Characteristics of study population
In our comprehensive baseline table1 analysis involving 54,555 participants, key demographic, clinical, and lifestyle variables were compared between those with MetS (N=14,670) and those non-MetS (N=39,885), as well as between females (N=26,796) and males (N=27,759).Age was significantly higher in the MetS group with a mean of 48.60 ± 0.42 years compared to 39.24 ± 0.30 years in the non-MetS group, indicating a strong age-related increase in MetS prevalence (P<0.001). The mean age difference between females and males was also statistically significant (42.80 ± 0.34 years vs. 40.43 ± 0.34 years, P =0.002). Gender distribution showed significant differences, with a slightly higher proportion of females in the MetS group (52.25%) compared to males (47.75%), supported by a chi-square test result (χ²=88.291, P <0.001). Racial composition showed that Non-Hispanic Whites were more prevalent in the MetS group compared to the non-MetS group, with statistically significant differences (χ²=124.057, P <0.001). There were no significant racial differences between genders (χ²=4.335, P =0.227). Educational levels varied significantly between the MetS and non-MetS groups. Participants in the MetS group were less likely to have attended college or above, which was statistically significant (χ²=2309.370, P <0.001). Differences between genders in education were also significant (χ²=130.371, P <0.001). PIR differed with the MetS group showing a lower mean PIR of 2.79 ± 0.05 compared to 2.90 ± 0.05 in the non-MetS group (P <0.001). Between genders, females had a lower PIR compared to males (P <0.001).
BMI was significantly higher in the MetS group (30.99 ± 0.14 vs. 27.21 ± 0.10, P <0.001). BMI differences were also significant between females and males (27.35 ± 0.14 vs. 27.07 ± 0.08, P <0.001), reflecting higher obesity rates among females. Prevalence of Hypertension, DM, and Hyperlipidemia was markedly higher in the MetS group, demonstrating strong links to MetS (Hypertension χ²=62.792, P =0.01; DM χ²=4339.643, P <0.001; Hyperlipidemia χ²=2741.282, P <0.001). Smoking Status showed significant differences with former smoking being more prevalent in the MetS group (χ²=615.222, P <0.001). HEI and PA-MET scores indicated poorer dietary habits and slightly higher physical activity levels in the MetS group, suggesting lifestyle impacts on metabolic health (P <0.001 for both comparisons).
Regression Analysis on cystatin C and MetS Correlations
In our regression analysis conducted as part of the study using NHANES 1999-2004 data, we meticulously explored the relationship between cystatin C and MetS, adjusting for various demographic, lifestyle, and clinical factors across multiple models detailed in table2. The initial crude model provided an unadjusted overview, showing a significant association between cystatin C and MetS, with an odds ratio (OR) of 4.42 and a confidence interval (CI) from 3.81 to 5.13, indicating a strong positive correlation (P<0.001). As we progressed to Model 1, which adjusted for age, sex, race, ethnicity, education, and PIR, the strength of the association between cystatin C and MetS increased, with an OR of 3.01 (CI: 3.51 to 3.60, P <0.001). Further adjustments in Model 2, which included additional variables such as DM, hypertension, hyperlipidemia, BMI, and smoking status, intensified the association, presenting an OR of 2.53 (CI: 2.08 to 3.08, P <0.001). The most comprehensive analysis, Model 3, incorporated liver function tests such as ALT, AST, TBIL, ALP, TP, and ALB, further refining the relationship. This model showed an OR of 1.69 (CI: 1.31 to 2.18, P <0.001), illustrating that even after controlling for a broad range of potential confounders, cystatin C remains significantly associated with MetS. The statistical significance of the findings, supported by P-values less than 0.05 across all comparisons and models, confirms the robustness of the associations despite the comprehensive adjustments for multiple covariates. The adjusted R-squared values increased with each model, indicating a better fit and explaining a significant proportion of the variance in cystatin C levels based on the included predictors.
In our study detailed in table 2, we explored the relationships between urea nitrogen, uric acid, HDL, and cystatin C across several progressively comprehensive regression models. Initially, the crude model highlighted significant associations with urea nitrogen showing an OR of 1.14, indicating a robust link to increased cystatin C levels, with this association remaining strong across all models despite adjustments for various covariates (OR in Model 3: 1.09). Similarly, uric acid started with an OR of 1.74 in the crude model, affirming a strong positive relationship with cystatin C levels; this association persisted albeit slightly reduced in the fully adjusted Model 3 (OR: 1.42). Conversely, HDL demonstrated an inverse relationship with cystatin C. In the crude model, an OR of 0.34 indicated a significant negative association, suggesting higher HDL levels correspond to lower cystatin C levels. This relationship remained robust and became even more pronounced in the most comprehensive model (Model 3), where the OR was drastically reduced to 0.065, illustrating the protective role of HDL against elevated cystatin C levels. Each model incorporated additional variables to control for potential confounding factors. Model 1 adjusted for basic demographic factors such as age, sex, race, ethnics, education, and PIR, while Model 2 included health-related variables like DM, hypertension, hyperlipidemia, BMI, and smoking status. Model 3 was the most extensive, also accounting for liver function measures including ALT, AST, TBIL, ALP, TP, and ALB. The progression through these models not only supported the initial findings but also highlighted the adjustments' impact, as seen in the slightly varying ORs. The adjusted R-squared values increased with the addition of more covariates, indicating an improved fit and explaining a larger proportion of the variance in cystatin C levels based on the included predictors. The F-changes and P-values were consistently significant across all models for each biomarker, validating the robustness of the associations despite the comprehensive adjustments.
Mediating Effects of urea nitrogen, uric acid, and HDL on the relationship between MetS and cystatin C
In our comprehensive mediation analysis in table 3 using NHANES 1999-2004 data, we explored how urea nitrogen, uric acid, and HDL influence the relationship between MetS and cystatin C levels. This analysis adjusted for various demographic and clinical factors, offering a nuanced understanding of the pathways through which these biomarkers mediate the impact of MetS on cystatin C.
The total effect of MetS on cystatin C, not accounting for any mediators, was significantly positive, indicating a strong and direct relationship (0.047, 95% CI: 0.036-0.060, P <0.001). When urea nitrogen was considered as a mediator, the indirect effect was 0.011 (95% CI: 0.006-0.020, P <0.001), and the direct effect was 0.036 (95% CI: 0.026-0.050, P <0.001). Urea nitrogen accounted for approximately 24.19% of the effect of MetS on Cystatin C levels.
Similarly, when uric acid was analyzed as a mediator, it accounted for a more substantial portion of the relationship, with an indirect effect of 0.022 (95% CI: 0.020-0.030, P <0.001) and a direct effect of 0.024 (95% CI: 0.013-0.040, P <0.001). Uric acid mediated about 48.13% of the effect, indicating its significant role in the metabolic processes associated with MetS impacting cystatin C levels. The role of HDL was even more substantial; it mediated 52.58% of the effect (indirect effect: 0.022, 95% CI: 0.009-0.030, P<0.001; direct effect: 0.025, 95% CI: 0.021-0.030, P <0.001). This highlights HDL's protective function, mitigating the negative implications of MetS on cystatin C levels.
These mediating effects, supported by consistently significant p-values across all models, underline the intricate interplay between these biomarkers and MetS in influencing cystatin C levels. Adjustments for factors such as age, sex, race, education, poverty status, diabetes, hypertension, hyperlipidemia, BMI, smoking, and liver function tests ensured that the findings were robust and reflective of a broad spectrum of potential influences. The analysis provides essential insights into potential therapeutic targets and preventive strategies in managing MetS, emphasizing the critical roles of urea nitrogen, uric acid, and HDL in this context.
Complex interactions in metabolic health: a GAM model analysis
In our GAM analysis in table S1 and fig.1 utilizing NHANES data from 1999-2004, we examined the nuanced relationships among MetS, HDL, uric acid, and urea nitrogen and their impact on cystatin C levels (Table S1) (Fig.1). The model was comprehensive, incorporating adjustments for an extensive array of variables including demographic factors such as age, sex, and ethnicity, educational attainment, PIR, clinical health markers such as DM, hypertension, hyperlipidemia, BMI, liver function tests (AST, TBIL, ALP, TP, ALB, GGT), and lifestyle factors like smoking. The results indicated significant individual effects of the biomarkers on cystatin C levels. Urea nitrogen showed a particularly strong positive association, with an estimate of 0.0179 and an extremely significant P-value (<0.001). HDL demonstrated a negative impact on cystatin C levels, suggesting its protective role against renal stress, with an estimate of -0.0569 and similarly high statistical significance. MetS was associated with an increase in cystatin C levels, indicated by an estimate of 0.0286, highlighting its adverse impact on renal function. Notably, the interaction between urea nitrogen and uric acid showed a significant effect (estimate 0.00119, P <0.001), suggesting that combined elevations in these biomarkers synergistically increase cystatin C levels. The three-way interaction between MetS, HDL, and urea nitrogen was also significant (estimate -0.00232, P <0.003), pointing to a specific modulatory effect of HDL in the presence of MetS and elevated urea nitrogen, potentially buffering the negative impacts on renal function. These findings underline the complexity of metabolic interactions affecting renal health and emphasize the need for a multifactorial approach in managing patients with metabolic disturbances. The model’s adjusted R-squared value of 0.408 indicates a good fit, explaining approximately 40.8% of the variability in cystatin C levels based on the predictors included. The F-statistic (83.7 on 29 and 35016 degrees of freedom) and a P -value of <0.001 confirm the statistical robustness and significance of the model, validating the comprehensive nature and predictive power of our analysis in understanding and potentially managing conditions influencing renal function.
Fig.1 further presents the results of a Generalized Additive Model (GAM) analysis exploring the relationships between urea nitrogen, HDL, and uric acid with cystatin C levels. The top-left plot shows a non-linear relationship between urea nitrogen and cystatin C, where cystatin C levels increase with urea nitrogen up to approximately 60 mg/dL, after which they decrease until about 60 mg/dL and increase until about 80 mg/dL. The top-right plot displays a relatively flat line, indicating a weak or negligible relationship between HDL levels (ranging from 1 to 4 mg/dL) and cystatin C. The bottom plot shows a mostly flat relationship with slight non-linearity between uric acid (ranging from 0 to 14 μmol/L) and cystatin C, suggesting a weak association.
Subgroup analysis of urea nitrogen, uric acid, HDL, and cystatin C relationship across MetS
In our stratified analysis in table 4 examining the association between cystatin C levels, segmented into quartiles from Q1 to Q4, and the prevalence of MetS across different demographic and physiological characteristics, we observed consistent trends indicating an increased risk of MetS as cystatin C levels rose. This relationship was statistically significant across multiple stratifications, demonstrating the robust association between renal function markers and metabolic health risks.
For sex, the progression of cystatin C levels from Q1 to Q4 showed a stark increase in MetS risk. Males in the highest quartile (Q4) had a risk ratio of 3.36 (P <0.001) compared to the baseline (Q1), while females exhibited an even higher risk at 4.06 (P <0.001). This trend was similarly significant, with p-values for trend under 0.001 for both genders, highlighting a strong, consistent increase in MetS risk across increasing cystatin C levels. Age presented a significant modifier of the relationship between cystatin C levels and MetS. Older adults (≥65 years) exhibited a higher sensitivity to changes in cystatin C levels, with those in the highest quartile having 2.86 times the risk of MetS compared to their first quartile counterparts (P <0.001). The trend across age groups remained significant, with those in the 40-65 years bracket also showing a notable increase in risk (3.12 times higher in Q4, P <0.001). The impact of socioeconomic status, as indicated by the poverty level, showed that individuals with a higher PIR (>3.5) faced a 4.15 times higher risk of MetS in Q4 compared to Q1 (P <0.001), with the trend across economic levels remaining statistically significant (P for trend <0.001). When examining the influence of existing conditions such as DM and hyperlipidemia, the data indicated that individuals with DM had a higher risk in the fourth quartile at 2.5 times (P<0.001), whereas those with hyperlipidemia had a 3.19 times higher risk (P<0.001), suggesting that these conditions exacerbate the impact of higher cystatin C levels on MetS risk. Smoking status also significantly interacted with cystatin C levels in predicting MetS. Current smokers in the highest quartile showed a 3.27 times increased risk (P<0.001), emphasizing the compounded risk factors of smoking and poor renal function on metabolic health. Lastly, ethnicity played a role in the risk profiles, with Non-Hispanic Whites in the highest quartile facing a 4.18 times higher risk (P<0.001) and other races also showing increased risks albeit at slightly lower magnitudes. This underscores potential disparities and the varying impact of renal function on metabolic health across different ethnic groups. Moreover, Fig.S1 shows that the relationship between cystatin C levels and metabolic variables varies significantly by gender, BMI, and age. Individuals with metabolic syndrome (MetS) often exhibit different trends compared to those without MetS, highlighting the impact of MetS on these associations. Notably, females with a higher BMI (≥25) and younger individuals (10-40 years old) tend to show more pronounced relationships between cystatin C and the metabolic variables.
These results, characterized by consistently low p-values and significant trends across various subgroups, underline the strong predictive value of cystatin C levels for MetS. The data highlight the importance of considering individual and demographic differences when assessing metabolic health risks associated with elevated cystatin C levels. The robust associations across sex, age, socioeconomic status, existing health conditions, smoking habits, and ethnicity stress the multifaceted nature of MetS and the key role of renal function as an indicator of broader health issues. This analysis not only confirms the relevance of cystatin C as a biomarker for MetS but also points towards targeted interventions that could potentially mitigate these risks across diverse populations.