The core data for this study were obtained from the NHANES database, which is managed by the U.S. Centers for Disease Control and Prevention. The NHANES employs a sophisticated probability sampling method every 2 years to collect data reflecting the U.S. population, encompassing details on demographics, socioeconomic conditions, dietary habits, and health-related factors.

This study used data from six NHANES data cycles spanning 2007-2018. The inclusion criteria were as follows: (1) individuals aged 20 years or older; (2) participants providing full details on BMI, metabolic syndrome, and history of kidney stones; and (3) availability of data regarding physical activity and additional covariates. The exclusion criteria were as follows: (1) age <20 years; (2) missing data regarding the diagnosis of kidney stone or metabolic health status; (3) incomplete covariate data, e.g., age, sex, and BMI. After applying these criteria, the final sample consisted of 12,498 participants (Figure 1).

Kidney stones: The main focus of this study was to determine the incidence of kidney stones, evaluated using data from the Questionnaire (question KIQ026): “Have you ever experienced kidney stones?” Individuals who answered affirmatively were categorized as having a “kidney stone.” (Li et al., 2024).

Metabolic syndrome was defined based on the 2009 criteria (Alberti et al., 2009). Individuals meeting at least three of the following five criteria were considered to have metabolic syndrome: (1) triglyceride levels more than1.7 mmol/L or reliance on lipid-lowering medications; (2) reduced HDL-C levels, less than 1.0 mmol/L in men or 1.3 mmol/L in women, or use of cholesterol-lowering drugs; (3) elevated blood pressure (systolic ≥130 mm Hg and/or diastolic ≥85 mm Hg), or using antihypertensive drugs; (4) fasting blood glucose levels of 5.6 mmol/L or higher, a self-reported diabetes diagnosis, or use of glucose-lowering treatments; (5) elevated waist circumference (≥102 cm in men; ≥88 cm in women).

BMI was determined based on the examination data and calculated by dividing body mass in kilograms by the square of height in meters. According to World Health Organization (WHO) guidelines, participants were categorized into three groups: normal weight (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obese (>30 kg/m²) (Elmaleh-Sachs et al., 2023).

Using the combination of metabolic syndrome status and BMI categories, participants were divided into six groups: MHN: metabolically healthy normal weight, MHOW: metabolically healthy overweight, MHO: metabolically healthy obese, MUN: metabolically unhealthy normal weight, MUOW: metabolically unhealthy overweight, and MUO: metabolically unhealthy obese.

To account for potential confounding factors, the model was adjusted for several demographic and health-related variables, including education level, sex, age, race, family income-to-poverty ratio (FIR), smoking status, coronary heart disease, gout, and physical activity. A comprehensive summary of these covariates can be found in Supplementary Table S1 (available in Supplementary Digital Content 1).

Physical activity included both moderate and vigorous activities. Moderate physical activity was defined as engagement in work-related or recreational activities lasting ≥10 min per week, accompanied by small increases in the heart rate or breathing (e.g., brisk walking, recreational volleyball, or swimming). Vigorous physical activity included activities that largely increased the heart rate or breathing (e.g., carrying heavy loads, running, competitive basketball, or construction work) for ≥10 min weekly (Li et al., 2024). Since more physical activity may reduce the risk of kidney stones (Mao et al., 2022), we conducted a logistic regression analysis, stratified by participants’ physical activity levels, to more precisely assess the association between MHO and the risk of developing kidney stones.

This study followed the statistical analysis protocols established by the Centers for Disease Control and Prevention (CDC), incorporating appropriate sample weights for participant selection. The baseline characteristics of the study population were outlined based on metabolic status and BMI. Continuous variables are presented as mean ± standard deviations (SD), and categorical variables are expressed as percentages (%). Survey-weighted Analysis of Variance (ANOVA) was applied to evaluate differences in continuous variables across the six metabolic-BMI groups, and survey-weighted χ² tests were employed for categorical variables. Logistic regression analyses were used to assess the association between MHO and kidney stones, using both unadjusted (Model 1) and adjusted models. Model 1 was unadjusted; Model 2 was adjusted for sex, age, and race; and Model 3 was further adjusted for participant factors, including education level, FIR, smoking status, coronary heart disease, and physical activity. The magnitude of the association was determined using odds ratios (ORs) with corresponding 95% confidence intervals. Interaction effects were measured using stratified logistic regression analyses to investigate potential interactions between covariates, assess the association of MHO with kidney stones, and confirm the consistency of the findings. This approach aimed to identify the factors modifying the association between MHO and kidney stones within subgroups of physical activity. Statistical analyses were conducted using R software (developed by The R Foundation) and STATA 16.0 (produced by Stata Corporation, located in College Station, TX, USA), A two-tailed P-value <0.05 was considered statistically significant.

In total, 59,842 participants were recruited for this study from 2007-2018. After applying the inclusion and exclusion criteria, 12,498 participants were included (Figure 1). A thorough and comprehensive outline of the baseline characteristics of participants is displayed in Table 1.

Among the 12,498 participants, 5,239 were metabolically unhealthy, and 7,259 were metabolically healthy. Of the 7,259 metabolically healthy individuals, 3,534 (48.68%) were male, and 3,725 (51.32%) were female. Among the 5,239 metabolically unhealthy participants, 2,556 (48.79%) were male, and 2,683 (51.21%) were female. Among metabolically healthy participants, 3,113 (42.9%) had normal weight, while only 394 (7.5%) participants with normal weight were metabolically unhealthy. In addition, metabolically healthy participants had higher education levels and higher FIRs, were younger, and suffered from a lower risk of coronary heart disease and gout. They also reported higher levels of physical activity.

The overall prevalence of kidney stones was 10.20% among all participants. In each BMI category, the prevalence of kidney stones was higher among metabolically unhealthy participants compared to metabolically healthy participants: normal weight (8.1% vs. 6.3%), overweight (12.3% vs. 8.0%), and obesity (14.5% vs. 11.1%) (P < 0.001). Furthermore, in each BMI category, participation in physical activity was more evident among metabolically healthy participants compared to metabolically unhealthy participants: normal weight (80.2% vs. 61.2%), overweight (79.5% vs. 68.9%), and obesity (75.2% vs. 66.1%) (P < 0.001).

Using the MHN group as a reference, we employed a weighted logistic regression model to determine the association between MHO and kidney stones. In univariate analysis (Model 1), among physically active individuals, the risk of kidney stones was 83% higher in the MHO group compared to the MHN group (OR = 1.83, 95% CI: 1.29–2.59, P = 0.001). The MUN group exhibited a 22% higher risk than the MHN group (OR = 1.22, 95% CI: 0.64–2.33, P = 0.554), while the MUO group exhibited a 130% increased risk (OR = 2.30, 95% CI: 1.72–3.07, P < 0.001). After adjusting for covariates in Model 2 (MHO: OR = 2.04, 95% CI: 1.43–2.91, P < 0.001; MUO: OR = 2.08, 95% CI: 1.55–2.80, P < 0.001) and Model 3 (MHO: OR = 2.10, 95% CI: 1.47–2.98, P < 0.001; MUO: OR = 1.98, 95% CI: 1.45–2.67, P < 0.001), MHO and MUO were still significantly associated with the risk of kidney stones. Interestingly, among participants who did not participate in physical activities, the risk of kidney stones was increased in the MHO group (OR = 1.98, 95% CI: 1.10–3.60, P = 0.025) and the MUO group (OR = 3.10, 95% CI: 1.95–4.92, P < 0.001) compared to the MHN group. Adjustments in Model 2 (MHO: OR = 2.27, 95% CI: 1.24–4.16, P = 0.008; MUO: OR = 2.71, 95% CI: 1.69–4.34, P < 0.001) and Model 3 (MHO: OR = 2.22, 95% CI: 1.23–4.05, P = 0.007; MUO: OR = 2.88, 95% CI: 1.83–4.52, P < 0.001) did not change these significant associations (Table 2). Furthermore, the prevalence of kidney stones tends to increase with increasing body weight.

Additional stratified logistic regression and interaction effect analyses were conducted among participants involved in physical activities to identify variables that may modify the association between MHO and kidney stones. Formal tests for interaction revealed that age (P-interaction = 0.451), sex (P-interaction = 0.266), and race (P-interaction = 0.225) did not significantly affect the association between MHO and kidney stones. Similarly, no significant interaction effects were found for education level (P-interaction = 0.140), family income-to-poverty ratio (P-interaction = 0.416), smoking history (P-interaction = 0.661), diabetes mellitus (P-interaction = 0.202), hypertension (P-interaction = 0.940), coronary heart disease (P-interaction = 0.193), or gout (P-interaction = 0.587) (Table 3). We also assessed the association between participation in physical activity and the presence of kidney stones. Among most metabolic-BMI groups, individuals who engaged in physical activity were at a lower risk of kidney stones compared to inactive individuals (Figure 2).