Data sources and participant selection

The participants in this cross-sectional study were extracted from five consecutive survey cycles of the NHANES database spanning from 2007 to 2016. The NHANES, launched by the National Center for Health Statistics (NCHS) in the United States, conducts a comprehensive nationwide cross-sectional survey with the aim of gathering data on the health status of the American population. The survey employs a stratified multistage random sampling approach to ensure the representation of a national sample [22]. The research protocol was approved by the ethical review board of the NCHS. Written consent was obtained from all participants during recruitment. The NHANES data are publicly accessible and can be accessed on the website of the Centers for Disease Control and Prevention (CDC) at https://www.cdc.gov/nchs/nhanes/index.htm.

Data from the years 2007–2016 were extracted, involving a total of 50,588 participants. We excluded the following specific participants: (1) individuals aged under 40 years or over 79 years (n = 33,199); (2) those with incomplete data required for diagnosing metabolic syndrome (n = 1,494); (3) those without recorded Koltho protein data (n = 3,625); and (4) those lacking white cell count data (n = 1,319). Ultimately, 13,119 participants were selected and included in the analysis. The flowchart illustrating sample selection from NHANES 2007–2016 is presented in Figure S1.

Acquisition of serum klotho protein

The serum Klotho concentration was the primary exposure variable in this study and was analyzed in frozen serum samples from participants aged 40–79 years during the NHANES 2007–2016 period. The samples were stored at -80 °C and then sent to the Northwest Lipid Metabolism and Diabetes Research Laboratory at the University of Washington for analysis via ELISA kits from IBL International, Japan [23]. Validation of the ELISA kits was conducted prior to analysis. Among the 114 apparently healthy participants, the α-Klotho protein concentration ranged from 285.8 to 1638.6 pg/mL, with a mean of 698.0 pg/ml. Each ELISA plate included two quality control samples of low and high Klotho concentrations; if these fell outside the specified 2SD range, the sample analysis was repeated to ensure accuracy. The detection method had a sensitivity of 4.33 pg/mL, with intra- and interassay coefficients of variation less than 5%. The formal analysis involved two repeated tests for each sample, with the mean value serving as the final result. More detailed laboratory testing methods can be viewed on the website https://wwwn.cdc.gov/Nchs/Nhanes/2009-2010/SSKL_F.htm.

Diagnostic criteria for metabolic syndrome

Metabolic syndrome is a clustering of metabolic risk factors that can help identify individuals at increased risk of developing diabetes and CVD. According to the NCEP-ATP III criteria [1], diagnosing metabolic syndrome requires having at least three of the following metabolic abnormalities: (1) elevated waist circumference (WC): ≥88 cm for females or ≥ 102 cm for males; (2) elevated fasting blood glucose (FBG): FBG ≥ 100 mg/dL or undergoing hyperglycemia treatment; (3) elevated triglycerides (TG): ≥150 mg/dL or taking lipid-lowering medication; (4) reduced HDL-C: <50 mg/dL for females or < 40 mg/dL for males, or receiving treatment for this lipid anomaly; and (5) elevated BP: systolic blood pressure (SBP) ≥ 130 mmHg or diastolic blood pressure (DBP) ≥ 85 mmHg, or currently on antihypertensive therapy. Each participant had their blood pressure measured twice, with the average value used to determine the final result. Data on antihypertensive, antidiabetic, and lipid-lowering drugs and disease diagnoses were all obtained through the study questionnaire.

Systemic inflammation-related indicators

Previous studies have confirmed that systemic immune inflammation increases the risk of metabolic syndrome [18]. Additionally, the levels of leukocytes, neutrophils, lymphocytes, monocytes, and platelets, as well as the derived neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and systemic immune-inflammation index (SII), serve as markers of systemic inflammation [24, 25]. In our study, leukocyte, neutrophil, lymphocyte, monocyte, and platelet counts were measured via an automated hematology analyzer (Coulter DxH 800 analyzer) and aFre presented as 10^9 cells/L. The NLR and PLR are determined by dividing the neutrophil count by the lymphocyte count and the platelet count by the lymphocyte count, respectively. On the other hand, the SII is obtained by multiplying the platelet count by the neutrophil count and dividing the product by the lymphocyte count [18, 24]. Additionally, the monocyte-to-HDL ratio (MHR) is regarded as a valuable systemic inflammation marker in forecasting the prognosis of cardiovascular conditions, calculated as the ratio of monocyte count to HDL [26].

Covariables

Drawing on previous research [27,28,29], this study incorporates various confounding factors as covariables for analysis, including sex (male and female), age (continuous variable), race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races), family poverty income ratio (PIR, categorical variable: <1.3, 1.3–3.5, > 3.5 representing low, middle, and high income levels, respectively), educational attainment (categorical variable), marital status (married/cohabiting, widowed/divorced/separated, or never married), smoking (categorical variable), drinking status, physical activity (categorical variable), body mass index (BMI, continuous variable), estimated glomerular filtration rate (eGFR), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and daily energy intake. Additionally, building upon prior literature [30], drinking status is divided into four categories: (1) never alcohol consumers; (2) former drinkers; (3) current moderate drinkers; and (4) current heavy drinkers. BMI was calculated as weight (kg) divided by the square of height (m). The eGFR was calculated via the chronic kidney disease epidemiology collaboration (CKD-EPI) formula [24]. Additionally, this study assessed daily total energy intake by utilizing information from the first 24-hour dietary recall interview [31].

Statistical analysis

This study considered sample weighting, stratification, and clustering. The NHANES weighting principles and appropriate sample weights were combined to ensure the national representativeness of the study population. Descriptive analysis utilized means (95% CI) and counts (percentages) to depict both quantitative and qualitative data. Demographic variances between groups were evaluated via chi-square tests and t tests. Additionally, odds ratios (ORs), β coefficients and 95% confidence intervals (CIs) were calculated via survey-weighted multivariable logistic regression and linear regression. Missing covariable data were handled by encoding categorical variables as a separate category for missing records and imputing continuous variables with the mean. All the statistical analyses were carried out via R statistical software (version 4.4.0; https://www.R-project.org), with two-tailed p values < 0.05 considered statistically significant.

Our study initially divided Klotho levels into four quartiles (Q1, Q2, Q3, and Q4) as categorical variables, with Q1 as the baseline group for analysis. To address the skewed distribution of the serum Klotho protein concentration, the data were ln-transformed (Fig S2A). Model 1 was unadjusted, whereas Model 2 controlled for age, sex, race/ethnicity, marital status, PIR, education, and BMI. Model 3 was additionally adjusted for ALT, AST, and eGFR, and Model 4 included further adjustments for alcohol consumption, smoking, physical activity, and energy intake. This study examined the model through several methods. First, a scatter plot was utilized to investigate the linear relationship between exposure and outcome variables. Second, Q-Q plots and residual plots were employed to examine the normal distribution and independence of the residuals. Moreover, residual versus fitted value plots were performed to assess the homoscedasticity of the residuals [32]. The model in this study satisfied the aforementioned assumptions. Finally, multicollinearity in the regression model was evaluated via the variance inflation factor (VIF). Consistent with previous studies, all the VIF values in this study were less than 5 [33]. Building upon the Akaike information criterion (AIC), a restricted cubic spline (RCS) regression model with knots at the 10th, 50th, and 90th percentiles was constructed to explore the dose-response relationship between the Klotho protein and the outcome variables of interest [34]. This study employs RCS to examine the dose-response relationships involving serum Klotho protein and various outcomes, including metabolic syndrome, elevated WC, elevated FBG, elevated TG, reduced HDL-C, and elevated BP, while maintaining the same covariables as Model 4. Interaction and subgroup analyses were performed to detect potential variations among different populations on the basis of factors such as age (< 60 years and ≥ 60 years), sex, race/ethnicity, marital status, education, PIR, smoking, drinking, physical activity, and BMI subgroups (< 25 kg/m2 and ≥ 25 kg/m2). The total sample was stratified by median total energy intake (< 1915 kcal/day and ≥ 1915 kcal/day). AST or ALT > 70 IU/L indicates potential liver disease [35], and an eGFR < 60 ml/min/1.73 m2 suggests chronic kidney disease [36]. Given the significant association between Klotho and the processes of aging-related inflammation and oxidative stress, this research employes a 10-year age range to investigate the variation in Klotho protein ORs across different age groups. The initial dataset comprises individuals aged 40–50 years, followed by the subsequent group aged 50–60 years, and so forth. Furthermore, considering the involvement of systemic inflammation in the progression of metabolic syndrome, we posit that the Klotho protein might alleviate the progression of metabolic syndrome by exerting its anti-inflammatory effects. Consequently, this study investigated the mediating effect of systemic inflammation-related indicators on the associations between serum Klotho concentrations and metabolic syndrome and its components.

Sensitivity analysis

Sensitivity analysis was employed to confirm the robustness of the study findings. Initially, individuals with serum Klotho levels below the Q1-interquartile range (IQR) and above the Q3 + IQR were excluded (n = 437) to reduce the influence of outliers on the analytical outcomes (Fig S2B). After excluding participants with missing covariable information (n = 5307), the relationships between serum Klotho concentrations and metabolic syndrome, along with its components, were further assessed in the complete dataset.