Nonlinear relationship between serum Klotho and chronic kidney disease in US adults with metabolic syndrome

Abstract

Background: Current evidence regarding the effects of serum Klotho among patients with metabolic syndrome (MetS) is scarce. This study explored the relationship between serum Klotho levels and the odds of chronic kidney disease (CKD) in middle-aged and older populations with MetS.

Materials and methods: This cross-sectional study analyzed data from 4870 adults aged 40-79 years who participated in the National Health and Nutrition Survey (NHANES) from 2007 to 2016. CKD was identified at urinary albumin to creatinine ratio (UACR) of 30 mg/g or higher and/or an estimated glomerular filtration rate (eGFR) below 60 mL/min/1.73 m². Measurement of serum Klotho concentration was determined via enzyme-linked immunosorbent assay (ELISA) and subsequently divided into four quartiles (Q1-Q4). The NHANES criteria were followed in calculating the sampling weights. Multivariable logistic regression models were employed to assess the correlation between Klotho and CKD, while generalized linear models with cubic spline functions and smooth curve fitting were utilized to detect any nonlinear relationship. Additionally, subgroup analysis and a range of sensitivity analyzes were conducted.

Results: Results showed that a nonlinear L-shaped relationship existed between serum Klotho levels and CKD risk, with the lowest prevalence observed at 9.63-9.94 pg/mL Klotho concentrations. With a two-segment linear regression model, an inflection point of 9.88 pg/mL was noted. Hypertension status was identified as an interaction mediator (P _interaction = 0.006). Sensitivity analysis showed stable results.

Conclusions: A nonlinear L-shaped relationship exists between serum Klotho levels and risks of CKD among middle-aged and older adults with MetS, with the lowest prevalence observed at 9.63 to 9.94 pg/mL Klotho concentrations. Our findings, if replicated, underscore the need to estimate the optimal serum Klotho concentrations and the consequential inverse relationship, thus implying the potential of Klotho as both a serum biomarker and a possible preventive or therapeutic intervention.

Keywords: L-shaped; NHANES; chronic kidney disease; metabolic syndrome; α-Klotho.

Figure 1

Flow diagram of the screening and enrollment of study participants. MetS, metabolic syndrome; CKD, chronic kidney disease; PIR, family poverty income ratio; BMI, body mass index; CVD, cardiovascular disease.

Figure 2

Serum Klotho levels across CKD stages. The level of serum Klotho decreased with more advanced stage (P<0.001). CKD, chronic kidney disease.

Figure 3

Association between log2-transformed Klotho and CKD. Model 1: Crude model. Model 2: Adjusted for age, sex and race. Model 3: Adjusted for Model 2 + HbA1c, CVD, anti-hypertensive medication use and lipid-lowering medication use. Model 4: Adjusted for Model 3 + education, PIR, waist circumference, smoking status, alcohol drinking status, protein intake, fiber intake, fat intake, carabohydrate, diabetes, hypertension, hyperlipidemia, anti-diabetic medication use, physical activity, serum calcium, serum phosphorus, components of MetS and the number of MetS components. OR, odds ratio; CI, confidence interval; CKD, chronic kidney disease; HbA1c, glycosylated hemoglobin A1c; CVD, cardiovascular disease; PIR, family poverty income ratio; MetS, metabolic syndrome; FBG, fasting blood glucose.

Figure 4

Smooth curve fitting. (A) Association between log2-transformed Klotho and the incidence of CKD. (B) Association between Klotho log2 transform and the incidence of CKD stratified by hypertension status. The solid and dashed lines represent the odds ratios and their corresponding 95% confidence intervals. Only 99% of the data is displayed after adjusting variables in Model 4. PIR, family poverty income ratio; HbA1c, glycosylated hemoglobin A1c; CVD, cardiovascular disease; MetS, metabolic syndrome; FBG, fasting blood glucose.

Figure 5

Associations between serum Klotho and CKD in different subgroups. Except for the stratification component itself, each stratification factor was adjusted for variables in Model 4. OR, odds ratio; CI, confidence interval; PIR, family poverty income ratio; HbA1c, glycosylated hemoglobin A1c; CVD, cardiovascular disease; HDL, high-density lipoprotein; WC, waist circumference.