Association between skeletal muscle index and kidney stones in health screening populations: a single-center cross-sectional study

Introduction

Kidney stones (KS) are one of the most common urinary system diseases worldwide, with an increasing prevalence year by year and a recurrence rate of as high as approximately 50% (1). Traditional epidemiological studies have identified multiple metabolic and lifestyle factors closely associated with the development of KS, including low fluid intake, dietary calcium, oxalate, salt, and weight-related indicators such as obesity (1,2). In recent years, body composition, especially fat distribution (e.g., visceral fat index, waist circumference-to-weight ratio), has been confirmed to be positively correlated with KS risk, suggesting that the body’s metabolic status plays a crucial role in stone formation (3,4).

The skeletal muscle index (SMI) is a commonly used metric for assessing an individual’s muscle mass, typically expressed as skeletal muscle mass divided by height squared (kg/m²). It is one of the core parameters for the diagnosis of sarcopenia (5). Low SMI is closely associated with metabolic syndrome, insulin resistance, and poor prognosis in chronic kidney disease (6,7). Muscle tissue performs important functions in glucose metabolism, uric acid excretion, and acid-base balance. Theoretically, a decrease in muscle mass may affect the risk of stone formation by altering urine composition (8-10).

Previous studies have explored the association between the skeletal muscle-to-visceral fat ratio (SVR) and KS. The findings showed that a lower SVR is significantly associated with an increased risk of KS, suggesting that relative muscle mass insufficiency may be a potential risk factor for stone development (11). However, since SVR combines fat-related indicators, it is difficult to independently evaluate the role of muscle mass itself. Most existing studies focus on obesity and fat-related parameters, while the independent contribution of muscle mass to KS development has not been fully elucidated (3,4). On the other hand, higher skeletal muscle mass is generally recognized as a protective factor for health, as it enhances metabolic homeostasis and reduces the risk of various diseases, the potential role of low muscle mass in the pathogenesis of KS has not been validated in large-scale general populations (12-14). Therefore, a critical research gap persists: the independent association between SMI and KS risk has not been established, and the specific contribution of muscle mass to stone formation remains unclear.

This study aims to fill the research gap regarding the role of muscle mass in the pathological mechanism of KS among a large-scale health check-up population, and to provide a novel body composition indicator for early risk assessment and prevention strategies. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2026-1-0068/rc).

Methods

Study population

The study population consisted of individuals who underwent routine health check-ups at the Health Promotion Center of Sir Run Run Shaw Hospital, Zhejiang University, located in Hangzhou, China. The recruitment timeline was clearly defined as January 1, 2019, to December 31, 2025, with continuous enrollment of eligible participants during this period.

Inclusion criteria were explicitly established as follows: (I) aged 18 years or older; (II) voluntarily received a comprehensive health check-up at the aforementioned center during the recruitment period; and (III) provided complete cooperation for all required examinations (including renal ultrasound, body composition analysis, physical measurement, and laboratory tests).

Exclusion criteria were strictly implemented to avoid potential confounding and selection bias, including: (I) a confirmed history of malignancies (any type); (II) a history of cerebrovascular diseases (cerebral hemorrhage, cerebral infarction); (III) a history of severe heart diseases (e.g., myocardial infarction, heart failure, severe arrhythmia); (IV) presence of liver dysfunction (defined as alanine aminotransferase or aspartate aminotransferase levels exceeding 1.5 times the upper limit of the normal reference range); (V) end-stage renal disease (estimated glomerular filtration rate <15 mL/min/1.73 m²); and (VI) diagnosed autoimmune disorders (e.g., rheumatoid arthritis, systemic lupus erythematosus).

The patient selection process was conducted in two sequential stages for this health check-up population: first, all individuals who attended health check-ups during the recruitment period were initially screened against the inclusion criteria; second, those who met the inclusion criteria were further excluded based on the exclusion criteria. Regarding handling of missing data, considering the characteristics of the health check-up population and to ensure data integrity, all participants with missing main data (including key variables such as SMI, renal ultrasound results, age, sex, and essential biochemical indicators) were directly excluded from the study. No imputation was performed for missing data. After strict screening and excluding participants with missing main data, a final study population of 18,918 individuals was included in the subsequent analyses. Figure 1 shows the flow chart of study participants

Figure 1 Flow chart of study participants.

Outcome and exposure variables

KS diagnosis

Renal ultrasound assessments were carried out by ultrasound clinicians who hold the professional title of attending physician or above. All scanning procedures were performed using ultrasound systems of a uniform model (produced in the United States), which were equipped with linear array transducers with a frequency range of 3.0–5.0 MHz. Comprehensive records of sonographic observations were made, encompassing the key characteristics of KS (including size in millimeters, quantity, and anatomical position), the severity grade of renal hydronephrosis (classified as mild, moderate, or severe), and the presence of other urinary tract anomalies (such as ureteral dilatation, renal cysts, or structural malformations).

SMI measurement

The SMI is a standardized metric used to evaluate skeletal muscle mass relative to an individual’s height. It was derived using a body composition analyzer based on bioelectrical impedance analysis (BIA), with measurements performed in strict adherence to standardized operating procedures to ensure the accuracy and reproducibility of results, particularly for the health examination population included in this study.

Specifically, the BIA measurement procedure was conducted as follows: First, all participants were required to be in a fasting state for at least 8 hours prior to measurement. Additionally, participants were instructed to avoid strenuous exercise, excessive fluid intake, and alcohol consumption within 24 hours before the test to eliminate potential interference of these factors on body fluid distribution and skeletal muscle mass assessment. Prior to formal measurement, participants were asked to remove all metallic objects (e.g., necklaces, bracelets, watches, and keys) to prevent disruption of bioelectrical signals. Subsequently, participants were guided to stand barefoot on the electrode plates of a BIA body composition analyzer with their feet shoulder-width apart, ensuring full contact between the soles of their feet and the metal electrodes. Participants held the hand electrodes of the analyzer with their arms naturally hanging down and separated from their bodies to avoid contact with other objects. During measurement, participants were instructed to maintain a relaxed posture, breathe normally, and refrain from body movement to ensure the stability of bioelectrical signals. The analyzer emitted a low-intensity, high-frequency alternating current through the electrodes, which propagated through the body’s conductive tissues (e.g., skeletal muscle and body fluid) and non-conductive tissues (e.g., adipose tissue and bone). Based on the differences in electrical impedance among various tissues, the analyzer automatically calculated and recorded each participant’s skeletal muscle mass. Finally, the SMI was computed using the formula: SMI = skeletal muscle mass (kg)/height² (m²). This calculation was directly performed by the analyzer’s built-in system to minimize manual calculation errors.

Covariates assessment

All enrolled participants underwent a comprehensive physical evaluation. During structured interviews, trained general practitioners systematically collected information on participants’ medical histories, smoking habits, and drinking status. Height (measured to the nearest 0.1 cm) and body weight (measured in light attire to the nearest 0.1 kg) were obtained by qualified nurses under standardized operating conditions using calibrated measuring devices. Blood pressure (BP) measurements were performed in accordance with the guidelines established by the American Heart Association (AHA) (15). Specifically, participants were instructed to rest in a seated posture for a minimum of 5 minutes prior to measurement; the measurement arm was supported at the level of the heart, and a cuff of appropriate size was applied to ensure accuracy.

After recording the initial BP reading, a duplicate measurement was taken at an interval of 30–60 seconds, and the mean value of these two readings was documented. In cases where the discrepancy between the two measurements exceeded 10 mmHg for either systolic BP (SBP) or diastolic BP (DBP), a third measurement was conducted, and the average of all three readings was utilized for subsequent statistical analyses. Fasting venous blood specimens were collected from all participants in the morning following an overnight fast of at least 8 hours (16). During this fasting period, participants were restricted from consuming caloric substances but were permitted ad libitum water intake. Standardized laboratory methodologies were employed to analyze serum biochemical indicators, including total cholesterol (TC), triglycerides (TG), and serum creatinine (CR).

Statistical analyses

Continuous variables were summarized as mean ± standard deviation (SD) or median [interquartile range (IQR)] based on normality tests, and compared using Student’s t-test or Mann-Whitney U test accordingly. The selection of tests was based on the distribution of continuous variables: Student’s t-test was used for normally distributed data, while the Mann-Whitney U test was applied for non-normally distributed data. Categorical variables were presented as counts and percentages, and group comparisons were conducted using the Chi-squared test or Fisher’s exact test, as appropriate. Multivariable logistic regression was applied to evaluate the independent association between SMI and KS, with both unadjusted and fully adjusted models. The unadjusted model reflects the crude association, while the multivariable model controls for potential confounding factors to obtain the independent effect of SMI. SMI was treated as both a continuous variable and a categorical variable divided into quartiles for regression analyses. The multivariable models were adjusted for potential confounding factors listed in the baseline characteristics table, including age, sex, hypertension, diabetes, smoking status, drinking status, SBP, DBP, TC, low-density lipoprotein cholesterol (LDL-C), TG, and CR, as they are known to be associated with both skeletal muscle mass and KS formation.

Statistical analyses were performed using R software (version 4.4.3; http://www.R-project.org/). A two-sided P value <0.05 was considered statistically significant, ensuring a robust assessment of the associations observed in the study.

Ethical consideration

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (approval No. 2025-2961-01). The requirement for obtaining informed consent from participants was waived by the aforementioned ethics committee, given that this study utilized data derived from prior clinical diagnosis and treatment procedures. Notably, medical records of patients who had explicitly refused to authorize the use of their information were excluded from the study. Throughout the research process, no adverse impacts on the rights and health of the participants were incurred, and the privacy as well as personal identity information of all subjects were safeguarded to the maximum extent possible.

Results

Baseline characteristics of study participants

Baseline characteristics of the study population are summarized overall and across quartiles of SMI in Tables 1,2. A total of 18,918 participants were included in the present analysis, with a mean age of 46.7±11.5 years. The mean ± SD of SMI across the four quartile groups was 5.98±0.39 (Q1), 7.08±0.29 (Q2), 7.89±0.20 (Q3), and 8.84±0.55 (Q4), respectively. Regarding the prevalence of a history of KS, the rates were 1.8% in Q1, 4.0% in Q2, 5.8% in Q3, and 5.6% in Q4 among the different SMI categories, revealing a generally increasing trend in stone prevalence with ascending SMI categories. Figure 2 illustrates the differences in SMI between participants with and without KS. Statistical analysis revealed that participants with KS had a significantly higher SMI compared to those without KS (P<0.001), indicating a clinically noticeable difference in skeletal muscle status between groups.

Figure 2 Comparison of SMI between participants with and without KS. KS, kidney stones; SMI, skeletal muscle index.

Association between SMI and KS (multivariate regression analysis)

Multivariate logistic regression analyses were performed with varying degrees of adjustment for potential confounding factors. A consistent positive correlation between SMI and the risk of KS was observed across all three models: model 1 [odds ratio (OR) =1.365; 95% confidence interval (CI): 1.282–1.454], model 2 (OR =1.104; 95% CI: 1.003–1.213), and model 3 (OR =1.102; 95% CI: 1.000–1.213), demonstrating that higher SMI was independently associated with elevated kidney stone risk after full adjustment. Additionally, compared with participants in the lowest SMI quartile (Q1), those in the highest SMI quartile (Q4) had a significantly elevated risk of KS in model 1 (OR =3.182; 95% CI: 2.496–4.095), model 2 (OR =1.720; 95% CI: 1.197–2.480), and model 3 (OR =1.708; 95% CI: 1.184–2.469), supporting a clinically meaningful stepwise increase in risk. The test for trend yielded a P value <0.05 in all three models (Table 3), confirming a robust dose-response relationship between SMI and kidney stone prevalence.

Subgroup or interaction analyses

To evaluate the robustness of the association between SMI and KS, subgroup analyses were conducted (Table 4). Subgroup analyses revealed significant effect modification by body mass index (BMI) and diabetes status in the association between SMI and KS. A significant positive association between SMI and kidney stone risk was observed only in participants with BMI <24 kg/m² (OR =1.440; 95% CI: 1.138–1.825; P=0.002) and those without diabetes (OR =1.112; 95% CI: 1.004–1.231; P=0.041), with statistically significant interaction terms (P for interaction =0.006 and 0.04, respectively). No significant association was detected in other subgroups stratified by age, sex, smoking status, alcohol consumption, or hypertension, and no significant effect modification was observed for these variables. These findings indicate that the association between SMI and KS is specifically pronounced in non-obese and non-diabetic populations, highlighting the importance of metabolic status in the relationship between skeletal muscle mass and kidney stone risk.

Discussion

This study is the first to systematically evaluate the association between SMI and the risk of KS in a large Chinese population undergoing health checkups. Results showed a positive correlation between SMI and KS, which remained statistically significant after full adjustment for multiple confounders (OR =1.102; 95% CI: 1.000–1.213). Compared with the lowest quartile (Q1), individuals in the highest quartile (Q4) of SMI had approximately a twofold increased risk of KS (OR =1.708; 95% CI: 1.184–2.469). This finding aligns with reports from U.S. National Health and Nutrition Examination Survey (NHANES) data indicating that “low muscle mass index (sarcopenia)” significantly increases the risk of KS (11). However, discrepancies exist in the definition of key indicators: the present study employs absolute SMI (calculated as muscle mass divided by height squared), whereas sarcopenia research focuses on relative deficits in muscle mass and strength (17).

A recent cross-sectional study based on U.S. NHANES data reported an inverse association between reduced SVR and elevated KS risk, also presenting a non-linear pattern (11). This contradictory finding may stem from differences in indicator definitions between the two studies: NHANES used SVR (muscle/fat), emphasizing the relative proportion of muscle to fat, whereas the current study adopted SMI alone (muscle mass/height²) without accounting for the regulatory role of fat mass. High SMI accompanied by increased body weight or obesity may lead to elevated urinary excretion of metabolic products such as calcium and uric acid, thereby enhancing the risk of stone formation. Multiple studies have demonstrated positive correlations between high animal protein intake, low-calcium diet and KS incidence, while adequate dietary calcium reduces this risk (18). Increased muscle mass is often associated with higher protein intake and metabolic activity, which may explain the elevated KS risk observed in participants with higher SMI in the present study. Additionally, obesity and reduced urinary acidity have been confirmed to be associated with uric acid stone formation (19).

At the biological mechanism level, higher SMI may promote kidney stone formation through three pathways: (I) amino acid metabolism: skeletal muscle is the primary site for amino acid metabolism; increased muscle mass can raise urinary concentrations of stone precursors such as uric acid and cystine (20); (II) insulin resistance and urinary acidification: high muscle mass is frequently accompanied by weight gain, which induces insulin resistance, reduces urinary pH, and facilitates uric acid stone formation (21,22); and (III) bone-muscle crosstalk: increased muscle mass may be associated with bone calcium release, elevating urinary calcium concentrations and increasing the risk of calcium oxalate stones (23). These mechanisms are consistent with existing literature: The association between obesity and decreased urinary acidity has been validated in multiple studies, and low urinary ammonia production induced by insulin resistance is considered a key pathological factor for uric acid stones (22,23); epidemiological evidence linking high animal protein intake to increased KS risk also supports the hypothesis of “high SMI accompanied by high protein intake” (24). Conversely, adequate dietary calcium reduces KS risk, suggesting that dietary balance should be emphasized in populations with high SMI (25).

Subgroup analyses confirmed effect modification by BMI and diabetes status, highlighting that the association between SMI and kidney stone risk is not consistent across all populations. Mechanistically, the lack of association in obese participants (BMI ≥24 kg/m²) likely reflects the dominant metabolic perturbations of obesity—including insulin resistance, dyslipidemia, and hyperuricemia—that overshadow the independent contribution of skeletal muscle mass. In this context, obesity itself is a well-established driver of kidney stone risk, such that metabolic pathways associated with stone formation mask any potential effect of SMI. In contrast, among non-obese individuals (BMI <24 kg/m²), where the systemic metabolic burden is lower, SMI emerges as a more prominent metabolic modifier; higher SMI may reflect distinct metabolic profiles that influence stone risk, such as altered renal handling of calcium or urate (26-28). Similarly, the positive association observed only in non-diabetic individuals stems from the strong confounding effect of diabetes. Diabetes is a major etiologic factor for KS, primarily via hyperglycemia-induced hypercalciuria and urine acidification (29). The overwhelming stone risk associated with diabetes may overshadow the metabolic signal conveyed by muscle mass; thus, in the non-diabetic population, where diabetes-related confounders are absent, SMI serves as a reliable marker of metabolic health, and its association with stone risk becomes apparent. Clinically, these findings support personalized stone risk stratification. SMI is a valuable supplementary indicator for kidney stone risk assessment specifically in non-obese, non-diabetic health screening populations. Evaluating SMI provides insights into metabolic health beyond traditional adiposity measures, enabling targeted preventive strategies to optimize muscle mass and reduce stone incidence.

The strengths of this study include a large sample size (n=18,918), standardized ultrasound diagnosis and BIA measurements, and multi-level analyses (quartile comparisons and subgroup analyses), which systematically revealed the SMI-KS association. This study has several limitations that should be acknowledged when interpreting the findings. First, the cross-sectional design of the study precludes the determination of causal direction between SMI and KS, as we cannot establish whether changes in SMI precede KS development or vice versa. Second, renal ultrasound was used as the primary diagnostic tool for KS, which has lower sensitivity compared to computed tomography (CT); this inherent limitation may lead to underdiagnosis of small calculi, potentially introducing bias into the accuracy of KS diagnosis and subsequent association analysis between SMI and KS. Third, the lack of detailed data on dietary patterns and fluid intake represents a major oversight, as these factors are well-known to influence KS formation; the inability to adjust for these key confounding variables may interfere with the reliability of the observed association between SMI and KS. Fourth, BIA was employed to measure SMI, which may be subject to estimation bias in obese populations due to the interference of adipose tissue with electrical conduction. Additionally, unreported control of factors such as hydration and eating status—known to affect BIA accuracy—introduces further uncertainties into the SMI measurements. These limitations necessitate caution in interpreting the study conclusions and highlight the need for further research to validate the generalizability and causal inference of the findings. Future studies should address these limitations by conducting prospective follow-up to clarify the temporal sequence of SMI changes and KS incidence, applying urinary metabolomics to explore the association between muscle metabolites and stone precursors, and performing intervention trials targeting protein intake or weight management to evaluate the impact of SMI modulation on KS incidence.

Conclusions

This study demonstrates that, among Chinese individuals undergoing health examinations, a higher SMI is positively associated with the risk of KS. The finding suggests that, in addition to traditional metabolic and dietary factors, muscle mass itself may serve as an important independent predictor of kidney-stone risk. Further longitudinal investigations and mechanistic elucidation are needed to develop more personalized prevention strategies.

Acknowledgments

The authors would like to thank the participants in this study and all members involved in collecting the baseline data.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-1-0068/rc

Data Sharing Statement: Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-1-0068/dss

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-1-0068/prf

Funding: This study was supported by the Grass-Roots Health Science and Technology Innovation Program of Zhejiang Province (No. 2024ZJWX-B016, to T.C. and L.C.), the Zhejiang Province Science and Technology Plan Project (No. 2024C35054, to L.H.), the 2023 Annual Teaching Reform Research Project of the Third Clinical College of Zhejiang University (No. SYF2023JG17, to L.H.), and the 2025 Annual Project of Zhejiang Province’s Medical and Health Science Research Program (No. 2025KY083, to L.H.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2026-1-0068/coif). L.H. reports that this study was supported by the Zhejiang Province Science and Technology Plan Project (No. 2024C35054), the 2023 Annual Teaching Reform Research Project of the Third Clinical College of Zhejiang University (No. SYF2023JG17), and the 2025 Annual Project of Zhejiang Province’s Medical and Health Science Research Program (No. 2025KY083). T.C. and L.C. report that this study was supported by the Grass-Roots Health Science and Technology Innovation Program of Zhejiang Province (No. 2024ZJWX-B016). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (approval No. 2025-2961-01). The requirement for obtaining informed consent from participants was waived by the aforementioned ethics committee, given that this study utilized data derived from prior clinical diagnosis and treatment procedures.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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