This population-based study drew upon data from the NHANES, a comprehensive survey conducted by the National Center for Health Statistics of the Centers for Disease Control and Prevention. NHANES employed a rigorous randomized, stratified, multi-stage survey methodology to ensure nationwide representation. Participants underwent thorough physical examinations, health, and nutrition questionnaires, as well as laboratory assessments (16, 17). The NHANES study protocol received approval from the Ethics Review Board of the National Center for Health Statistics. Detailed design and data from this study could be accessed at https://www.cdc.gov/nchs/nhanes/. The current study included a total of 28,878 eligible participants, obtained by consolidating data from the NHANES cycles: 2007–2008, 2009–2010, 2011–2012, 2013-2014, 2015-2016, and 2017-2018, encompassing 59,842 participants. All participants were aged between 20 and 79 years, were not pregnant, had complete data of MHR, and provided comprehensive questionnaire records on kidney stone.

The MHR, serving as an exposure variable, is defined as the quotient of monocyte count to HDL-c levels, with units in 10^9/L and mmol/L, respectively. Three typical indices associated with inflammatory response were also used to represent the effect of inflammation on kidney stones (7, 8, 18). The SII, NLR, and platelet to lymphocyte ratio (PLR) were calculated using the following formulas: SII = platelet x neutrophil/lymphocyte (10^9/L), NLR = neutrophil/lymphocyte (10^9/L), and PLR = platelet/lymphocyte (10^9/L). The assessment of the history of kidney stones, which served as the outcome variable, was determined by asking the question, “Have you or the sample person (SP) ever had a kidney stone?” (ID: KIQ026). Individuals who responded “yes” were categorized as having kidney stones, while those who responded “no” were classified as not having kidney stones. The reliability of self-reported kidney stone history has been established in previous studies (3, 4, 7, 19–21).

Demographic data (age, gender, and race) was obtained, along with various potential covariates such as annual household income, educational level, smoking status, hypertension, diabetes, cardiovascular disease, body mass index (BMI, kg/m²), alanine transaminase (ALT, U/L), aspartate transaminase (AST, U/L), gamma-glutamyl transferase (GGT, U/L), glycohemoglobin, triglycerides (TG, mmol/L), total cholesterol (TC, mmol/L), low-density lipoprotein cholesterol (LDL-c, mmol/L), blood urea nitrogen (BUN, mmol/L), serum creatinine (Scr, μmol/L), and serum uric acid (SUA, μmol/L). BMI is categorized as follows: <25 kg/m² (normal weight), 25-29.9 kg/m² (overweight), ≥30 kg/m² (obesity). Smokers were identified as current or former smokers. Additionally, self-reported diabetes, hypertension, and cardiovascular disease were recorded. The presence of cardiovascular disease was determined based on self-reported history of heart attack, stroke, congestive heart failure, coronary artery disease, or angina. Comprehensive measurement procedures for all variables were publicly accessible in the NHANES database.

The statistical analyses adhered to the Centers for Disease Control and Prevention guidelines, using a complex multistage cluster survey design and weights from six cycles. Continuous variables were presented as means with standard errors (SE), and categorical variables as percentages. The weighted Student’s t-test and chi-squared test compared continuous and categorical variables across groups, respectively. Weighted logistic regression models were used to investigate the associations between monocytes, HDL-c, and MHR (both continuous and quartile) with the risk of kidney stones. Three common models were used: Model 1 was unadjusted; Model 2 adjusted for age, gender, and race; and Model 3 additionally for annual household income, education level, smokers, hypertension, diabetes, cardiovascular disease, BMI, ALT, AST, GGT, glycohemoglobin, TG, BUN, Scr, and SUA. Furthermore, the impacts of SII, NLR, and PLR have been additionally adjusted based on Model 3. Decision curve analysis (DCA) was employed to evaluate the performance of MHR, SII, NLR, and PLR on kidney stone risk. Subgroup analysis was based on age, gender, race, BMI, hypertension, diabetes, and cardiovascular disease stratification. Finally, Restricted Cubic Spline (RCS) analysis further investigated the relationship between MHR and kidney stone risk. For observed non-linear correlations, a two-piecewise linear regression model was used to define intervals and identify threshold effects. All statistical analyses in this study were performed based on the Empower software (http://www.empowerstats.com) and R software (http://www.R-project.org). A P value < 0.05 was deemed statistically significant.

The study included 28,878 participants aged 20 to 79 years, who were not pregnant, had complete MHR data, and provided comprehensive kidney stone questionnaire records. Among them, 2,663 individuals were diagnosed with kidney stones. The average age was 46.25 years, and males constituted 49.21% of the cohort. Table 1 delineates the comparative analysis of general characteristics and clinical indicators between those with and without kidney stones. The kidney stone group had higher average age, male ratio, BMI, and prevalence of smokers, hypertension, diabetes, and cardiovascular disease. Elevated biochemical levels included glycohemoglobin, GGT, TG, Scr, BUN, and SUA, monocytes, and neutrophils (P<0.05). Conversely, this group demonstrated lower levels of HDL-c (P<0.001). Significant differences in race distribution were also observed between the groups (P<0.001). Crucially, the kidney stone group exhibited higher levels of MHR, SII, and NLR compared to the non-kidney stone group.

Participants were categorized into four quartiles based on their MHR levels ( Table 2 ). In comparing the first quartile to quartiles 2-4, there was a notable rise in the percentage of males, annual household incoming under $20,000, smokers, and individuals with hypertension, diabetes, cardiovascular disease, and higher BMI (P<0.001). Concurrently, there was a significant increase in biochemical markers such as ALT, AST, GGT, glycohemoglobin, TG, LDL-c, BUN, Scr, SUA, and counts of monocytes, neutrophils, lymphocytes, and platelets (P<0.001). Additionally, inflammation indices like SII and NLR also escalated with higher MHR levels (P<0.001). In contrast, age, education level above high school, TC, HDL-c, and PLR, were inversely associated with higher MHR levels, and race distribution varied significantly (P<0.001). Notably, the prevalence of kidney stones increased progressively from 7.20% in quartile 1 to 12.05% in quartile 4, suggesting a strong association between elevated MHR levels and the risk of kidney stones (P<0.001).

Table 3 illustrates the relationship between the MHR and kidney stone risk. Initial analyses without adjustment indicated that monocytes and MHR each had a direct correlation with heightened kidney stone risk, whereas HDL-c was inversely correlated (P<0.001). These correlations persisted as significant even when adjusted for age, gender, and race (P<0.05). Additionally, after fully adjusted for confounding factors, for each unit increase in the MHR, the odds of kidney stone risk rose by 31% (OR=1.31, 95%CI=1.11-1.54, P=0.001). It is noteworthy that even after considering the influences of SII, NLR, and PLR, MHR remains an independent risk factor for kidney stones (OR=1.22, 95%CI=1.03-1.44, P=0.021). Additionally, according to the results of the DCA analysis, MHR demonstrated superior performance compared to SII, NLR, and PLR ( Figure 1 ). When categorizing the MHR into quartiles, the result indicated that individuals in the higher MHR quartiles had a greater prevalence of kidney stones compared to those in the lowest quartile (P for trend < 0.001).

A multivariate logistic regression analysis was performed, with the independent variables including the MHR, SII, NLR, PLR, age, gender, race, annual household income, educational level, smoking, hypertension, diabetes, cardiovascular disease, BMI, ALT, AST, GGT, glycohemoglobin, TG, BUN, Scr, and SUA ( Table 4 ). The results showed that MHR, age, male, race, hypertension, diabetes, cardiovascular disease, BMI, and SUA were independent risk factors for kidney stones.

The subgroup analysis was performed to evaluate the consistency of the relationship between MHR and the risk of kidney stones across diverse demographic cohorts. The analysis results, depicted in Figure 2 , demonstrated that stratification by race, BMI, or diabetes, hypertension, and cardiovascular disease did not significantly modify the association between MHR and kidney stones (P for interaction>0.05). Intriguingly, we noted a significant interplay between age (20-49/50-79) or gender (female/male) and the MHR-kidney stone linkage (P for interaction<0.05). In individuals aged 20 to 49 and among females, there is a stronger correlation between MHR and the risk of kidney stones compared to those aged 50 to 79 and males.

The RCS analysis suggests a nonlinear relationship between the MHR and the risk of kidney stones in the overall sample ( Figure 3 ) (P for nonlinear < 0.001). Further investigation using a two-piecewise linear regression model reveals a breakpoint (K) at 0.44 ( Table 5 ). To the left of this breakpoint, there is a positive correlation between MHR and the risk of kidney stones, with an OR of 4.57 and a 95%CI ranging from 2.64 to 7.92 (P<0.001). To the right of the breakpoint, the association between MHR and kidney stones is not statistically significant, with an OR of 1.02 and a 95% CI from 0.84 to 1.23 (P=0.864). There is a significant change across the breakpoint (P for logarithmic likelihood ratio<0.001). The results of RCS analysis stratified by age and gender is also provided in Supplementary Figure 1 .