Serum Protein α-Klotho Mediates the Association between Lead, Mercury, and Kidney Function

doi:10.21203/rs.3.rs-3939956/v1

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Research Article

Serum Protein α-Klotho Mediates the Association between Lead, Mercury, and Kidney Function

https://doi.org/10.21203/rs.3.rs-3939956/v1

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Background

Exposure to heavy metals, particularly lead and mercury, has been identified as a significant risk factor for renal dysfunction, often through mechanisms involving oxidative stress. Despite extensive research, the specific role of serum α-klotho in modulating the effects of these metals on kidney function remains unclear. This study aims to elucidate the relationship between serum levels of lead, mercury, and renal function, investigate oxidative stress's potential modulatory effects, and explore the mediating role of serum α-klotho in this context among US adults, as derived from the National Health and Nutrition Examination Survey (NHANES) data spanning 2007 to 2016.

Methods

In a cross-sectional analysis of 11,032 adults aged 40 to 79 years from NHANES, we measured serum lead and mercury levels via inductively coupled plasma mass spectrometry and serum α-klotho levels using enzyme-linked immunosorbent assay (ELISA). Kidney function was evaluated through the creatinine-based estimated glomerular filtration rate (eGFR). Multivariable linear regression models were applied to investigate the correlations between serum heavy metal concentrations, serum α-klotho, and eGFR. Additionally, a mediation analysis model was employed to determine the role of serum α-klotho in mediating the relationship between heavy metal exposure and renal function.

Results

Our findings indicate a significant association between serum lead and mercury levels and reduced eGFR, suggesting impaired renal function with increased heavy metal exposure. Mediation analysis revealed that serum α-klotho mediated 6.10–9.75% of the effect of serum lead and mercury on eGFR, highlighting its role in the pathway between heavy metal exposure and kidney function. Subgroup analysis further specified that serum α-klotho significantly mediates the relationship for lead in women and individuals aged 40–69, whereas its mediating role for mercury did not show distinct patterns across gender and age groups.

Conclusions

The study demonstrates that serum lead and mercury are negatively correlated with renal function, with serum α-klotho playing a mediating role in this association. These findings underscore the importance of considering serum α-klotho in the context of heavy metal exposure and kidney health, offering new insights into potential preventive and therapeutic mechanisms for renal function impairment. Further research is warranted to explore the clinical applications of targeting serum α-klotho in mitigating the adverse effects of heavy metals on the kidneys.

Kidney function

α-Klotho

Lead

Mercury

Mediation

Estimated glomerular filtration

The current global burden of chronic kidney disease (CKD) is severe. It has an insidious onset and a long incubation period. As a progressive disease, it may lead to renal failure, cardiovascular complications, and premature death (1, 2), placing a considerable burden on families. It is estimated to affect 11–15% of the world’s population, and CKD is expected to become the fifth leading cause of death globally by 2040 and will become one of the fastest-growing significant causes of death (3). Therefore, it is essential to prevent CKD by reducing the risk factors for renal function.

Heavy metals are widely present in the environment. They can enter the human body through skin, diet, breathing, etc., causing neurological, metabolic, renal, cardiovascular, endocrine, and autoimmune diseases, as well as certain malignant tumor diseases, such as gastric cancer and liver cancer. Even deficient levels can cause damage to organ function (4, 5). The kidney is the most critical target organ for metal toxicity (6), and many divalent ions and metals are concentrated in the glomerulus, which may lead to renal cell damage, glomerulosclerosis, and a considerable reduction in functional renal cells (7). Previous studies have shown (8) that heavy metals can promote the production of reactive oxygen species (ROS), oxidative stress, and kidney cell damage. Some researchers believe (9) that heavy metals can cause damage or apoptosis of renal tubular epithelial cells by destroying the oxidative stress system and inflammatory response—the balance of heme oxygenase activity, impairing renal tubular reabsorption and ultimately leading to Decreased kidney function. Therefore, oxidative stress is an essential mechanism of metal damage to renal function (10–16).

The protein Klotho encoded by the Klotho gene was accidentally discovered in 1997 (17). It is a single-channel transmembrane protein with three subtypes: α-, β-, and γ-Klotho. It is mainly expressed in the kidney, brain, and parathyroid glands (18, 19). It is involved in many physiological processes, including phosphate homeostasis, insulin signaling, and regulation of oxidative stress (20). The expression level and circulating level of Klotho decrease during the aging process, leading to intimal hyperplasia, arteriosclerosis, hypertension, Alzheimer’s disease, etc., so it is also considered an anti-aging protein (18). Animal studies have shown (21–24) that α-Klotho can inhibit kidney damage and fibrosis and delay the progression of CKD by inhibiting TGF-β1 activity and Wnt signaling activity. α-Klotho plays a positive role in producing antioxidant enzymes (25). Its soluble form can slow down tumor progression, delay cell senescence, promote the synthesis of antioxidant substances, and have neuroprotective effects (26–29). In animals and humans with CKD of various causes, renal α-Klotho expression and soluble Klotho in blood and urine are reduced (30). Moreover, α-Klotho will gradually decrease with the progression of CKD (31), which may be related to the hypermethylation of the α-Klotho encoding gene (32). Studies have shown (33–36) that patients with lower eGFR also have lower α-Klotho. Serum α-Klotho levels are negatively correlated with the prevalence of CKD. Klotho may become a biomarker of kidney disease. However, the role of serum α-Klotho in the relationship between serum lead, serum mercury, and renal function has not been fully elucidated. We hypothesized that there is a link between α-Klotho, an antioxidant, and kidney function and lead and mercury. This study selected a cross-sectional study among middle-aged and older adults in the NHANES database from 2007 to 2016, to study whether serum α-Klotho can regulate the relationship between serum lead, mercury, and renal function.

Study population

The Centers for Disease Control and Prevention (CDC) conducts the National Health and Nutrition Examination Survey (NHANES), a nationally representative survey of the noninstitutionalized U.S. civilian population. NHANES includes interviews, physical examinations, and laboratory tests. Extensive data were collected through interviews, including demographic, socioeconomic, dietary and health-related questionnaire data (37). Laboratory tests and medical, dental and physiological examinations are performed by trained medical personnel. The NHANES dataset is publicly available at (https://www.cdc.gov/nchs/nhanes/index.htm).

Our study analyzed NHANES data from 2007 to 2016, during which serum α-Klotho levels were measured. We combined five NHANES cycles (2007–2016) with 50588 subjects (37). Among 50588 subjects, serum α-Klotho levels were analyzed in participants aged 40–79 (n = 13764). Of these participants, only 11036 had simultaneously measured blood lead and mercury levels. The final study population included 11032 participants (Fig. 1).

The NHANES research protocol was approved by the National Center for Health Statistics Institutional Review Board (https://www.cdc.gov/nchs/nhanes/irba98.htm). Additionally, all participants provided verbal and written consent for future research.

Determination of serum α-Klotho levels

Serum α-Klotho levels were analyzed in frozen serum samples aged 40–79 collected from the NHANES database between 2007 and 2016. Fresh-frozen serum samples, stored at − 80°C, were analyzed by the Centers for Disease Control and Prevention to the Northwest Lipid Metabolism and Diabetes Research Laboratories, Division of Metabolism, Endocrinology, and Nutrition, University of Washington. Serum α-Klotho levels were detected using an ELISA kit produced by Japan’s IBL International Company. The average of two repeated analyses was used for quality assurance, and samples with duplicate results that differed by more than 10% were reanalyzed. The resulting detection sensitivity was 4.33 pg/mL. Detection was performed in 114 samples from healthy volunteers, with a reference range of 285.8–1638.6 pg/ml and an average value of 698.0 pg/mL (38).

Determination of blood lead and blood mercury levels

All determination results were completed at the National Center for Environmental Health, and Centers for Disease Control and Prevention for analysis in accordance with the Laboratory Procedures Manual. Mass spectrometry was used to determine lead and mercury, and inductively coupled plasma mass spectrometry (ICP-MS) was used to determine lead and mercury levels in blood. The lowest detection limits for lead and mercury are 0.07ug/dL and 0.28ug/L respectively. The results field for analytes with values below the lower limits of detection were filled with an imputed fill value equal to the lower limit of detection divided by the square root of 2 (LLOD/sqrt [2]) (0.05 µg/dL for blood lead, 0.14 µg/L for blood mercury). From 2013 onwards, for participants 12 years and older, special sample weights were created for the subsample. These special weights accounted for the additional probability of selection into the subsample, as well as the additional nonresponse to these lab tests. Therefore, if a participant 12 years and older was selected as part of the one-half subsample, but did not provide a blood specimen, he/she would have the sample weight value assigned as “0” in his/her record. The accuracy and precision of all detection methods were controlled to adhere to the Scientific Quality Control and Quality Assurance Performance Standards for Environmental Health Laboratories (39).

Renal function assessment

Renal function assessment in this study was mainly performed using two parameters. Namely, eGFR and urinary albumin-to-creatinine ratio (UACR), the main focus of this study was eGFR, which was determined by the 2021 Chronic Kidney Disease Epidemic Collaboration equation (CKD-EPI) (40), which relies on serum creatinine levels. UACR was used as the outcome measure for sensitivity analysis, and its calculation formula (41) is:

$${UACR}_{\left(\frac{mg}{g}\right)}=\frac{{urinary albumin level}_{\left(\frac{mg}{dL}\right)}}{{urinary creatinine level}_{\left(\frac{g}{dL}\right)}}$$

Urinary albumin and creatinine levels were obtained from urine samples.

Covariates

Covariate data included demographic questionnaire data, socioeconomic status, lifestyle, and habit variables. Age is divided into two groups (40–59 years, 60–79 years). Body mass index is calculated by dividing weight (kilograms) by the square of height (meters) (18.5–25 is normal, 25–30 is overweight, and > 30 is obese). Race is divided into White, Black, Mexican, and other races. Education Degree is divided into Less than high school (less than 9th, High school (9–11th grade [including 12th with no diploma], high school or equivalent), and College or higher (some college or associate’s degree or college graduate or above). Sports activities are divided into Vigorous, Moderate, and low).

Health status included hypertension, defined as mean systolic blood pressure (SBP) ≥ 140 mmHg and/or mean diastolic blood pressure (DBP) ≥ 90 mmHg, or self-reported diagnosis of hypertension and taking hypertension medication. Diabetes is defined as (1) Doctor diagnosed diabetes, (2) Glycated hemoglobin > 6.5%, (3) Fasting blood glucose ≥ 7.0mmol/L, (4) Random blood glucose ≥ 11.1mmol/L, (5) Glucose tolerance test 2-hour blood glucose ≥ 11.1mmol /L, (6) Use diabetes drugs or insulin. Atherosclerotic heart disease refers to a history of coronary heart disease, angina, stroke, and heart attack.

Statistical analysis

First, α-Klotho, blood lead, blood mercury, and eGFR were log-transformed to reduce skewness. Categorical variables were assessed by calculation of (percent), while continuous variables were determined by (M ± SD), and nonparametric continuous variables were analyzed by calculating (IQR). Pearson correlation coefficients were calculated for eGFR, blood lead, blood mercury, and Klotho levels and their corresponding log-transformed values. Linear regression analysis was used to evaluate the correlation between renal function as the dependent variable and blood lead and blood mercury as independent variables. Beta coefficients and 95% CI were calculated. Generalized additive models (GAM) were used for nonlinear relationships. Models were adjusted for age, sex, race, education, income, BMI and physical activity, hypertension, diabetes, and cardiovascular disease.

Second, we explored the potential mediating role of Klotho in the association between metals and renal function. Causal mediation analysis was used to conduct mediation analysis linear regression models for metals (blood lead, blood mercury), mediators, and outcomes. Use the bootstrap method to estimate confidence intervals. The logarithm of metals (lead, mercury) is the exposure variable, the logarithm of serum anti-aging protein α-Klotho is the mediating factor, and the logarithm of eGFR is the outcome variable. The following results were measured:

The effect of metals on α-Klotho;

The effect of α-Klotho on renal function;

The effect of metals on overall renal function;

Considering the overall impact of metals on renal function in the case of α-Klotho protein, the proportion of intermediary factors is calculated using the following formula:

$$\left(\beta total effect - \beta direct effect\right)/\beta total effect \times 100$$

Third, subgroup analysis was performed to explore whether age or gender modulates the mediating effect of serum α-Klotho on the correlation between metals and eGFR. In mediation analyses, stratification was performed by age of analysis (40–59 years, 60–79 years) or gender (male, female). Since MICE can handle variable types (continuous variables, binary, unordered categorical, ordinal categorical), we performed multiple imputations on covariate data with < 5% missing data. The imputation procedure was performed using a linear regression method for continuous variables, and an ordinal or binary logistic regression model for categorical variables, chained equations with m = 5. Use Rubin’s rules to summarize the effects of the five data sets to obtain a data set after the combined effects with the most minor error (42, 43). To verify the robustness of the results after imputation, we used sensitivity analysis to explore the role of serum α-Klotho in the relationship between metals and UACR. Based on NHANES guidelines, we used weighted estimation with MEC weights. All analytical calculation processes were performed in R software (Version 4.3.1), and two-sided p < 0.05 indicated statistical significance.

Characteristics of study participants

Finally, 11032 eligible participants aged 40–79 years from 2007 to 2016 were selected from the NHANES database in Table 1. The average age (standard deviation) of the participants was 57.76 (10.89) years old; 49% were male, and most of them were white (44.1%) and had a college degree (48.7%). Most of the patients were obese (41.1%), without diabetes (82.3%), and had hypertension (53.4%). The values of blood lead (Median [IQR]) and blood mercury (Median [IQR]) in the total study population were 0.07 [0.05, 0.10] umol/L and 0.93 [0.50, 1.90]ug/L, respectively. The α-Klotho levels in the overall study population were 802.80 [654.65, 996.20] pg/ml. Since only half of the blood lead and blood mercury samples were tested in the NHANES cycle starting from 2013–2014, the number of study participants decreased as the study cycle increased. In 2007–2008, the number of participants was 3002; in 2009–2010, it was 2896; in 2011–2012, it was 2453; in 2013–2014, it was 1375; and in 2015–2016, it was 1306 (Table 1).

Table 1

Characteristics of 11032participants aged 40–79 years in the NHANES from 2007 to 2016
	Overall
n	11032
Gender = Male (%)	5408(49.0)
Age (mean (SD))	57.76 (10.89)
Klotho pg/mL(median [IQR])	802.80 [654.65, 996.20]
Bld_lead_umol/L (median [IQR])	0.07 [0.05, 0.10]
bld_mercury_ug/L (median [IQR])	0.93 [0.50, 1.90]
UACR (median [IQR])	5.38 [0.00, 10.75]
eGFR mL/min.1,73m2(median [IQR])	92.00 [76.00, 103.00]
Race (%)
Black	2194 (19.9)
Mexican	1733 (15.7)
Other	2243 (20.3)
White	4862 (44.1)
Education (%)
College graduate or above	5375 (48.7)
Don't Know	4 ( 0.0)
high school	4098 (37.1)
Less Than 9th Grade	1552 (14.1)
Refused	3 ( 0.0)
Household_Income (%)
< 20000	4198 (38.1)
> 75000	958 ( 8.7)
20000–75000	5485 (49.7)
Don't know	155 ( 1.4)
Refused	236 ( 2.1)
BodyWeight (%)
Normal	2592 (23.5)
Obesity	4537 (41.1)
Overweight	3903 (35.4)
Physical_level (%)
Light	3326 (30.2)
Moderate	4579 (41.6)
Vigrous	3104 (28.2)
Hypertension = Yes (%)	5118 (46.4)
Diabetes_Status = Yes (%)	1941 (17.6)
Cardiovascular_disease = Yes (%)	1462 (13.3)
Year(%)
2007–2008	3002(27.2)
2009–2010	2896(26.3)
2011–2012	2453(22.2)
2013–2014	1375(12.5)
2015–2016	1306(11.8)

Relationship between serum α-Klotho, blood lead, blood mercury, and eGFR

Table 2 is a log-transformed matrix of the relationship between α-Klotho, blood lead, blood mercury, and eGFR. Serum α-Klotho has a negative correlation with serum lead (− 0.076, p < 0.001), and a positive correlation with serum mercury. (0.045, p < 0.05), eGFR has a negative correlation with serum lead (− 0.187, p < 0.001), and a positive correlation with serum mercury (0.33, p < 0.001)

Table 2

Pearson's correlation coefficients among log-transformed blood lead, blood mercury, eGFR, and α-klotho levels
	Klotho		Blood lead		Blood mercury		eGFR		Log klotho		Log blood lead		Log blood mercury
	(pg/mL)		(umol/L)		(ug/L)		(ml/min/1.73m2)		(pg/mL)		(umol/L)		(ug/L)
	r	p-value	r	p-value	r	p-value	r	p-value	r	p-value	r	p-value	r	p-value
Klotho	1
Blood lead	-0.056	< 0.001	1
Blood mercury	0.041	< 0.001	0.05	< 0.001	1
eGFR	0.119	< 0.001	-0.127	< 0.001	0.04	< 0.001	1
Log Klotho	0.957	< 0.001	-0.064	< 0.001	0.004	< 0.001	0.139	< 0.001	1
Log blood lead	-0.069	< 0.001	0.823	< 0.001	0.071	< 0.001	-0.184	< 0.001	-0.076	< 0.001	1
Log blood mercury	0.039	< 0.001	0.045	< 0.001	0.732	< 0.001	0.029	< 0.001	0.045	< 0.001	0.078	< 0.001	1
Log eGFR	0.122	< 0.001	-0.0149	< 0.001	0.041	< 0.001	0.946	< 0.001	0.149	< 0.001	-0.187	< 0.001	0.33	< 0.001

Linear relationship between serum lead and eGFR and UACR

Table 3 shows the relationship model between log-transformed glomerular filtration rate (eGFR), blood lead, and blood mercury. After adjusting for all variables, eGFR was inversely associated with lead (β [95%CI], − 0.0269 (− 0.0400, − 0.0138), p < 0.001). To analyze the results further, we used sensitivity analysis to analyze the association between log-transformed blood lead and UACR to confirm the findings. After adjusting for all covariates, we found that serum lead concentration was positively associated with UACR (β [ 95%CI], − 1.7176 (− 0.0400, − 0.0138), p < 0.001). (Table 5)

Table 3

Association between standardized log(lead), log(mercury) and log(eGFR ) among 11032 participants aged 40–79 years in the NHANES
		Weight βCoefficient(95%CI)for log(eGFR)
	Model1	Model2	Model3
Log(Lead)(umol/L)	-0.0200(-0.0324, -0.0075)**	-0.0230(-0.0353, -0.0106)***	-0.0269(-0.0400, -0.0138)***
Log(Mercury)(ug/L)	0.0116(0.0055, 0.0176)***	0.0107(0.0047, 0.0168)***	0.0086(0.027, 0.0145)***
Mode l1.adjusted for age, sex and race; Mode l2: model plus education, income, alcohol, smoke; Mode l3: Mode l2 plus BMI, hypertension, diabetes mellitus, Cardiovascular_disease, p<0.05, p < 0.01,**p < 0.001

The linear relationship between serum mercury and eGFR and the nonlinear relationship between serum mercury and UACR

Table 3 shows the relationship model between log-transformed glomerular filtration rate (eGFR) and blood mercury. After adjusting for all variables, eGFR was positively associated with mercury (β (95%CI), 0.0086 (0.027, 0.0145), p < 0.001). To analyze the results further, we used sensitivity analysis to analyze the association between log-transformed blood mercury and UACR to confirm the study results. After adjusting for all covariates, there was a nonlinear relationship between blood mercury and UACR. We used the generalized additive model (GAM), which showed a curvilinear relationship, and the effective degrees of freedom between UACR and serum mercury was 5.97 (p < 0.001) (Figure. 2).

Mediating role of serum α-Klotho in serum lead and mercury

We performed a mediation analysis to determine whether α-Klotho mediates the association between log-transformed metals (blood lead, blood mercury) and renal function, adjusting for potential confounders. The results showed that serum lead had a significant negative indirect effect on eGFR through serum α-Klotho (β[95%CI], − 0.0025(− 0.0040, 0), p < 0.001), indicating that serum α-Klotho has a partial mediating effect. Controlling for serum α-Klotho (β[95%CI], − 0.0253(− 0.0379, − 0.02), p < 0.001) remained significant, indicating both direct and indirect effects of the presence of blood lead. Approximately 9.75% (6.52, 19) of the effect of serum lead on eGFR is mediated by serum α-Klotho. Serum mercury has a positive indirect effect on eGFR through serum α-Klotho (β[95%CI], 0.0006 (0.0003, 0), p < 0.05), and a positive direct effect (β[95%CI], 0.0091 (0.0040, 0.01), p < 0.001), the effect of serum mercury on eGFR is about 6.10% (2.56, 29) mediated by serum α-Klotho (Table 4). To further analyze the mediating role of serum α-Klotho, we used UACR for sensitivity analysis. The results showed that serum α-Klotho did not mediate the association between serum lead, mercury and UACR (Table 4).

Table 4

Mediating Role of α-Klotho on Lead and Mercury's Association with eGFR and UACR in NHANES Participants Aged 40–79
	Total study population
	weighted β coefficient (95% CI)for eGFR
Medicator α-klotho	total effect	indirect effect	direct effect	proportion Mediated, %(95%)
log (Lead)(umol/L)	-0.0253 (-0.0324, -0.0075)***	-0.0025 (-0.0040, 0)***	-0.0228(-0.0353, -0.01)***	9.75(6.53, 19)****
log(Mercury)(ug/L)	0.091(0.0040, 0.01)***	0.0006(0.0003, 0)*	0.0085(0.0028, 0.01)**	6.10(2.56, 29)*
	weighted β coefficient (95% CI)for UACR
Medicator α-klotho	total effect	indirect effect	direct effect	proportion Mediated, %(95%)
log(Lead)(umol/L)	-1.8701(-1.9764, -1.47)***	‘0.0160(-0.0076, 0.03)	-1.8861(-1.9793, -1.48)***	-
log(Mercury)(ug/L)	-0.2035(-0.1963, 0.11)	-0.0036(-0.0085, 0)	-0.2000(-0.1929, 0.12)	-
stratification by age
weighted β coefficient (95% CI)for eGFR
Age	total effect	indirect effect	direct effect	proportion Mediated, %(95%)
40–59	-0.0386(-0.048, -0.02)***	-0.0027(-0.0036, 0)	-0.0360(-0.0448, -0.02)***	6.87(4.14, 11)***
60–79	-0.0644(-0.0908, -0.05)***	-0.0013(-0.0063, 0)*	-0.0631(-0.0854, -0.04)***	2.03(1.45, 10)*
40–59	5.38e-03(-2.38e-03, 0.01)	7.54e-04(-5.57e-05, 0)	4.63e-03(-2.74e-03, 0.01)	-
60–79	0.0059(-0.0004, 0.02)	0.0019(0.0002, 0)*	0.0040(-0.0024, 0.02)	-
weighted β coefficient (95% CI)for UACR
Age	total effect	indirect effect	direct effect	proportion Mediated, %(95%)
40–59	-1.9142(-1.9881, -1.37)***	0.0182(-0.0144, 0.03)	-1.9323(-1.999, -1.39)***	-
60–79	-1.3308(-1.7143, -1.01)***	0.0021(-0.0043, 0.03)	-1.3329(-1.7294, -1.02)***	-
40–59	-0.2250(-0.3008, 0.05)	-0.0052(-0.0075, 0)	-0.2199(-0.3014, 0.05)	-
60–79	0.1619(-0.0783, 0.35)	-0.0030(-0.0193, 0)	0.1649(-0.0676, 0.35)	-
stratification by gender
weighted β coefficient (95% CI)for eGFR
Gender	total effect	indirect effect	direct effect	proportion Mediated, %(95%)
male	-0.02360(-0.0404, -0.01)***	-0.0027(-0.0055, 0)***	-0.0209(-0.0362, -0.01)***	11.56(7.54, 31)***
female	-0.0264(-0.0462, -0.01)***	-0.0033(-0.0039, 0)**	-0.0231(-0.0442, -0.01)**	12.43(2.04, 17)**
male	-0.0002(-0.0067, 0.01)	0.0016(0.0002, 0)*	-0.0020(-0.0078, 0)	-
female	0.0154(0.0101, 0.02)***	0.0010(-0.0004, 0)	0.0144(0.0096, 0.02)***	-
weighted β coefficient (95% CI)for UACR
Gender	total effect	indirect effect	direct effect	proportion Mediated, %(95%)
male	-1.9587(-1.8996, -1.20)***	0.0297(-0.0148, 0.05)	-1.9883(-1.9195, -1.21)***	-
female	-2.1227(-2.2969, -1.58)***	-0.0037 (-0.0125, 0.02)	-2.1190(-2.3072, -1.59)***	-
male	-0.3345(-0.3330, 0.06)	-0.0183(-0.0180, 0.01)	-0.3162(-0.3285, 0.07)	-
female	-0.0592(-0.1166, 50.30)	0.0012(-0.00921, 0)	-0.0604 (-0.1162, 0.30)	-

Table 5

Association between standardized log(lead), log(mercury) and log(UACR) among 11032 participants aged 40–79 years in the NHANES
		Weight βCoefficient(95%CI)for UACR
	Mode l1	Mode l2	Mode l3
Log(Lead)(umol/L)	-1.8245(-0.0325, -0.0075)***	-1.7490(-0.0353, -0.0106)***	-1.7176(-0.0400, -0.01380)***
Log(Mercury)(ug/L)	0.0325(0.0055, 0.0176)	-0.0398(-0.0046, 0.01679)	-0.0353(0.0027,0, 0145)
Model adjusted for age,sex,race,education,income, BMI,hypertension,diabetes mellitus, Cardiovascular_disease, p<0.05, p < 0.01,**p < 0.001

Gender and age subgroup analysis

To examine the adjustment of age and sex on the mediating effect of serum α-Klotho on the relationship between serum lead, mercury and eGFR, we performed a subgroup analysis. The results of the study showed that the mediating role of serum α-Klotho in the relationship between serum lead and eGFR was for men (β[95%CI], − 0.0027 (− 0.0055, 0), p < 0.001), and for participants aged 40–59 years (β [95%CI], − 0.0027 (− 0.0036, 0), p < 0.001), mediator proportions were 11.56% (7.54, 31) and 6.87% (4.14, 11), respectively, while serum mercury did not Statistical significance (Table 4). After using UACR to replace eGFR for sensitivity analysis, the results showed that serum α-Klotho had no mediating effect on the relationship between serum lead, serum mercury and UACR (Table 4).

Key points of results

This study selected 11032 participants from the NHANES database between 2007 and 2016 aged 40–79 years to explore the mediating role of serum α-Klotho in the relationship between serum lead, mercury, and renal function (eGFR). After adjusting for covariates such as age, gender, family income, hypertension, and coronary atherosclerotic heart disease, our study found a significant negative linear correlation between serum lead and eGFR (− 0.0269 (− 0.0400, − 0.0138), p < 0.001), there is a positive linear correlation between serum mercury and eGFR (0.0086 (0.027, 0.0145), p < 0.01). In addition, the mediation analysis results showed that serum α-Klotho also mediated this association to a certain extent. The mediating role of serum α-Klotho in the association between serum lead (9.75% (6.52, 19), p < 0.001) and eGFR compared with serum mercury (6.10% (2.56, 29), p < 0.05) is more significant. In the subgroup analysis of age and gender, α-Klotho did not mediate in serum mercury. In serum lead, especially in men (11.56% (7.54, 31), p < 0.05) and middle-aged people (6.87% (4.14, 11), p < 0.05), α-Klotho mediates the relationship between serum lead and eGFR effect is more obvious. These findings provide insights into the oxidative stress pathways between serum lead, mercury, and improved renal function.

Previous research

The relationship between metals and kidney function has been extensively studied. A Swedish prospective cohort study (44) that included 4341 people aged 46–67 years with long-term follow-up found that blood lead levels were inversely associated with decreased renal function and positively associated with the incidence of CKD. Navas-Asien (45) used the NHANES database from 1996 to 2006 and confirmed that blood lead is an independent risk factor for eGFR decline and proteinuria. Results on blood mercury have been inconsistent in different studies. In an NHANES study (46), exposure to lead alone resulted in renal dysfunction in adults, whereas exposure to mercury alone did not affect renal function. Another study (47) included 110 miners in Ghana and found an association between serum mercury levels and reduced eGFR. In this study, we found that blood lead levels were negatively associated with renal function levels and positively associated with proteinuria after adjusting for covariates. These studies are consistent with previous research, demonstrating the harmful effects of blood lead levels on kidney function. However, the effects of serum mercury and renal function in this study were inconsistent. This study showed a slight positive correlation between serum mercury and renal function. This may be related to the fact that most of the participants in this study were ordinary participants with healthy kidney function, and the mercury exposure was much lower than that of mine workers with occupational exposure and people who have lived in environmental pollution for a long time. Overall, our findings highlight the importance of the effects of serum lead and mercury on renal function; however, the mechanisms underlying this association are not fully understood.

Underlying mechanisms

This study reveals a new mediating effect of serum α-Klotho on the relationship between serum lead, mercury, and renal function. In an NHANES cross-sectional study (37) among 9800 participants aged 40–79, blood lead levels were positively associated with serum α-Klotho in US adults. Among various metals, blood lead has been linked to blood homocysteine (48), an amino acid involved in oxidative stress and aging. Blood lead and blood mercury can affect kidney function through excessive production of ROS (49) and cause damage or apoptosis of renal tubular epithelial cells by destroying the balance of the oxidative stress system and heme oxygenase activity (9). Oxidative stress is considered an important mechanism of metal damage to renal function. Klotho has antioxidant effects. A study on mouse HELa and ovarian cells (50) showed that Klotho can induce the expression of superoxide dismutase and promote the removal of ROS. Participants with higher metal exposure showed higher Klotho methylation status, while participants with lower metal exposure showed lower Klotho methylation status, suggesting that metal exposure is related to Klotho methylation, thereby altering protein synthesis (51). Heavy metals are related to the production of ROS and DNA methylation, and serum Klotho levels are partially regulated by ROS and DNA methylation (51–53). Our research results show that serum lead and mercury can affect renal function through a new mediation pathway of serum α-Klotho, which will play a crucial role in preventing and treating renal function damage caused by metals in the future. However, our sensitivity analysis showed no evidence that blood α-Klotho mediates the association between UACR and serum lead and mercury. Therefore, the relationship between serum lead, mercury, and UACR cannot be explained by serum α-Klotho; further studies are needed to confirm the mechanisms behind these.

Strengths and Limitations

To our best knowledge, this is the first study to report the relationship between serum α-Klotho levels and renal function as mediated by blood lead and mercury levels in a representative sample of US adults. It contributes to a new understanding how serum lead and mercury mediate renal function through serum α-Klotho. In addition, this study had a large sample, and all data were collected through standardized interviews, physical examinations, and laboratory tests, minimizing potential sources of measurement bias. However, this study also has some limitations. First, this study observed that serum α-Klotho mediated the negative relationship between blood lead and eGFR, as well as UACR. However, unlike previous studies, blood mercury was negatively correlated with eGFR, and blood mercury did not correlate with UACR. It is necessary to Further studies on a large number of samples, and different populations will elucidate the relationship and mechanism between blood mercury and renal function. Second, we adjusted as much as possible for confounders, but some unobserved confounders may still exist. Third, this study is a cross-sectional study. We cannot confirm any chance or temporal relationships. Therefore, a longer longitudinal observational study is needed to fully understand the relationship between serum lead, mercury, and renal function and to understand the serum α-Potential biological role of Klotho.

This study observed the important role of serum α-Klotho in the mechanism of serum lead, mercury, and renal function, proved the importance of the effects of serum lead and mercury on renal function, and suggested that the oxidative stress system is involved in this relationship. Specifically, we observed the important role of serum α-Klotho in the relationship mechanism between metal lead, mercury, and kidney function. This may help provide reference value for the mechanism of prevention and treatment of renal damage and provide a new way to treat and prevent kidney disease. In the future, more randomized controlled trials or cohort studies are urgently needed to confirm this finding.

Ethics approval and consent to participate: Not applicable.

Consent for publication: Not applicable.

Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests: Not applicable.

Funding: Not applicable.

Authors' contributions: Conceptualization：Lin Jiang; Writing - original draft preparation：Lin Jiang and Tingting Guo; Writing - review and editing: Jun Zhang and Xin Zhong; Methodology: Tingting Guo; Resources: Wanyu Yang; Formal analysis and investigation: Tingting Guo and Yini Cai; Supervision: Jun Zhang.

Acknowledgements: Not applicable.

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Serum Protein α-Klotho Mediates the Association between Lead, Mercury, and Kidney Function

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Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

Study population

Determination of serum α-Klotho levels

Determination of blood lead and blood mercury levels

Renal function assessment

Covariates

Statistical analysis

Results

Characteristics of study participants

Relationship between serum α-Klotho, blood lead, blood mercury, and eGFR

Linear relationship between serum lead and eGFR and UACR

Mediating role of serum α-Klotho in serum lead and mercury

Gender and age subgroup analysis

Discussion

Key points of results

Previous research

Underlying mechanisms

Strengths and Limitations

Conclusion

Declarations

References

Additional Declarations

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