—CRP, albumin, and lymphocyte count—are readily available through routine clinical examinations. It is a simple calculation and is highly practical, and it could potentially quantify the impact of a nutritional-immune-inflammation scoring system on kidney stone formation. Our research highlights the association between elevated CALLY levels and kidney stone prevalence, providing evidence for early detection and promoting effective intervention measures and preventive strategies. Future studies should further investigate the relevant physiological and biological mechanisms to offer prospective insights for the clinical management of kidney stones. Strengths and Limitations This study presents several strengths. First, screening a large sample and applying multiple statistical methods, this innovative study is the first to investigate the connection between the CALLY index and kidney stones, thereby enhancing our comprehension of the connection between inflammation, immune response, and kidney stone formation. Second, the consistency of the results was validated through stratified analyses of the study population. Furthermore, adjustments were made for other potential confounders to enhance the reliability of the data. However, certain limitations inherent to this study must be acknowledged. First, the study's cross-sectional design of the study restricts the ability to determine causal relationships. Second, despite the rigorous control of confounding factors in the multivariable logistic regression analysis aimed at assessing the prevalence of kidney stones, some unmeasured potential confounders may still have influenced the results. Finally, as the study primarily involves participants from the United States, whether the same patterns apply to broader populations remains to be explored in future research. Conclusion This study confirms that CALLY, a novel inflammatory-immune-nutritional composite biomarker, correlates significantly negatively with kidney stones. This biomarker is an innovative scoring system closely associated with kidney stones' pathogenesis. The relationship between CALLY and kidney stones should be prioritized in widespread screening programs that could support early detection of high-risk individuals, ultimately improving the prevention and treatment of kidney stones. Abbreviations NHANES National Health and Nutrition Examination Survey CALLY index C-reactive protein-albumin-lymphocyte index CRP C-reactive protein HDL High-density lipoprotein ESR Erythrocyte sedimentation rate FPG Fasting plasma glucose BMI Body mass index CVD Cardiovascular disease CKD Chronic kidney disease PIR Income-to-Poverty ratio OR Odds ratio CI Confidential interval ROS Reactive oxygen species Declarations Acknowledgements Acknowledgement is given to the NHANES databases for granting access to these valuable databases. Conflict of interest The authors have disclosed that there are no conflicts of interest. Ethics approval and consent to participate The research involving human subjects received approval from the NCHS Research Ethics Review Board (ERB). In line with national laws and institutional guidelines, this study did not necessitate written informed consent from participants. Clinical trial number Not applicable Consent for publication Not applicable. Funding No funding. Author Contribution YJQ, DYZ: Contributed to paper design data processing, drafted the manuscript and involved in data collection. GJ: Revised the manuscript. Data Availability The database utilized in this study is available in the NHANES: https://wwwn.cdc.gov/nchs/nhanes/. References Pak CY (1998) Kidney stones. Lancet (London England) 351(9118):1797–1801 Abufaraj M, Xu T, Cao C, Waldhoer T, Seitz C, D'Andrea D, Siyam A, Tarawneh R, Fajkovic H, Schernhammer E et al (2021) Prevalence and Trends in Kidney Stone Among Adults in the USA: Analyses of National Health and Nutrition Examination Survey 2007–2018 Data. 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J Med Virol 95(2):e28546 Okugawa Y, Ohi M, Kitajima T, Higashi K, Sato Y, Yamashita S, Uratani R, Shimura T, Imaoka H, Kawamura M et al (2024) Clinical feasibility of the preoperative C-reactive protein-albumin-lymphocyte index to predict short- and long-term outcomes of patients with gastric cancer. J Gastrointest surgery: official J Soc Surg Aliment Tract 28(7):1045–1050 Luo L, Li M, Xi Y, Hu J, Hu W (2024) C-reactive protein-albumin-lymphocyte index as a feasible nutrition-immunity-inflammation marker of the outcome of all-cause and cardiovascular mortality in elderly. Clin Nutr ESPEN 63:346–353 Xu Z, Tang J, Xin C, Jin Y, Zhang H, Liang R (2024) Associations of C-reactive protein-albumin-lymphocyte (CALLY) index with cardiorenal syndrome: Insights from a population-based study. Heliyon 10(17):e37197 Soligo M, Morlacco A, Zattoni F, Valotto C, Beltrami GDEG (2022) Metabolic syndrome and stone disease. Panminerva Med 64(3):344–358 Wang AY, Woo J, Lam CW, Wang M, Sea MM, Lui SF, Li PK, Sanderson J (2003) Is a single time point C-reactive protein predictive of outcome in peritoneal dialysis patients? J Am Soc Nephrology: JASN 14(7):1871–1879 Shoag J, Eisner BH (2014) Relationship between C-reactive protein and kidney stone prevalence. J Urol 191(2):372–375 Sproston NR, Ashworth JJ (2018) Role of C-Reactive Protein at Sites of Inflammation and Infection. Front Immunol 9:754 Li J, Chen J, Lan HY, Tang Y (2023) Role of C-Reactive Protein in Kidney Diseases. Kidney Dis (Basel Switzerland) 9(2):73–81 Khan SR (2013) Reactive oxygen species as the molecular modulators of calcium oxalate kidney stone formation: evidence from clinical and experimental investigations. J Urol 189(3):803–811 Gianfrancesco MA, Paquot N, Piette J, Legrand-Poels S (2018) Lipid bilayer stress in obesity-linked inflammatory and metabolic disorders. Biochem Pharmacol 153:168–183 Huang HS, Liao PC, Liu CJ (2020) Calcium Kidney Stones are Associated with Increased Risk of Carotid Atherosclerosis: The Link between Urinary Stone Risks, Carotid Intima-Media Thickness, and Oxidative Stress Markers. J Clin Med 9(3) Wang YP, Xie Y, Ma H, Su SA, Wang YD, Wang JA, Xiang MX (2016) Regulatory T lymphocytes in myocardial infarction: A promising new therapeutic target. Int J Cardiol 203:923–928 Mao W, Wu J, Zhang Z, Xu Z, Xu B, Chen M (2021) Neutrophil-lymphocyte ratio acts as a novel diagnostic biomarker for kidney stone prevalence and number of stones passed. Translational Androl Urol 10(1):77–86 Buonacera A, Stancanelli B, Colaci M, Malatino L (2022) Neutrophil to Lymphocyte Ratio: An Emerging Marker of the Relationships between the Immune System and Diseases. Int J Mol Sci 23(7) Tung HT, Liu CM, Huang HS, Lu ZH, Liu CJ (2023) Increased risk of chronic kidney disease in uric acid stone formers with high neutrophil-to-lymphocyte ratio. Sci Rep 13(1):17686 Yuan D, Yang J, Wu W, Amier Y, Li X, Wan W, Huang Y, Li J, Yu X (2024) Inflammatory cytokines and their potential role in kidney stone disease: a Mendelian randomization study. Int Urol Nephrol 56(10):3249–3257 Weller S, Varrier M, Ostermann M (2017) Lymphocyte Function in Human Acute Kidney Injury. Nephron 137(4):287–293 Fanali G, di Masi A, Trezza V, Marino M, Fasano M, Ascenzi P (2012) Human serum albumin: from bench to bedside. Mol Aspects Med 33(3):209–290 Moshage HJ, Janssen JA, Franssen JH, Hafkenscheid JC, Yap SH (1987) Study of the molecular mechanism of decreased liver synthesis of albumin in inflammation. J Clin Investig 79(6):1635–1641 Streng KW, Hillege HL, Ter Maaten JM, van Veldhuisen DJ, Dickstein K, Ng LL, Samani NJ, Metra M, Ponikowski P, Cleland JG et al (2022) Clinical implications of low estimated protein intake in patients with heart failure. J cachexia sarcopenia muscle 13(3):1762–1770 Ellez HI, Keskinkilic M, Semiz HS, Arayici ME, Kısa E, Oztop I (2023) The Prognostic Nutritional Index (PNI): A New Biomarker for Determining Prognosis in Metastatic Castration-Sensitive Prostate Carcinoma. J Clin Med 12(17) Barutcu Atas D, Tugcu M, Asicioglu E, Velioglu A, Arikan H, Koc M, Tuglular S (2022) Prognostic nutritional index is a predictor of mortality in elderly patients with chronic kidney disease. Int Urol Nephrol 54(5):1155–1162 Zeković M, Krga I, Bumbaširević U (2024) Editorial: Nutrition and urological disorders: the crossroads of contemporary research and clinical perspective. Front Nutr 11:1394600 Cardoso CRL, Leite NC, Salles GF (2021) Importance of hematological parameters for micro- and macrovascular outcomes in patients with type 2 diabetes: the Rio de Janeiro type 2 diabetes cohort study. Cardiovasc Diabetol 20(1):133 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-5865840","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":405344779,"identity":"2f1e553c-ae85-4eda-b79d-0bb48be2c280","order_by":0,"name":"Jiaqing Yang","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqing","middleName":"","lastName":"Yang","suffix":""},{"id":405344780,"identity":"132653af-9c28-43df-a449-9ae688f29ad7","order_by":1,"name":"Yuanzhuo Du","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Yuanzhuo","middleName":"","lastName":"Du","suffix":""},{"id":405344781,"identity":"b64f919f-025d-42bb-aa6c-d28ccc13f467","order_by":2,"name":"Ju Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYFCCBIYPDAY2dvaHmQ9ABA4Q1sI4g6EiLZnhOFsCKVrOHGZsOM9jQJwWc/Ycw2beNmZmxmaebw9+tjHI8d1IYPxcgEeLZc8bkBY2PmZm3u2GvW0MxpI3EpilZ+DRYnAjx/wxbxsPMxsz7zZpxjaGxA03EtiYefBrAdkiwdjDzPMMpKWeOC08ZwwYZzDzsIG0JBgQ1HLmWWHjnIqEZANmNjPJnnMShjPPPGyWxqvlePLGhjcG/+0M+A8/k/hRZiPPdzz54Gd8WhgYOAyYkBRIADFjA14NDAzsDxh/EFAyCkbBKBgFIxwAACSlSmnf2POzAAAAAElFTkSuQmCC","orcid":"","institution":"Nanchang University","correspondingAuthor":true,"prefix":"","firstName":"Ju","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2025-01-20 12:23:26","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5865840/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5865840/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74589984,"identity":"c9774177-99da-4759-a8da-a603c72ac8fd","added_by":"auto","created_at":"2025-01-23 17:34:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":239174,"visible":true,"origin":"","legend":"\u003cp\u003eThe participant flow diagram.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-5865840/v1/cb544516a1f7e75ee2cedea4.png"},{"id":74590566,"identity":"de9f8586-fcee-49a4-bea0-5142212712e2","added_by":"auto","created_at":"2025-01-23 17:42:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":340126,"visible":true,"origin":"","legend":"\u003cp\u003eThe nonlinear associations between the CALLY index and Kidney stones.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-5865840/v1/cb4000f124c0dde34583ab72.png"},{"id":74589987,"identity":"b938ebf5-d523-41e2-adbe-2768227ee986","added_by":"auto","created_at":"2025-01-23 17:34:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3534755,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the association between CALLY index and Kidney stones.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-5865840/v1/04f6c541049374918dfe2bed.png"},{"id":74592072,"identity":"a64817cf-a388-440c-a864-0679f4ab1cb0","added_by":"auto","created_at":"2025-01-23 18:06:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5185399,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5865840/v1/e76221d3-ee17-4167-b522-22814d463d19.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The negative association between C-reactive protein-albumin-lymphocyte (CALLY) index and kidney stone: a cross‑sectional study","fulltext":[{"header":"Background","content":"\u003cp\u003eKidney stones result from the abnormal accumulation of crystallized substances in the kidneys and have become one of the most common urinary tract stone diseases worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Estimates suggest the worldwide prevalence of kidney stones ranges from 7.2\u0026ndash;7.7%, with a steady increase observed annually. In North America, the prevalence ranges from 7\u0026ndash;13%, while in South America, it is approximately 4%. In Europe, the prevalence is between 5% and 10%, and in Asia, it varies widely, ranging from 1\u0026ndash;19%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Suppose kidney stones are not treated promptly and effectively. In that case, they may lead to severe consequences, such as permanent renal damage and even a greater risk of progression to end-stage renal disease[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Kidney stones are increasingly recognized as a systemic chronic disease, imposing significant economic and public health burdens [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, identifying potential risk factors for kidney stones and implementing early clinical interventions and treatments have become essential for improving kidney stone management.\u003c/p\u003e \u003cp\u003eThe formation of kidney stones is closely related to various factors, with their pathogenesis considered to involve multiple interrelated mechanisms, including chronic inflammation and immune responses. These factors significantly elevate the risk of stone formation[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recently, there has been growing interest in the impact of nutritional factors on kidney stone formation, with different nutrients playing vital roles in preventing and managing kidney stones[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Serum albumin, a clinical indicator reflecting nutritional status, is closely associated with renal metabolic function and often participates in kidney stone formation by interacting with calcium oxalate crystals[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Beyond its role in evaluating nutrition, albumin indirectly indicates systemic inflammatory response[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Previous clinical studies have also emphasized specific Inflammatory markers, including C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR), whose elevated levels may indicate local chronic or acute inflammation in the kidneys[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Inflammatory responses promote the deposition of minerals in urine, thereby increasing the probability of kidney stone occurrence by inducing calcium salt deposition or calcium oxalate crystal formation, accompanied by the activation of reactive oxygen species (ROS) and inflammasomes[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. At the same time, peripheral blood lymphocytes are generally regarded as key components of immune responses, and excessive immune activation may result in a reduced lymphocyte count [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In routine clinical practice, albumin, lymphocytes, and CRP indicate inflammation and immune status. Therefore, combining these biomarkers should offer better reproducibility and accuracy compared to individual biomarkers.\u003c/p\u003e \u003cp\u003eThe CALLY index is a composite biomarker that includes albumin (which represents the state of nutrition), lymphocyte count (which indicates immune function), and CRP (a classic marker of inflammation)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. There is considerable evidence suggesting that CALLY, as a novel clinical biomarker, can predict the prognosis and diagnosis of various diseases. For example, CALLY has been linked to the prognosis of individuals with colorectal cancer and hepatocellular carcinoma, outperforming other inflammation-based biomarkers[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. CALLY has also been identified as a prognostic factor for lung cancer. Patients with a low CALLY index demonstrate markedly worse overall survival than those with a high index[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Furthermore, CALLY is crucial in developing cytokine storms and the progression of systemic inflammatory diseases in patients with severe fever and thrombocytopenia[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Although existing studies have clearly revealed the correlation between CALLY and multiple diseases, research on the relationship between CALLY and kidney stones remains scarce. As a comprehensive assessment tool, CALLY may offer an innovative perspective for monitoring kidney stones. Therefore, this research seeks to investigate the potential application of CALLY in kidney stones, offering a reference for developing clinical intervention strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThe data for this research were obtained from the 2007\u0026ndash;2010 NHANES surveys, as these two cycles are the only ones that include data on kidney stones and CRP. A cross-sectional study design was employed. The study included 20,686 participants in total. Exclusion criteria included the following: (A) participants under 18 years of age (n\u0026thinsp;=\u0026thinsp;7,931); (B) missing CALLY data (n\u0026thinsp;=\u0026thinsp;1,293); (C) missing kidney stone data (n\u0026thinsp;=\u0026thinsp;524). Ultimately, data from 10,938 participants were analyzed, meeting the inclusion criteria for complete case analysis, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of CALLY\u003c/h3\u003e\n\u003cp\u003eThe exposure variables in this study were derived from three biomarkers: albumin, CRP, and lymphocyte count. CRP levels from NHANES 2007\u0026ndash;2010, not high-sensitivity CRP, were quantified using the latex-enhanced turbidimetric method on a Behring turbidity meter. Lymphocyte count was determined using the Beckman Coulter\u0026reg; counting method, and serum albumin levels were determined by the Roche Cobas 6000 analyzer with the bromocresol purple reagent. Detailed information is available on the NHANES website. The CALLY index was determined by the formula [(Albumin in g/L) * (Lymphocyte count in 1000 cells/\u0026micro;L)] / (CRP in mg/dL), offering a thorough assessment of both inflammatory and nutritional health[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The CALLY was log-transformed in regression analysis to improve the interpretability of the odds ratios (ORs).\u003c/p\u003e\n\u003ch3\u003eAssessment of kidney stones\u003c/h3\u003e\n\u003cp\u003eKidney stones were determined based on self-reported data from participants. Data were collected from the kidney condition urology file, specifically the KIQ026 questionnaire. In the questionnaire, participants were asked, \"Have you ever had kidney stones?\" Participants who responded with \"yes\" were categorized as having a past history of renal stones. The data obtained represent the prevalence of kidney stones in participants, revealing whether they had ever experienced kidney stones during their lifetime instead of the incidence of newly diagnosed cases during the study period.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eSeveral potential confounders were selected as covariates in this study to enhance the reliability of the model. The key variables assessed include age, gender, race/ethnicity, an education level (less than high school, high school, and beyond), marital status (categorized as single, married, cohabitating, widowed, divorced, or separated), family income-to-poverty ratio (PIR: \u0026lt; 1.3, 1.3\u0026ndash;3.5, \u0026gt; 3.5), BMI (BMI, kg/m\u0026sup2;), smoking status (Yes/ No, or ever smoked\u0026thinsp;\u0026ge;\u0026thinsp;100 cigarettes), alcohol consumption (yes, no, or had at least 12 alcoholic drinks per year), diabetes (FPG\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L or HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%), hypertension (defined as an average systolic blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;130 mmHg or average diastolic blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;80 mmHg), gout (Yes/ No), coronary heart disease (Yes/No), and stroke (Yes/No). Laboratory indicators include albumin, creatinine, uric acid, HDL cholesterol, C-reactive protein, lymphocytes, monocytes, neutrophils, and platelets. Data for all variables in this research utilized the NHANES database, ensuring comprehensiveness and consistency.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eConsidering the intricate, multi-stage sampling procedure design of NHANES. Correspondingly, the statistical description included continuous (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation) and categorical (percentages) variables, employing weighted \u003cem\u003et\u003c/em\u003e-tests and weighted chi-square tests. Multivariable logistic regression models evaluated the independent association between the CALLY and kidney stones. Different adjustment levels were incorporated for potential confounding factors: Model 1 was unadjusted for covariates, Model 2 had adjustments for sex, age, and race, and Model 3 was designed considering all the enrolled variables. Threshold analysis identified key turning points, while smooth curve fitting was applied to assess the nonlinear link between CALLY and kidney stones. The variability across various subgroups was examined through subgroup analyses and interaction tests to confirm the findings' robustness. The statistical analyses used R software (version 4.4.2) and EmpowerStats(version 2.0). \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to be statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the Participants\u003c/h2\u003e \u003cp\u003eThis study included 10,938 participants. The average age was 50.01\u0026thinsp;\u0026plusmn;\u0026thinsp;17.77 years. Among the participants, 48.82% were male and 51.18% were female. Participants were grouped based on whether or not they had a history of kidney stones. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics for different groups. Among the participants, 1,000 reported a personal history of kidney stones. Significant differences in baseline characteristics were observed between the kidney stone and non-kidney stone groups, except for lymphocyte levels (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Characteristics of kidney stone participants generally included a higher likelihood of being male, older, non-Hispanic white, married or living with a partner, and having a higher PIR.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of the Study Population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;10,938)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal (N\u0026thinsp;=\u0026thinsp;9,938)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKidney stones (N\u0026thinsp;=\u0026thinsp;1000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.01\u0026thinsp;\u0026plusmn;\u0026thinsp;17.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.36\u0026thinsp;\u0026plusmn;\u0026thinsp;16.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.55\u0026thinsp;\u0026plusmn;\u0026thinsp;15.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/ethnicity, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Races\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or living with partners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed, divorced, or separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.3\u0026ndash;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol use, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGout, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (1000 cells/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet (1000 cells/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252.83\u0026thinsp;\u0026plusmn;\u0026thinsp;68.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254.13\u0026thinsp;\u0026plusmn;\u0026thinsp;65.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e249.72\u0026thinsp;\u0026plusmn;\u0026thinsp;71.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte (1000 cells/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils (1000 cells/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.04\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.50\u0026thinsp;\u0026plusmn;\u0026thinsp;6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.04\u0026thinsp;\u0026plusmn;\u0026thinsp;6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.25\u0026thinsp;\u0026plusmn;\u0026thinsp;16.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.95\u0026thinsp;\u0026plusmn;\u0026thinsp;16.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.65\u0026thinsp;\u0026plusmn;\u0026thinsp;14.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCALLY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.68\u0026thinsp;\u0026plusmn;\u0026thinsp;23.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.15\u0026thinsp;\u0026plusmn;\u0026thinsp;21.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.15\u0026thinsp;\u0026plusmn;\u0026thinsp;14.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnCALLY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eCALLY, C-reactive protein-albumin-lymphocyte ; PIR, Income-to-Poverty ratio; BMI, Body mass index; HDL, High-density lipoprotein; CRP, C-reactive protein\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation between CALLY and Kidney Stones\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results from the weighted multivariable logistic regression models. Since CALLY is not normally distributed, a logarithmic transformation (ln) was applied during the analysis. According to the model results, a marked negative correlation was observed between the CALLY and the occurrence of kidney stones. The negative association was consistently validated across three adjusted models using lnCALLY as a continuous variable. This negative association remained significant in Model 3 after full adjustment (OR\u0026thinsp;=\u0026thinsp;0.946; 95% CI: 0.898\u0026ndash;0.997; P\u0026thinsp;=\u0026thinsp;0.03874). This indicates that with each one-unit rise in lnCALLY, the likelihood of having kidney stones decreases by 5.4%. Furthermore, when lnCALLY was categorized into quartiles, the positive correlation with kidney stones remained. Further analysis revealed that participants in the highest quartile (Q4) of CALLY had a markedly lower risk of kidney stones (OR\u0026thinsp;=\u0026thinsp;0.785; 95% CI: 0.643\u0026ndash;0.959; P\u0026thinsp;=\u0026thinsp;0.01756), with a 21.5% lower risk of kidney stone than participants in the lowest quartile. The trend p-values for all three models were less than 0.01.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between CALLY and kidney stones\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnCALLY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eOR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.894 (0.852, 0.938)\u0026thinsp;\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.918 (0.872, 0.965) 0.00090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.946 (0.898, 0.997) 0.03874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.932 (0.783, 1.109) 0.42502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.919 (0.770, 1.097) 0.34947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.972 (0.813, 1.163) 0.75861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.776 (0.647, 0.930) 0.00595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.767 (0.638, 0.922) 0.00478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.830 (0.688, 1.002) 0.05239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.653 (0.541, 0.788)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.703 (0.578, 0.853) 0.00037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.785 (0.643, 0.959) 0.01756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eOR: odds ratio, 95%CI: 95% confidence interval\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 1: unadjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 2: adjusted for age, gender, and race/ethnicity\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 3: adjusted for all variables, including age, gender, race/ethnicity, educational level, marital status, ratio of family income to poverty, smoking status, alcohol use, hypertension, diabetes, creatinine, uric acid, gout, coronary heart disease, stroke\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNonlinear and Saturation Effect Analysis of CALLY and Kidney Stones\u003c/h2\u003e \u003cp\u003eSmoothing curve fitting results confirmed a nonlinear relationship between lnCALLY and kidney stones, revealing a statistically significant inverted U-shape, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. To further clarify this relationship, threshold effect analysis was conducted (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The likelihood ratio test (P\u0026thinsp;=\u0026thinsp;0.011) indicated significant differences between two segmented linear models. The turning point for the relationship between CALLY and kidney stones was \u0026minus;\u0026thinsp;0.48. Above this threshold, the OR was 0.91 (95% CI: 0.86\u0026ndash;0.97; P\u0026thinsp;=\u0026thinsp;0.0026), while below this threshold, the OR was 1.40 (95% CI: 1.00-1.95; P\u0026thinsp;=\u0026thinsp;0.0506), the association between CALLY and kidney stones was no longer reach statistical significance below this threshold. These findings indicate that the effects of CALLY on the prevalence of kidney stones differ above and below this threshold.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold effect analysis of CALLY on kidney stone using a linear regression model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eThreshold effect analysis Kidney stones\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnCALLY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitting by a standard linear model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.90, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitting by two-piecewise linear model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe inflection point of CALLY(K)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCALLY\u0026thinsp;\u0026lt;\u0026thinsp;K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.40 (1.00, 1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0506\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCALLY\u0026thinsp;\u0026gt;\u0026thinsp;K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.86, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for log-likelihood ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAdjusted for all variables, including age, gender, race/ethnicity, educational level, marital status, ratio of family income to poverty, smoking status, alcohol use, hypertension, diabetes, creatinine, uric acid, gout, coronary heart disease, stroke\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eOR, odds ratio; CI, confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup Analysis\u003c/h2\u003e \u003cp\u003eSubgroup analyses and interaction tests were conducted to explore whether the relationship between CALLY and kidney stones was consistent across different population groups. The results in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show that none of the interaction tests yielded significant results (P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that the link between the CALLY index and kidney stones remains consistent across the specified stratifying variables. This indicates that no significant interactions were found between these factors, and they did not influence the negative relationship between CALLY and kidney stones. These differences were not statistically significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis research utilized NHANES data from 2007 to 2010, establishing a negative correlation between the CALLY and the rising occurrence of kidney stones in individuals aged 18 and above. It is hypothesized that CALLY may serve as a predictor for kidney stone formation, and modulating the factors associated with CALLY could potentially reduce the incidence of kidney stones. First, the results of the adjusted models for relevant confounders demonstrated a statistically significant inverse relationship between CALLY and the likelihood of kidney stones in both continuous and categorical variable models. Lower levels of CALLY were connected to an elevated risk, a relationship consistent in the fully adjusted model. Subgroup analyses confirmed this association's consistency and the findings' validity, with no significant interactions from potential confounding factors. Finally, smoothing curve fitting analysis revealed an inverted U-shaped correlation between CALLY and kidney stones. The risk of kidney stones was highest at the turning point of -0.48. At the right side of the turning point, the OR was 0.91 (95% CI: 0.86\u0026ndash;0.97; P\u0026thinsp;=\u0026thinsp;0.0026), indicating a continued negative correlation between CALLY and kidney stones; however, to the left of the turning point, the OR was 1.40 (95% CI: 1.00-1.95; P\u0026thinsp;=\u0026thinsp;0.0506), where the correlation became statistically insignificant. These findings have profound implications for clinical interventions and management strategies for kidney stones.\u003c/p\u003e \u003cp\u003eThis study demonstrates a close relationship between CALLY and kidney stones, with CALLY serving as an innovative nutritional-inflammatory immune score system that reflects factors related to inflammation, immune function, and nutritional status in kidney stone patients. Although limited research has examined the link between CALLY and kidney stones, similar results have been reported in prior research examining CALLY and other diseases. Initially, CALLY was developed as an index combining multiple clinical parameters, and early studies used it to assess prognosis in various malignancies, including colorectal cancer, which supplements the limitations of TNM staging in prognostic prediction.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The study found that lower CALLY scores were associated with a higher incidence of postoperative complications. Our study further validated this conclusion with a larger sample size. Furthermore, CALLY has been shown to have significant clinical value in the perioperative and tumor management of patients with a diagnosis of non-small cell lung cancer and gastric cancer. Malnutrition, chronic inflammation, and dysfunction of the immune system are major contributing factors driving tumor progression and impacting patient outcomes[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, a significant negative correlation has been found between CALLY and all-cause mortality and cardiovascular-related deaths in the elderly, where lower CALLY scores are associated with worse survival outcomes in older adults [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Studies have also demonstrated a notable link between CALLY and the likelihood of developing cardiorenal syndrome, highlighting the crucial role of inflammation and immune deficiencies in exacerbating kidney damage and contributing to renal insufficiency[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. While these studies confirm the association between CALLY and various diseases, the specific mechanisms linking CALLY with the incidence of kidney stones remain unclear. Several potential mechanisms have been proposed to explain the connection between the formation of kidney stones and a range of contributing factors.\u003c/p\u003e \u003cp\u003eExtensive research has established a strong relationship between kidney stone formation and systemic disturbances, with inflammation, oxidative stress, and immune response playing complex roles in kidney stone pathogenesis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Increased expression of genes associated with inflammation, immunity, and complement activation pathways has been observed in the renal tissue of kidney stone patients, where inflammation not only facilitates the onset of the disease but also actively participates in its progression[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. C-reactive protein (CRP), a pattern recognition molecule in the inflammatory process, indicates the degree of inflammation[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As a biomarker of local or systemic inflammation, high CRP levels have been shown to correlate with clinical symptoms of kidney stones and stone size, with serum CRP levels significantly associated with self-reported kidney stone history[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Moreover, in multiple studies examining CRP as a novel inflammatory biomarker, CRP is elevated in chronic inflammatory diseases such as rheumatoid arthritis, certain cardiovascular diseases, and infections[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, CRP is a known predictor for chronic kidney disease (CKD), with elevated serum CRP levels correlating with the incidence and mortality of CKD. High CRP levels are likely to promote the infiltration of inflammatory cells and the release of cytokines from diseased kidneys, leading to progressive renal inflammation and fibrosis[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, increasing evidence suggests that oxidative stress often accompanies the inflammatory response. Elevated levels of oxalate in the kidneys can induce oxidative stress, and during crystal deposition, inflammation is typically localized to the renal tissue surrounding the crystal deposits[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Systemic oxidative stress and inflammatory factors can damage endothelial cells, increasing the risk of atherosclerosis leading to endothelial dysfunction and the progression of atherosclerosis[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Patients often develop disturbances in calcium-phosphate metabolism as atherosclerosis progresses, leading to vascular calcification and calcium salt deposition through renal interstitial tissues, which may play a crucial role in kidney stone formation[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, as clinically relevant inflammatory indicators, identifying and monitoring inflammation biomarkers such as CRP can offer a broader perspective for evaluating the inflammatory burden of patients, providing early insights into stone formation and related complications.\u003c/p\u003e \u003cp\u003eEarlier research has demonstrated that, in addition to the effects of inflammation on kidney stone formation, immune balance also has a crucial and intricate role in kidney stone development. Lymphocytes, as key components of the immune response, mainly govern the immune system's ability to respond specifically to infections and foreign agents and to execute immune defense mechanisms[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Mao et al. found significant correlations between kidney stones and several lymphocyte-related indices, such as SII, NLR, and MLR. Only NLR remained significantly associated with the occurrence and the number of stones passed after controlling for other confounders [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. NLR, the ratio of neutrophils to lymphocytes, has emerged as a novel inflammatory and immune-related measure with established value in predicting the risk of kidney diseases and metabolic syndromes[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. An elevated NLR primarily reflects the imbalance between increased neutrophils and decreased lymphocytes, indicating immune dysregulation and ongoing systemic inflammation. Higher NLR is strongly associated with uric acid stones and their accompanying CKD complications, playing a significant role in CKD related to uric acid stones[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The formation and progression of kidney stones are often driven by the interaction of specific immune activation and inflammatory responses, which shape the stone microenvironment. Excessive immune activation leads to a reduction in lymphocyte count, which in turn lowers the CALLY index. In the presence of inflammation, CRP, lymphocytes, and neutrophil concentrations fluctuate[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Furthermore, some studies indicate that lymphocytes contribute significantly to the development of various types and stages of acute kidney injury through immune responses triggered by the recognition of specific antigens[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In conclusion, future research should concentrate on the biochemical and molecular dynamics of the immune system with urolithiasis to help develop new preventive and therapeutic strategies and create more precise intervention measures.\u003c/p\u003e \u003cp\u003eSerum albumin is a key marker for assessing nutritional status. It is primarily synthesized in the liver and plays an essential role in transporting nutrients in the blood and maintaining blood volume balance[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Previous studies have indicated that malnutrition is a significant risk factor for kidney stones, and nutritional influences are crucial in stone formation. Proper dietary management can help regulate urinary tract risks and reduce the likelihood of stone formation[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Malnutrition and systemic inflammation response can impair serum albumin production, which is why malnourished patients typically exhibit low serum albumin levels[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The Prognostic Nutritional Index (PNI) is determined by serum albumin levels, and lymphocyte counts in peripheral blood serve as a marker reflecting both nutritional and immune status in patients[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Initially used to assess cancer prognosis, PNI has since been recognized for its role in preventing and managing urological diseases, advancing a more comprehensive understanding of the multifactorial origins of chronic kidney disease and kidney stones[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. A study by Cardoso demonstrated a significant correlation between PNI and inflammatory markers such as CRP[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The CALLY index, combining PNI and CRP, provides a more comprehensive assessment of nutritional status and inflammatory condition, offering a more precise clinical evaluation of patient health.\u003c/p\u003e \u003cp\u003eTherefore, we propose that the CALLY could serve as a comprehensive clinical marker, as the components required to calculate it\u0026mdash;CRP, albumin, and lymphocyte count\u0026mdash;are readily available through routine clinical examinations. It is a simple calculation and is highly practical, and it could potentially quantify the impact of a nutritional-immune-inflammation scoring system on kidney stone formation. Our research highlights the association between elevated CALLY levels and kidney stone prevalence, providing evidence for early detection and promoting effective intervention measures and preventive strategies. Future studies should further investigate the relevant physiological and biological mechanisms to offer prospective insights for the clinical management of kidney stones.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThis study presents several strengths. First, screening a large sample and applying multiple statistical methods, this innovative study is the first to investigate the connection between the CALLY index and kidney stones, thereby enhancing our comprehension of the connection between inflammation, immune response, and kidney stone formation. Second, the consistency of the results was validated through stratified analyses of the study population. Furthermore, adjustments were made for other potential confounders to enhance the reliability of the data.\u003c/p\u003e \u003cp\u003eHowever, certain limitations inherent to this study must be acknowledged. First, the study's cross-sectional design of the study restricts the ability to determine causal relationships. Second, despite the rigorous control of confounding factors in the multivariable logistic regression analysis aimed at assessing the prevalence of kidney stones, some unmeasured potential confounders may still have influenced the results. Finally, as the study primarily involves participants from the United States, whether the same patterns apply to broader populations remains to be explored in future research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study confirms that CALLY, a novel inflammatory-immune-nutritional composite biomarker, correlates significantly negatively with kidney stones. This biomarker is an innovative scoring system closely associated with kidney stones' pathogenesis. The relationship between CALLY and kidney stones should be prioritized in widespread screening programs that could support early detection of high-risk individuals, ultimately improving the prevention and treatment of kidney stones.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNHANES National Health and Nutrition Examination Survey\u003c/p\u003e\u003cp\u003eCALLY index C-reactive protein-albumin-lymphocyte index\u003c/p\u003e\u003cp\u003eCRP C-reactive protein\u003c/p\u003e\u003cp\u003eHDL High-density lipoprotein\u003c/p\u003e\u003cp\u003eESR Erythrocyte sedimentation rate\u003c/p\u003e\u003cp\u003eFPG Fasting plasma glucose\u003c/p\u003e\u003cp\u003eBMI Body mass index\u003c/p\u003e\u003cp\u003eCVD Cardiovascular disease\u003c/p\u003e\u003cp\u003eCKD Chronic kidney disease\u003c/p\u003e\u003cp\u003ePIR Income-to-Poverty ratio\u003c/p\u003e\u003cp\u003eOR Odds ratio\u003c/p\u003e\u003cp\u003eCI Confidential interval\u003c/p\u003e\u003cp\u003eROS Reactive oxygen species\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcknowledgement is given to the NHANES databases for granting access to these valuable databases.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors have disclosed that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research involving human subjects received approval from the NCHS Research Ethics Review Board (ERB). In line with national laws and institutional guidelines, this study did not necessitate written informed consent from participants.\u003c/p\u003e\n\u003ch2\u003eClinical trial number\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eYJQ, DYZ: Contributed to paper design data processing, drafted the manuscript and involved in data collection. GJ: Revised the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe database utilized in this study is available in the NHANES: https://wwwn.cdc.gov/nchs/nhanes/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePak CY (1998) Kidney stones. 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Mol Aspects Med 33(3):209\u0026ndash;290\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoshage HJ, Janssen JA, Franssen JH, Hafkenscheid JC, Yap SH (1987) Study of the molecular mechanism of decreased liver synthesis of albumin in inflammation. J Clin Investig 79(6):1635\u0026ndash;1641\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStreng KW, Hillege HL, Ter Maaten JM, van Veldhuisen DJ, Dickstein K, Ng LL, Samani NJ, Metra M, Ponikowski P, Cleland JG et al (2022) Clinical implications of low estimated protein intake in patients with heart failure. J cachexia sarcopenia muscle 13(3):1762\u0026ndash;1770\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllez HI, Keskinkilic M, Semiz HS, Arayici ME, Kısa E, Oztop I (2023) The Prognostic Nutritional Index (PNI): A New Biomarker for Determining Prognosis in Metastatic Castration-Sensitive Prostate Carcinoma. J Clin Med 12(17)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarutcu Atas D, Tugcu M, Asicioglu E, Velioglu A, Arikan H, Koc M, Tuglular S (2022) Prognostic nutritional index is a predictor of mortality in elderly patients with chronic kidney disease. Int Urol Nephrol 54(5):1155\u0026ndash;1162\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeković M, Krga I, Bumbaširević U (2024) Editorial: Nutrition and urological disorders: the crossroads of contemporary research and clinical perspective. Front Nutr 11:1394600\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCardoso CRL, Leite NC, Salles GF (2021) Importance of hematological parameters for micro- and macrovascular outcomes in patients with type 2 diabetes: the Rio de Janeiro type 2 diabetes cohort study. Cardiovasc Diabetol 20(1):133\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":true,"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":"NHANES, C-reactive protein-albumin-lymphocyte index, CALLY, Inflammation, Nutrition, Kidney stone, Cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-5865840/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5865840/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe C-reactive protein-albumin-lymphocyte (CALLY) index is a novel composite biomarker that reflects the body's immune response, nutritional state, and inflammatory response. However, no studies have reported the correlation between CALLY and kidney stones. This study aims to determine the correlation between CALLY and kidney stones.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from the 2007-2010 NHANES surveys were analyzed in this cross-sectional study. A weighted multivariable logistic regression model and smooth curve fitting were employed to examine the correlation between CALLY and kidney stones. Subgroup analyses and interaction assessments were subsequently performed to confirm the robustness of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 10,938 participants aged 18 years and older, 9.14% were diagnosed with kidney stones. The results demonstrated a notable inverse relationship between elevated CALLY and the prevalence of kidney stones. Specifically, after performing a natural logarithmic transformation of the CALLY index, the adjusted model showed that with each one-unit rise in lnCALLY, the risk of kidney stones decreased by 21.5% (OR = 0.785; 95% CI: 0.643-0.959; P = 0.01756). Subgroup analyses confirmed the consistency of this relationship across all cohorts, unaffected by stratifying variables. Curve fitting and threshold effect analysis revealed a U-shaped association between CALLY and the risk of kidney stones, with the inflection point at -0.48, showing a significant P-value (\u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study identifies a negative correlation between CALLY and the prevalence of kidney stones, characterized by a U-shaped curve. These results indicate the potential of CALLY as a valuable mark for identifying kidney stones.\u003c/p\u003e","manuscriptTitle":"The negative association between C-reactive protein-albumin-lymphocyte (CALLY) index and kidney stone: a cross‑sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-23 17:34:32","doi":"10.21203/rs.3.rs-5865840/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":"0cc74d40-048a-4c93-acc8-75550bfd7b66","owner":[],"postedDate":"January 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-22T11:38:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-23 17:34:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5865840","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5865840","identity":"rs-5865840","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}