Introduction

Metabolic syndrome (MetS) is estimated to affect approximately a quarter of the global population, defined by the World Health Organization as a pathological state characterized by abdominal obesity, insulin resistance, hypertension, and dyslipidemia1. Therefore, MetS is not a single disease, but a metabolic disorder syndrome associated with cardiovascular disease, diabetes, dyslipidemia, cancer and disability2. Different organizations have proposed slightly different definitions of MetS. With the transformation of lifestyles and rapid economic development, the incidence of metabolic syndrome in China is on the rise, posing a serious challenge to public health. Between 2000 and 2017, four nationwide surveys conducted across 31 provinces, autonomous regions, and municipalities directly under the central government in mainland China revealed that, despite slight differences in the definition of MetS among the surveys (mainly due to changes in the waist circumference cutoffs), the standardized prevalence of MetS increased from 13.7% to 31.1%3,4,5,6. Given this rising trend, it is necessary to conduct in-depth research on its risk factors in order to identify effective prevention strategies.

Hypertension is the most common component of MetS and is closely related to other key indicators used to assess MetS, including hyperglycemia, central obesity, and dyslipidemia. Research has demonstrated that high sodium intake is positively correlated with elevated blood pressure, as well as increased risks of stroke and cardiovascular diseases7. In contrast, increased potassium intake is linked to lower blood pressure. Given these associations, it is reasonable to hypothesize that high sodium and low potassium intake might indirectly contribute to the development of MetS.

Creatinine is a metabolic product of muscle tissue, is mainly cleared by the kidneys. Therefore, it can reflect kidney function, muscle mass, and overall metabolic status8. In a state of increased catabolism (such as fasting), the concentration of creatinine in urine tends to increase9. A prospective study in the general population found that increased urinary creatinine excretion is associated with a reduced risk of adverse cardiovascular events10. However, to date, no studies have thoroughly examined the relationship between creatinine and MetS.

Microalbuminuria (MAU) is defined as an abnormally elevated urinary albumin excretion of 30–300 mg/day in a 24-hour urine sample and is considered a potential risk factor for MetS11. Nevertheless, the specific association between urinary microalbumin and MetS remains unclear.

Collecting 24-hour urine samples allows for the analysis of indicators such as sodium, potassium, creatinine, and microalbumin, thereby accurately reflecting the body’s sodium and potassium intake, muscle mass, and kidney health. Typically, the diagnosis of MetS primarily relies on blood tests and routine physical examinations, with relatively few studies involving 24-hour urine analysis. In this study, we collected 24-hour urine samples from participants to analyze the relationship between these indicators and MetS, aiming to provide new ideas and strategies for the prevention and intervention of MetS.

Methods

Study design

This study utilized baseline survey data from a cross-sectional study conducted in Zhejiang Province in 201712. The study covered three urban areas and two rural areas across the eastern, northeastern, central, mid-western, and southern regions of Zhejiang Province. A multi-stage cluster random sampling method was employed to select participants aged 18 to 69 years in Zhejiang Province. The study was approved by the Ethics Review Committee of the Zhejiang Provincial Center for Disease Control and Prevention, and all participants have signed informed consent forms. All research methods, including biological specimen testing, physical measurements, and questionnaire surveys, were strictly conducted in accordance with relevant guidelines and regulations.

Data collection

Questionnaire Survey: Baseline information of the study subjects was collected through questionnaires administered by professionally trained staff. The questionnaires covered basic demographic information and major disease history of the subjects. Physical Measurements: Weight, height, and waist circumference were measured using a calibrated, standardized electronic weight scale and measuring tape. Subjects were required to remove their shoes and wear light clothing during measurements. Waist circumference was measured at the midpoint between the iliac crest and the lower edge of the ribs using a measuring tape. Blood pressure and heart rate were measured using a validated automatic electronic sphygmomanometer (HEM-7071, Omron Corp., Japan), with a one-minute interval between each measurement. The average of three measurements was taken as the final result.

Sample Collection and Testing: Fasting blood samples and 24-hour urine samples were collected from the study subjects and sent to Hangzhou KingMed Medical Laboratory for testing. The testing items included fasting blood glucose (FBG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), 24-hour urinary sodium, potassium, creatinine, and microalbumin. Blood glucose concentration was measured using a modified hexokinase method; serum cholesterol and triglyceride levels were analyzed by enzymatic methods with commercially available reagents. The detection methods for urinary sodium, potassium, creatinine, and microalbumin have been described in detail elsewhere13.

Definition of variables

According to the criteria of the “Guidelines for the Prevention and Treatment of Type 2 Diabetes in China (2020 Edition)”14, MetS is defined as the presence of three or more of the following risk factors:

(1) Central obesity: Waist circumference ≥ 90 cm in men and ≥ 80 cm in women;

(2) Elevated blood pressure: Systolic blood pressure ≥ 130 mmHg and/or diastolic blood pressure ≥ 85 mmHg, or a self-reported history of hypertension with current treatment;

(3) Hyperglycemia: FBG ≥ 6.1 mmol/L or 2-hour postprandial glucose (2hPG) ≥ 7.8 mmol/L, or a self-reported history of diabetes with current treatment;

(4) Hypertriglyceridemia: Fasting TG ≥ 1.70 mmol/L;

(5) Low HDL-C: Fasting HDL-C < 1.04 mmol/L.

Smoking is defined as smoking continuously or cumulatively for more than one cigarette per day in the past six months.

Alcohol consumption is defined as consuming alcohol at least once per week over the past year.

Regular exercise is defined as engaging in moderate-intensity physical activity, or a combination of moderate- and high-intensity physical activity, for ≥ 150 min per week, or high-intensity exercise for ≥ 75 min per week.

Statistical analysis

Quantitative data conforming to a normal distribution are presented as mean ± standard deviation (x̄ ± SD) and compared between groups using the t-test. Quantitative data not conforming to a normal distribution are presented as median (quartiles) [M (P₂₅, P₇₅)] and compared between groups using the Wilcoxon rank-sum test. For comparisons of median differences among three or more independent samples, the Kruskal-Wallis test is used. If the Kruskal-Wallis test indicates significant differences, further pairwise comparisons are conducted using Dunn’s test to identify which specific groups differ significantly. Categorical data are presented as frequency (percentage) [n (%)] and compared between two groups using the chi-square test.

Based on 24-hour urinary microalbumin levels, study subjects were divided into four groups: Q1 (< 2.20 mg/24 h), Q2 (2.20–4.31 mg/24 h), Q3 (4.31–9.34 mg/24 h), and Q4 (≥ 9.34 mg/24 h). Logistic regression analysis was used to investigate the association between 24-hour urinary microalbumin excretion and MetS and its components, with adjustment for potential confounding factors, including sociodemographic factors (age, gender, and education level) and lifestyle factors (regular exercise, alcohol consumption, and smoking). Participants in the first quartile (Q1) of urinary microalbumin excretion were used as the reference group to calculate the prevalence odds ratios (PORs) and their 95% confidence intervals (CIs). Additionally, the median levels of 24-hour urinary microalbumin excretion across the four groups were included as a continuous variable in the logistic regression model to test the linear trend of increasing urinary microalbumin excretion by quartile. All analyses were performed using R Statistical Software (v4.4.3; R Core Team 2025). All tests were two-sided, and a P-value < 0.05 was considered statistically significant.

Results

Characteristics of participants

This study recruited 1,572 participants. The procedures for the collection, preservation, and testing of 24-hour urine samples have been described in detail in other studies15. During the urine collection process, researchers recorded the start and end times of urine collection and measured the total volume of urine. According to the standards set by previous studies, samples with a 24-hour urine volume of less than 500 mL or with 24-hour urinary creatinine excretion exceeding ± 2 standard deviations from the gender-specific mean were also excluded16. Furthermore, to avoid the influence of treatment and medication in patients with chronic diseases on this study, individuals who self-reported having hypertension or diabetes were also excluded. Ultimately, 76 urine samples failing to meet the criteria and 321 individuals with chronic diseases were excluded, resulting in an effective sample size of 1175 (74.7% of the recruited participants)15.

As shown in Table 1, the median age (interquartile range) of the 1175 participants was 42 (31, 54) years, 510 individuals (43.4%) from urban areas, and 665 individuals (56.6%) from rural areas. The distribution of education years was as follows: <9 years (27.49%), 9–12 years (47.15%), and ≥ 12 years (25.36%). The median systolic and diastolic blood pressure (interquartile range) of the participants were 123 (112.84, 134.67) mmHg and 77 (71.33, 83.33) mmHg, respectively. The overall prevalence of MetS was 11.4% (134/1175).

Compared with participants without MetS, those with MetS were more likely to be male and smokers (P < 0.05). Additionally, participants with MetS had significantly higher levels of body mass index (BMI), waist circumference, body fat percentage, systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), triglycerides (TG), low-density lipoprotein (LDL), 24-hour urinary sodium-to-potassium ratio, and 24-hour urinary microalbumin compared with those without MetS (all P < 0.05). In contrast, participants without MetS had higher HDL levels than those with MetS (P < 0.05).

Association of metabolic syndrome components with 24-hour urinary test indicators in 1175 adults

As shown in Table 2, 24-hour urinary sodium levels were significantly higher in participants with central obesity but lower in those with elevated blood pressure. Urinary potassium levels were significantly decreased in participants with elevated blood pressure. The sodium-to-potassium ratio was significantly elevated in participants with central obesity and elevated blood pressure. Urinary creatinine were significantly increased in participants with low HDL-C. Urinary microalbumin level were significantly higher in participants with central obesity, elevated blood pressure, elevated FBG, and elevated TG. Additionally, participants with central obesity and elevated TG had significantly higher 24-hour urine volume.

The relationship between the number of abnormal components of metabolic syndrome and 24-hour urine test indicators

Table 1 Characteristics of 1175 participants according to the status in Zhejiang Province of China in 2017.
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Table 2 Association of the five components of metabolic syndrome with 24-hour urinary test indicators in 1175 individuals.
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Fig. 1
figure 1

Mean 24 h urinary sodium (a), potassium (b), sodium-to-potassium ratio (Na/K ratio; c), creatinine (d), microalbumin (e), and volume (f) according to number of metabolic risk factors (0, 1, 2, or ≥ 3 factors).

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Participants were grouped by the number of abnormal MetS components: Group 0 (0 abnormal components), Group 1 (1 abnormal component), Group 2 (2 abnormal components), and a “patient group” (3–5 abnormal components, combined for clarity). Among groups with different numbers of abnormal components, most 24-hour urine indicators (e.g., sodium, potassium, creatinine) showed no significant differences (P > 0.05, Kruskal-Wallis test; Fig. 1). However, three indicators—24-hour urinary sodium-to-potassium ratio, microalbumin, and urine volume—exhibited significant intergroup differences (Fig. 1c, e and f). As shown in Fig. 1c, the urinary sodium-to-potassium ratio in the patient group was significantly higher than that in Group 0 (P < 0.05). Figure 1e indicates that urinary microalbumin levels in the patient group were significantly higher than those in Groups 0 and 1 (P < 0.0001 and < 0.01, respectively), and Group 2 had significantly higher levels than Group 0 (P < 0.01). Additionally, 24-hour urine volume differed significantly among groups; pairwise comparisons revealed a significant difference between Group 0 and Group 2 (P < 0.01; Fig. 1f).

Results are expressed as mean ± SD. For comparisons of differences among the four groups, the Kruskal-Wallis test was used. If the Kruskal-Wallis test indicated significant differences, further pairwise comparisons were conducted using Dunn’s test. Results of the Dunn test are denoted as follows: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Higher 24-hour urinary microalbumin levels and gradual increase in metabolic syndrome risk

As shown in Table 3, participants were stratified into quartiles based on their 24-hour urinary microalbumin levels. After adjusting for potential confounders (gender, age, education level, physical activity, smoking, and alcohol consumption; Table 3, Model 2), a progressive increase in the risk of metabolic syndrome (MetS) was observed across the quartiles of 24-hour urinary microalbumin excretion. Specifically, compared to the lowest quartile (Q1), the odds ratios (ORs) for MetS in the Q2, Q3, and Q4 were 1.54 (95% CI: 0.84 to 2.81), 2.04 (95% CI: 1.15 to 3.63), and 2.71 (95% CI: 1.56 to 4.71), respectively. A linear trend test across adjusted ORs revealed a highly significant positive correlation (P < 0.0001), indicating that higher urinary microalbumin levels were associated with a progressive increase in MetS risk.

After adjusting for age and gender (Table 3, Model 1), linear trends were observed between urinary microalbumin levels and the risk of central obesity, elevated blood pressure, and elevated triglycerides (TG) (Table 3). However, the trends did not remain statistically significant after further adjustment for additional confounders (Table 3, Model 2). Compared with the lowest quartile (Q1) of 24-hour urinary microalbumin levels, the odds ratios (ORs) for elevated fasting blood glucose (FBG) were 0.32 (95% CI: 0.10 to 1.01) in quartile 2 (Q2), 1.54 (95% CI: 0.73 to 3.26) in quartile 3 (Q3), and 2.43 (95% CI: 1.20 to 4.90) in quartile 4 (Q4). A linear trend test indicated a significant positive correlation between 24-hour urinary microalbumin levels and the risk of elevated FBG (P = 0.0002). Specifically, although the OR in Q2 was lower than that in Q1, the 95% CI spanned 1.0, indicating no statistical significance. Starting from Q3, the ORs showed a gradual increasing trend, with the OR in Q4 (the highest quartile) reaching statistical significance. These findings suggest that the risk of elevated FBG exhibits an overall increasing trend with higher 24-hour urinary microalbumin levels.

Table 3 Associations between 24-hour urinary microalbumin quartiles and metabolic syndrome (MetS) and its components.
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Discussion

In this study, 24-hour urine samples were collected and analyzed for various indicators, including sodium, potassium, sodium-to-potassium ratio, creatinine, microalbumin, and urine volume. The associations between these urinary markers and metabolic syndrome (MetS) and its components were subsequently examined. The findings revealed that among all the tested indicators, only 24-hour urinary microalbumin exhibited a positive correlation with the risk of MetS and its components. Notably, this correlation persisted even after adjusting for potential confounding factors.

The prevalence of MetS in this study was 11.4%, which is lower than the 24.7% reported in a previous study conducted in Shandong Province, China17. That study did not exclude individuals with chronic diseases such as hypertension and diabetes. Additionally, diagnostic criteria for MetS vary, with key differences in the cutoff values for waist circumference, high-density lipoprotein cholesterol (HDL), and fasting blood glucose (FBG). Shandong Province, located in northern China, differs from Zhejiang Province in the south, where a lighter diet is more prevalent and the risk of metabolic diseases is relatively lower. Furthermore, this project was initiated six years after the Shandong study. Analysis of the population’s basic characteristics revealed that participants in this study had higher educational levels and stronger health awareness.

This study demonstrated that the median level of 24-hour urinary sodium in Zhejiang Province was 158.42 mmol/24 h, corresponding to an estimated daily salt intake of approximately 9.25 g. In comparison, a large-scale analysis of salt intake across China, published in 2020, collected 24-hour urine samples from 5,454 adults in various counties of six provinces (Qinghai, Hebei, Heilongjiang, Sichuan, Jiangxi, and Hunan) in 2018 and reported an average daily salt intake of 11 g18. These findings suggest that while Zhejiang Province has a lower salt intake level than the national average, it remains considerably higher than the World Health Organization’s recommended daily intake of 5 g19.

We noted that as the number of metabolic risk factors increased, the average level of 24-hour urinary microalbumin excretion also rose significantly, which is consistent with prior research findings20. However, in our study, no significant correlation was observed between the excretion of urinary sodium, potassium, and creatinine and metabolic risk factors. This contrasts with data from a study in Shandong, China, which suggested that high sodium intake and an elevated sodium-to-potassium ratio may be significant risk factors for MetS among Chinese adults17,21. This discrepancy may be attributed to the heightened health awareness among residents in Zhejiang Province: when metabolic factors become abnormal, individuals are more likely to actively reduce their salt intake. For example, participants with elevated blood pressure had significantly lower urinary sodium excretion (151.7 mmol/24 h, equivalent to a salt intake of 8.86 g/d) than those with normal blood pressure (161.2 mmol/24 h, equivalent to a salt intake of 9.42 g/d; P = 0.004). Conversely, participants with elevated blood pressure also had significantly lower urinary potassium excretion (32.14 mmol/24 h) than those with normal blood pressure (36.72 mmol/24 h; P < 0.001). Urinary potassium excretion is largely influenced by the sodium delivery to the distal nephron. Higher sodium intake can lead to greater urinary potassium loss via the sodium/potassium exchange mechanism in the kidneys, which may explain why both urinary sodium and potassium excretion levels were lower in participants with elevated blood pressure compared to the normal blood pressure group22.

Moreover, in obese participants, urinary sodium excretion was significantly higher, while no significant difference was observed in urinary potassium excretion. This finding is consistent with a South Korean study that identified high sodium intake as a potential independent predictor of obesity23. These results suggest that obese individuals may have lower awareness of the adverse effects of salt consumption. Additionally, in our analysis of urinary creatinine, participants with low HDL had a significantly higher creatinine excretion rate (10.95 mmol/24 h) compared to those with normal HDL (9.41 mmol/24 h; P < 0.001). This difference may indicate potential impairment of renal function in participants with low HDL.

After adjusting for potential confounding factors including gender, age, years of education, physical activity, smoking, and alcohol consumption, this study found that the 24-hour urinary microalbumin levels were positively correlated with the risk of MetS, with a significant linear trend. Moreover, among the components of MetS, 24-hour urinary microalbumin was positively correlated with elevated FBG, and this correlation exhibited a linear pattern. The findings from this population-based study demonstrate a strong association between urinary microalbumin and metabolic disorders, with elevated urinary microalbumin serving as an independent predictor of MetS risk. Our results clearly highlight the public health significance of reducing 24-hour urinary microalbumin levels in the prevention of MetS.

Currently, the underlying mechanisms linking urinary microalbumin levels to metabolic disorders have not been fully elucidated. MetS is essentially a disorder of glucose and lipid metabolism, but studies have suggested that it may also fall into the category of arteriolar diseases24. It is hypothesized that the development of MetS may first induce vascular damage and endothelial dysfunction, which in turn leads to increased urinary microalbumin excretion. Conversely, increased urinary microalbumin excretion can further promote the occurrence and progression of MetS and type 2 diabetes mellitus (T2DM), thereby forming a vicious cycle25. On the other hand, various components of MetS (such as abdominal obesity, elevated blood pressure, and elevated FBG) can increase endothelial permeability and elevate glomerular capillary pressure, which in turn causes renal function impairment and ultimately results in increased urinary microalbumin excretion25,26,27.​.

This study has several notable strengths. Firstly, we employed 24-hour urine collection for measuring various indicators. Despite the complexity of this method, it is widely recognized as the gold standard for accurately assessing sodium and potassium intake, as well as precisely reflecting urinary microalbumin excretion levels17,22,28. Secondly, our study included a sufficiently large sample size, allowing for the exclusion of patients with hypertension and diabetes mellitus. This approach avoids the potential interference from medication use and treatment interventions in patients with chronic diseases on the measurement of 24-hour urinary excretion indicators. Thirdly, throughout the data collection process, we strictly adhered to standardized protocols and implemented rigorous quality control measures to ensure data accuracy and consistency.

This study also has certain limitations. First, despite adjusting for multiple covariates in our analysis, we were unable to fully eliminate potential confounding factors, such as dietary habits and renal function status. Specifically, our questionnaire only collected data on hypertension and diabetes history as well as their medication status, but lacked information on kidney disease history or other medication use (beyond hypertension and diabetes), which may have influenced urinary excretion indicators. Second, due to logistical challenges and funding constraints, urine collection was limited to a single 24-hour period, which may have compromised the accuracy of our estimates of excretion levels29,30. Furthermore, as this was a cross-sectional study, our design inherently limits the ability to establish causal relationships between urinary albumin excretion and MetS. Therefore, we recommend that future prospective studies be conducted to further validate our findings, with more comprehensive collection of information on chronic diseases (including kidney disease) and medication use to better control for confounding factors.

Conclusion

This study demonstrated that 24-hour urinary microalbumin excretion is independently and dose-dependently associated with the risk of metabolic syndrome (MetS) in the general population of Zhejiang Province. Clinically, this finding highlights the potential of 24-hour urinary microalbumin as a practical, accessible biomarker for early identification of individuals at high risk of MetS, particularly in primary care settings where routine metabolic parameter screening may be limited. Incorporating urinary microalbumin assessment into clinical workflows could facilitate timely interventions (e.g., lifestyle modification or targeted monitoring) to reduce MetS incidence and alleviate its associated public health burden in Zhejiang. Given the cross-sectional nature of our data, further prospective studies are warranted to validate the causal relationship and clarify the underlying mechanisms, which will strengthen the clinical utility of urinary microalbumin in MetS prevention and management.