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The relationship of serum klotho levels and triglyceride glucose index-related indicators

Lipids in Health and Disease volume 23, Article number: 399 (2024) Cite this article

Abstract

Background

Klotho, an anti-aging protein, is linked to energy metabolism. There is limited research on the association of serum klotho and triglyceride glucose (TyG) index-related indicators. Our research aims to investigate the relationship of serum klotho with TyG-BMI (body mass index), TyG-WC (waist circumference), and TyG-WHtR (waist-to-height ratio).

Methods

From 2007 to 2016, we examined 6,370 participants in the National Health and Nutrition Examination Survey (NHANES). The enzyme-linked immunosorbent assay (ELISA) was utilized to measure serum klotho. We calculated the TyG-BMI, TyG-WC, and TyG-WHtR based on fasting triglycerides, fasting glucose, BMI, WC, and WHtR. Multiple linear regression analysis was used to evaluate the association of serum klotho with TyG-BMI, TyG-WC, and TyG-WHtR. Additionally, generalized additive model (GAM) and smoothing curves were used to evaluate the linear and nonlinear relationships. A piecewise regression model was also utilized to test for threshold effects and determine the breakpoints. Finally, the potential independent associations of serum klotho with TyG-BMI, TyG-WC, and TyG-WHtR were further explored using subgroup analysis.

Results

We observed a statistically significant difference in serum klotho levels across different quartiles of the population. Based on the multiple linear regression analysis, serum klotho levels were negatively associated with TyG-related indicators. There was a nonlinear relationship between the serum klotho and TyG-BMI, TyG-WC, and TyG-WHtR. The segmented regression analysis revealed that the breakpoints of TyG-BMI, TyG-WC, and TyG-WHtR were 5.42, 6.67, and 1.89, respectively. Subgroup analysis showed that TyG-related indicators interacted with gender and diabetes.

Conclusions

In this study, a negative and nonlinear relationship was identified between serum klotho and TyG-related indicators. Further research is needed to clarify the potential mechanisms that may link serum klotho to TyG-BMI, TyG-WC, and TyG-WHtR.

Introduction

Insulin resistance (IR) is a metabolic dysfunction characterized by the body’s reduced responsiveness to insulin, typically associated with metabolic disorders such as diabetes or obesity [1]. Aging is the gradual loss of function and adaptability in an organism, accompanied by decreased cellular function and organ degeneration [2]. Research indicates a close association between IR and aging. Prolonged IR increases oxidative stress and inflammatory responses, damaging cell structure and function, thus accelerating the aging process [3]. Additionally, IR affects the expression of genes related to aging, regulating cell apoptosis, proliferation and repair capacity, thereby influencing the overall pace of biological aging [4]. Conversely, aging can also lead to the onset and exacerbation of IR. As age advances, cells undergo changes that affect their sensitivity to insulin, which can disrupt insulin signaling pathways and contribute to the development of IR [5]. Furthermore, aging may induce changes in organismal energy metabolism, which can further exacerbate the degree of IR [6].

The klotho protein, encoded by the klotho gene, has been shown by recent research to play a significant role in modulating the aging process. Originally found in mice, the absence of the klotho gene in these animals can result in syndromes similar to those seen in human aging, including a shortened lifespan, infertility, skin atrophy, osteoporosis, etc [7]. In healthy individuals, serum klotho levels decrease with age, and this decline is further exacerbated in those with metabolic diseases [8], such as obesity [9], diabetes [1], hypertension [10], hyperlipidemia [11], and metabolic syndrome [12]. As research advances, klotho is gaining prominence for its crucial regulatory function in various physiological processes, including IR, glucose homeostasis, oxidative stress and inflammation, etc [13].

Based on the incorporation of triglycerides and fasting blood glucose (FBG), the triglyceride-glucose (TyG) index serves as a measure of IR and provides insight into an individual’s sensitivity to insulin [14]. Researchers have recently begun exploring the integration of the TyG index with other physiological indicators, such as WC, BMI, WHtR, or other simple, cost-effective, and non-invasive anthropometric measures. Furthermore, research has demonstrated a close correlation between metabolic diseases and IR. There is promising potential in integrating the TyG index with indicators (BMI, WC, WHtR) to enhance the predictive accuracy of IR or other metabolic disorders. For example, Yan et al. [15] proposed that TyG-BMI, TyG-WC, and TyG-WHtR could effectively screen for non-alcoholic fatty liver disease (NAFLD) and metabolic-associated fatty liver disease (MAFLD) at an early stage. Additionally, incorporating the HOMA-IR index allowed for metabolic risk assessment and disease monitoring for individuals with NAFLD. Wang et al. [16] identified that increase in TyG, TyG-BMI and METS-IR were associated with a higher risk of hyperuricemia (HU). Additionally, both TyG-BMI and METS-IR exhibited enhanced discriminatory capabilities for HU across both genders. Li et al. [17] demonstrated that TyG-BMI, TyG-WC, and TyG-WHtR were significant predictors of metabolic syndrome among middle-aged to elderly Chinese people.

At present, research on the potential association between serum klotho and TyG-related indicators is limited. Therefore, this study utilized the NHANES database to explore the association between serum klotho and the TyG-related indicators.

Materials and methods

Data source

The research utilized data from the National Health and Nutrition Examination Survey (NHANES), a cross-sectional study conducted by the National Center for Health Statistics (NCHS) and the Centers for Disease Control and Prevention (CDC) [18]. NHANES employed a complex stratified sampling methodology to ensure the representativeness of the sample. NHANES has been approved by the NCHS Ethics Review Board (ERB), with all participants furnishing written informed consent [19]. Data for this study was sourced from https://www.cdc.gov/nchs/nhanes/.

Study population

Our research incorporated data from five NHANES cycles (2007 to 2016), including demographics data, examination results, laboratory data, and questionnaire data. Overall, NHANES collected data from 50,588 qualified participants from 2007 to 2016. Based on the research criteria, the following participants were excluded: (1) absence of klotho data; (2) missing triglyceride and glucose data; and (3) lacking data on BMI, WC, and body height data. Ultimately, 6,370 participants were included for analysis. The screening flowchart is shown in Fig. 1.

Fig. 1
figure 1

Flowchart of participant selection. NHANES: National Health and Nutrition Examination Survey; BMI: body mass index; WC: waist circumference

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Exposure and outcome variable

In this study, the exposure variables included TyG-BMI, TyG-WC, and TyG-WHtR. The calculation procedure is outlined below: TyG = Ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2] [20]; BMI = body mass (kg)/height2 (m2); WHtR = WC (cm)/height (cm); TyG-BMI = TyG × BMI [21]; TyG-WC = TyG × WC [21]; TyG-WHtR = TyG × WHtR [22].

Serum klotho were the outcome variable. Blood samples were obtained from individuals aged over 40, preserved in dry ice at -80 °C, and then sent to the University of Washington’s Northwest Lipid Metabolism and Diabetes Research Laboratory for examination. Each sample was analyzed twice, and the mean value was used as the final result. More detailed testing methods for serum klotho can be obtained on the NHANES website.

Covariables

Covariables included demographic information, examination results, laboratory data, and questionnaire data. Demographics information included gender, age, race, educational level, marital status, and poverty income ratio (PIR). Examination data included body mass, WC, body height, and hypertension. Laboratory data included triglycerides, glycosylated hemoglobin, FBG, and serum klotho. Questionnaire data included information on smoking (the average number of cigarettes per day over the past 30 days) and alcohol intake (the average number of drinks per day over the past 12 months). The assessment of hypertension: in a resting state, mean systolic pressure ≥ 140 mmHg or mean diastolic pressure ≥ 90 mmHg on three consecutive measurements, or the use of antihypertensive medications. Diabetes is diagnosed with glycosylated hemoglobin (HbA1c) ≥ 6.5%, FBG ≥ 7.0 mmol/L (126 mg/dL) [23], or the use of hypoglycemic medications. Hyperlipidemia is diagnosed if the total cholesterol ≥ 200 mg/dL, triglyceride ≥ 150 mg/dL, low density lipoprotein ≥ 130 mg/dL, high density lipoprotein ≤ 40 mg/dL for males and ≤ 50 mg/dL for females [24], or the use of cholesterol-lowering medications.

Statistical analysis

In accordance with CDC guidelines, this study used a complex sampling design to analyze the relationship between serum klotho and TyG-BMI, TyG-WC, and TyG-WHtR. Initially, the study population was divided into four groups based on quartiles (Q1-Q4) of serum klotho. Rao-Scott χ2 and non-parametric tests was employed to assess the statistical differences in the four groups for categorical variables. The continuous variables were presented as mean ± standard error. The missing values were imputed by multiple imputation (e.g., smoking, alcohol intake) (Supplement material S1)) [25]. Considering the skewed distribution, the natural logarithm transformation of serum klotho (Inklotho), TyG-BMI (InTyG-BMI), TyG-WC (InTyG-WC), and TyG-WHtR (InTyG-WHtR) were performed when assessing the relationship between serum klotho and TyG-related indicators. Therefore, three types of weighted linear regression models were used to investigate the relationship of serum klotho with TyG-BMI, TyG-WC, and TyG-WHtR. Subsequently, we generated a smoothed curve using a generalized additive model. Furthermore, a segmented linear regression model was used to assess the potential nonlinear relationship between serum klotho and TyG-related indicators, determine the threshold effects, and calculate the inflection points. Finally, we conducted subgroup analysis to explore the potential factors affecting the association between serum klotho and TyG-related indicators. The statistical analysis was conducted using R software (version 4.3.1) and SPSS 27.0. A P-value less than 0.05 was considered statistically significant.

Results

Participant baseline characteristics

Our research included a sample of 6,370 individuals. We observed that gender, age, race, educational level, marriage status, PIR, smoking, alcohol intake, hypertension, diabetes, hyperlipidemia, BMI, WC, and height were statistically significant in serum klotho at different quartiles. With the increase in serum klotho concentration, BMI, WC, height, TyG-BMI, TyG-WC, and TyG-WHtR showed a decreasing trend (Table 1).

Table 1 Baseline characteristics of participants in the NHANES from 2007–2016
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Multivariable regression analysis

The association between serum klotho and TyG-related indicators was examined using a weighted multivariable regression model (Table 2). A significant negative association was found between serum klotho and TyG-BMI, TyG-WC, and TyG-WHtR. In model 1, serum lnklotho decreased by 0.063 pg/ml, 0.137 pg/ml, and 0.115 pg/ml with each one-unit increase in lnTyG-BMI, lnTyG-WC, and lnTyG-WHtR, respectively. In model 2, after adjustments for gender, age, race, educational level, marital status, PIR, hypertension, diabetes, and hyperlipidemia, serum lnklotho decreased by 0.065 pg/ml, 0.117 pg/ml, and 0.127 pg/ml in lnTyG-BMI, lnTyG-WC, and lnTyG-WHtR, respectively. In model 3, with all variables adjusted, serum lnklotho decreased by 0.064 pg/ml, 0.116 pg/ml, and 0.126 pg/ml in lnTyG-BMI, lnTyG-WC, and lnTyG-WHtR, respectively.

Table 2 Association between serum klotho and TyG-BMI, TyG-WC, TyG-WHtR
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lnTyG-BMI, lnTyG-WC, and lnTyG-WHtR were categorized into four groups (Q1 to Q4) based on their quartile, with the first quartile group (Q1) serving as the reference category (Table 2). We observed a nonlinear relationship between serum klotho and InTyG-BMI, InTyG-WC, and InTyG-WHtR, characterized by an initial decrease followed by an increase.

The dose-response association of serum Inklotho levels with InTyG-BMI, InTyG-WC, and InTyG-WHtR

Generalized additive models are very sensitive in identifying both linear and nonlinear relationships. We generated three smooth curves to illustrate the potential relationships between serum Inklotho and InTyG-BMI, InTyG-WC, and InTyG-WHtR, based on model 3 (Fig. 2). In addition, we used a piecewise regression model to calculate the breakpoints of InTyG-BMI, InTyG-WC, and InTyG-WHtR, which were 5.42, 6.67, and 1.89, respectively (Table 3). The results showed that the piecewise regression model is more suitable for clarifying the relationship between serum Inklotho and InTyG-WC, and InTyG-WHtR.

Fig. 2
figure 2

The smooth curve fitting for the associations between serum klotho and TyG-related indicators: (A) the relationship between serum Inklotho and InTyG-BMI; (B) the relationship between serum Inklotho and InTyG-WC; (C) the relationship between serum Inklotho and InTyG-WHtR

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Table 3 Threshold effect analysis of the association between serum Inklotho and InTyG-BMI, InTyG-WC, TyG-WHtR in NHANES 2007–2016
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Subgroup analysis

Subgroup analyses were conducted based on gender, age, diabetes, hypertension, and hyperlipidemia (Table 4). The analysis results showed that the association between serum klotho and TyG-related indicators was influenced by gender and diabetes (P for interaction < 0.05).

Table 4 Subgroup analysis for the relationship of serum Inklotho with InTyG-BMI, InTyG-WC, TyG-WHtR
Full size table

Discussion

Population aging poses a major public health challenge worldwide at present. The aging population often experiences declines in organ function and metabolic disorders. The potential link between the anti-aging protein klotho and metabolic diseases is also gaining attention [12]. Therefore, we used NHANES samples from 2007 to 2016 to explore the relationship between serum klotho and TyG-related indicators. After analyzing data from 6,370 participants, the key findings were as follows: (1) There were significant differences in levels of serum klotho according to the quartiles. (2) It showed a significant negative nonlinear relationship between the serum klotho and TyG-BMI, TyG-WC and TyG-WHtR. (3) The relationship between serum klotho and the TyG-related indicators was influenced by gender and diabetes.

The negative association between klotho and TyG-related indicators may suggest a potential role for klotho in regulating energy metabolism and slowing down the aging process. The klotho family comprises three subtypes: α, β, and γ. In addition, klotho can also be categorized into membrane-bound (mKL), secretory or soluble (sKL), and intracellular forms, with different forms of klotho involved in different physiological processes [26]. Studies have shown that the serum klotho levels in patients with diabetes are significantly lower than those in non-diabetic individuals [27]. Deficiency of soluble klotho in circulating blood leads to increased IR, diabetic phenotype, and hepatic lipid accumulation in T2D. Soluble klotho can target the IGF1R/PI3K/AKT/mTORC1 signaling pathway and up-regulate peroxisome proliferator-activated receptor α (PPARα) in T2D, thereby improving insulin sensitivity, hepatic glucose production, and lipid homeostasis [28]. The kidney is the main expression site for klotho, which affects glucose metabolism by influencing glucose reabsorption and filtration within the kidney [29]. Furthermore, klotho can alleviate podocyte damage in diabetic kidney disease (DKD) by modulating the IGF-1R/RAC1/OLR1 signaling pathway, thereby improving high-glucose-induced glomerular deposition of oxidized low-density lipoprotein (ox-LDL) [30]. Therefore, klotho has potential in the prevention and treatment of diabetes and its complications.

Elevated plasma lipoprotein levels and decreased lipid clearance lead to lipid accumulation in the body. Klotho can increase the body’s energy expenditure, reduce fat mass, and play a beneficial role in regulating lipid accumulation in the liver and adipose tissue. For example, a-klotho can improve obesity status in DIO mice by reducing lipid accumulation in the liver and adipose tissue [31]. Recombinant klotho can reduce lipid accumulation in foam cells and promote increased cholesterol efflux by inhibiting the Wnt/β-catenin pathway [32]. Furthermore, a-klotho promotes lipid oxidation and reduces adipogenesis by regulating gene expression in the liver and white adipose tissue (WAT) of DIO mice [26]. Soluble klotho can alleviate hepatic steatosis by inhibiting genes associated with lipid synthesis and uptake while promoting genes involved in lipid oxidation [28]. Klotho has a protective effect against high-fat diet (HFD) induced hepatic steatosis, reduces the weight of WAT and liver, and enhances the expression of thermogenic genes in brown adipose tissue (BAT) [33].

Although our study primarily investigated the association between serum klotho and TyG-related indicators, it offers novel insights into the prevention and treatment of metabolic diseases from the perspective of aging. Studies have shown that the level of s-klotho in the plasma of diabetic NOD mice is significantly lower than that in the non-diabetic mice [34]. A meta-analysis has demonstrated that individuals with chronic kidney disease (CKD) exhibiting reduced serum klotho levels are at an elevated risk of all-cause mortality [35]. Furthermore, polymorphisms G395A and C1818T in the klotho gene have been correlated with an elevated risk of developing T2D [36]. Klotho exerts a protective effect against diabetic retinopathy by attenuating the senescence of retinal macrophages, which is achieved by downregulating HECTD1 and diminishing IRS1 ubiquitination, and subsequent proteasomal degradation [37]. Numerous studies have also confirmed the potential mechanisms of α-klotho in alleviating diabetes and its complications [36], NAFLD [38], obesity [39], and other metabolic diseases. Therefore, serum klotho can not only serve as a potential biomarker for metabolic diseases but also as a therapeutic target.

In the subgroup analysis, we observed the impact of gender and diabetes on the relationship between serum klotho and TyG-related indicators, particularly among female and non-diabetic patients. Our study results are consistent with previous research findings [40, 41]. Gender differences may be related to variations in hormone levels, fat distribution, and differences in insulin sensitivity. Women in menopause experience a decrease in estrogen levels, which can reduce sensitivity to insulin [42]. Our study subjects are primarily over the age of 40. Furthermore, the reduction in estrogen levels can also lead to the accumulation of fat in the visceral area, thereby increasing metabolic risk [43]. Therefore, the gender differences observed in our study may be due to the influence of hormonal levels on serum klotho expression. Moreover, relevant research has also confirmed that estrogen can upregulate the expression of serum klotho [44]. Qiu S [41] investigated the relationship between klotho and TyG index in populations with and without diabetes, and the results indicated that klotho is positively associated with the TyG index in diabetic populations compared to those without diabetes. This result is consistent with our study findings. Wang C [40] speculated that as the level of IR increases, there may be a saturation effect in the association between klotho and TyG. In summary, the biological mechanisms underlying the influence of the relationship between serum klotho and TyG-related indicators in the non-diabetic female population still require further research for elucidation.

Our research has some advantages. Firstly, it represents the preliminary attempt to explore the association between serum klotho and indicators such as TyG-BMI, TyG-WC, and TyG-WHtR. This investigation may provide new insights into the prevention and treatment of both age-related conditions and metabolic disorders. Secondly, our study benefits from the weighted analysis of a population sample drawn from NHANES, enhancing the representativeness of the results. However, this study also has limitations. Firstly, this study is limited by its cross-sectional design, which hinders the ability to determine causality between klotho and TyG-BMI, TyG-WC, and TyG-WHtR. Secondly, our study included only the common complications of non-diabetic, hypertension, and hyperlipidemia, without incorporating other related chronic diseases. Future research should encompass a broader range of chronic conditions to fully understand the impact of different health status on the study outcomes. Finally, despite adjusting for potential confounding variables, there remains a possibility of unidentified factors that could impact the relationship between serum klotho and the TyG-BMI, TyG-WC, and TyG-WHtR.

Conclusion

Based on the analysis of nationally representative samples, our research revealed a negative nonlinear relationship between serum klotho and TyG-BMI, TyG-WC, and TyG-WHtR in U.S. adults. The findings indicate that the relationship between serum klotho and TyG-related indicators could offer a new perspective for anti-aging and prevention of metabolic diseases.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

TyG:

Triglyceride glucose index

NHANES:

National Health and Nutrition Examination Survey

BMI:

Body mass index

WC:

Waist circumference

WHtR:

Waist-to-height ratio

ELISA:

Enzyme-linked immunosorbent assay

GAM:

Generalized additive model

IR:

Insulin resistance

FBG:

Fasting blood glucose

NAFLD:

Non-alcoholic fatty liver disease

MAFLD:

Metabolic-associated fatty liver disease

NCHS:

National Center for Health Statistics

CDC:

Centers for Disease Control and Prevention

PIR:

Poverty income ratio

HbA1c:

Glycosylated hemoglobin

mKL:

Membrane-bound

sKL:

Secretory or soluble

PPARα:

Peroxisome proliferator-activated receptor α

DKD:

Diabetic kidney disease

ox-LDL:

Oxidized low-density lipoprotein

WAT:

White adipose tissue

HFD:

High fat diet

BAT:

Brown adipose tissue

CKD:

Chronic kidney disease

References

  1. Wang L, Wang B, Gasek NS, Zhou Y, Cohn RL, Martin DE, Zuo W, Flynn WF, Guo C, Jellison ER, et al. Targeting p21(Cip1) highly expressing cells in adipose tissue alleviates insulin resistance in obesity. Cell Metab. 2022;34:75–e8978. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.cmet.2021.11.002.

    Article  CAS  PubMed  Google Scholar 

  2. Gorgoulis V, Adams PD, Alimonti A, Bennett DC, Bischof O, Bishop C, Campisi J, Collado M, Evangelou K, Ferbeyre G, et al. Cellular Senescence: defining a path Forward. Cell. 2019;179:813–27. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.cell.2019.10.005.

    Article  CAS  PubMed  Google Scholar 

  3. Wang K, Mao Y, Lu M, Liu X, Sun Y, Li Z, Li Y, Ding Y, Zhang J, Hong J, Xu D. Association between serum klotho levels and the prevalence of diabetes among adults in the United States. Front Endocrinol (Lausanne). 2022;13:1005553. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fendo.2022.1005553.

    Article  PubMed  Google Scholar 

  4. Tatar M, Bartke A, Antebi A. The endocrine regulation of aging by insulin-like signals. Science. 2003;299:1346–51. https://doiorg.publicaciones.saludcastillayleon.es/10.1126/science.1081447.

    Article  CAS  PubMed  Google Scholar 

  5. Huang LY, Liu CH, Chen FY, Kuo CH, Pitrone P, Liu JS. Aging affects insulin resistance, insulin secretion, and glucose effectiveness in subjects with normal blood glucose and body weight. Diagnostics (Basel). 2023;13. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/diagnostics13132158.

  6. Palmer AK, Jensen MD. Metabolic changes in aging humans: current evidence and therapeutic strategies. J Clin Invest. 2022;132. https://doiorg.publicaciones.saludcastillayleon.es/10.1172/jci158451.

  7. Kuro-o M, Matsumura Y, Aizawa H, Kawaguchi H, Suga T, Utsugi T, Ohyama Y, Kurabayashi M, Kaname T, Kume E, et al. Mutation of the mouse klotho gene leads to a syndrome resembling ageing. Nature. 1997;390:45–51. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/36285.

    Article  CAS  PubMed  Google Scholar 

  8. Abraham CR, Li A. Aging-suppressor Klotho: prospects in diagnostics and therapeutics. Ageing Res Rev. 2022;82:101766. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.arr.2022.101766.

    Article  CAS  PubMed  Google Scholar 

  9. Landry T, Li P, Shookster D, Jiang Z, Li H, Laing BT, Bunner W, Langton T, Tong Q, Huang H. Centrally circulating α-klotho inversely correlates with human obesity and modulates arcuate cell populations in mice. Mol Metab 2021, 44:101136. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.molmet.2020.101136

  10. Kanbay M, Demiray A, Afsar B, Covic A, Tapoi L, Ureche C, Ortiz A. Role of Klotho in the development of essential hypertension. Hypertension. 2021;77:740–50. https://doiorg.publicaciones.saludcastillayleon.es/10.1161/hypertensionaha.120.16635.

    Article  CAS  PubMed  Google Scholar 

  11. Yan S, Luo W, Lei L, Zhang Q, Xiu J. Association between serum Klotho concentration and hyperlipidemia in adults: a cross-sectional study from NHANES 2007–2016. Front Endocrinol (Lausanne). 2023;14:1280873. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fendo.2023.1280873.

    Article  PubMed  Google Scholar 

  12. Orces CH. The association between metabolic syndrome and the anti-aging humoral factor klotho in middle-aged and older adults. Diabetes Metab Syndr. 2022;16:102522. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.dsx.2022.102522.

    Article  CAS  PubMed  Google Scholar 

  13. Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier P, Rinfret S, Schiffrin EL, Eisenberg MJ. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol. 2010;56:1113–32. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jacc.2010.05.034.

    Article  PubMed  Google Scholar 

  14. Lee J, Kim B, Kim W, Ahn C, Choi HY, Kim JG, Kim J, Shin H, Kang JG, Moon S. Lipid indices as simple and clinically useful surrogate markers for insulin resistance in the U.S. population. Sci Rep. 2021;11:2366. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41598-021-82053-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Xue Y, Xu J, Li M, Gao Y. Potential screening indicators for early diagnosis of NAFLD/MAFLD and liver fibrosis: triglyceride glucose index-related parameters. Front Endocrinol (Lausanne). 2022;13:951689. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fendo.2022.951689.

    Article  PubMed  Google Scholar 

  16. Wang H, Zhang J, Pu Y, Qin S, Liu H, Tian Y, Tang Z. Comparison of different insulin resistance surrogates to predict hyperuricemia among U.S. non-diabetic adults. Front Endocrinol (Lausanne). 2022;13:1028167. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fendo.2022.1028167.

    Article  PubMed  Google Scholar 

  17. Li Y, Gui J, Liu H, Guo LL, Li J, Lei Y, Li X, Sun L, Yang L, Yuan T, et al. Predicting metabolic syndrome by obesity- and lipid-related indices in mid-aged and elderly Chinese: a population-based cross-sectional study. Front Endocrinol (Lausanne). 2023;14:1201132. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fendo.2023.1201132.

    Article  PubMed  Google Scholar 

  18. Ahluwalia N, Dwyer J, Terry A, Moshfegh A, Johnson C. Update on NHANES Dietary Data: Focus on Collection, Release, Analytical considerations, and uses to inform Public Policy. Adv Nutr. 2016;7:121–34. https://doiorg.publicaciones.saludcastillayleon.es/10.3945/an.115.009258.

    Article  PubMed  PubMed Central  Google Scholar 

  19. National Center for Health Statistics. Centers for Disease Control and Prevention NCHS research ethics review board (ERB) approval. https://www.cdc.gov/nchs/nhanes/irba98.htm. Accessed 23 September 2024.

  20. Darshan An V, Rajput R, Meena, Mohini, Garg R, Saini S. Comparison of triglyceride glucose index and HbA1C as a marker of prediabetes - a preliminary study. Diabetes Metab Syndr 2022, 16:102605. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.dsx.2022.102605

  21. Er LK, Wu S, Chou HH, Hsu LA, Teng MS, Sun YC, Ko YL. Triglyceride glucose-body Mass Index is a simple and clinically useful surrogate marker for insulin resistance in nondiabetic individuals. PLoS ONE. 2016;11:e0149731. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pone.0149731.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Dang K, Wang X, Hu J, Zhang Y, Cheng L, Qi X, Liu L, Ming Z, Tao X, Li Y. The association between triglyceride-glucose index and its combination with obesity indicators and cardiovascular disease: NHANES 2003–2018. Cardiovasc Diabetol. 2024;23:8. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12933-023-02115-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Gao HX, Regier EE, Close KL. Prevalence of and trends in diabetes among adults in the United States, 1988–2012. J Diabetes. 2016;8:8–9.

    PubMed  Google Scholar 

  24. Third Report of the National Cholesterol Education Program (NCEP). Expert Panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report. Circulation. 2002;106:3143–421.

    Article  Google Scholar 

  25. Barnard J, Meng XL. Applications of multiple imputation in medical studies: from AIDS to NHANES. Stat Methods Med Res. 1999;8(1):17–36. https://doiorg.publicaciones.saludcastillayleon.es/10.1177/096228029900800103.

    Article  CAS  PubMed  Google Scholar 

  26. Landry T, Shookster D, Huang H. Circulating α-klotho regulates metabolism via distinct central and peripheral mechanisms. Metabolism. 2021;121:154819. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.metabol.2021.154819.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Xin C, Sun X, Li Z, Gao T. Relationship of Soluble Klotho and Early Stage of Diabetic Nephropathy: a systematic review and Meta-analysis. Front Endocrinol (Lausanne). 2022;13:902765. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fendo.2022.902765.

    Article  PubMed  Google Scholar 

  28. Gu H, Jiang W, You N, Huang X, Li Y, Peng X, Dong R, Wang Z, Zhu Y, Wu K, et al. Soluble Klotho improves hepatic glucose and lipid homeostasis in type 2 diabetes. Mol Ther Methods Clin Dev. 2020;18:811–23. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.omtm.2020.08.002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Aziz MS, Aamir AU, Khan A, Khan Z, Shah SQ, Safi SZ, Batumalaie K, Alobaid HM, Ali A, Imran M. Investigation of Klotho G395A and C1818T polymorphisms and their association with serum glucose level and risk of type 2 diabetes Mellitus. Genes (Basel). 2022, 13. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/genes13091532

  30. Jiang W, Gan C, Zhou X, Yang Q, Chen D, Xiao H, Dai L, Chen Y, Wang M, Yang H, Li Q. Klotho inhibits renal ox-LDL deposition via IGF-1R/RAC1/OLR1 signaling to ameliorate podocyte injury in diabetic kidney disease. Cardiovasc Diabetol. 2023;22:293. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12933-023-02025-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Rao Z, Landry T, Li P, Bunner W, Laing BT, Yuan Y, Huang H. Administration of alpha klotho reduces liver and adipose lipid accumulation in obese mice. Heliyon 2019, 5:e01494. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.heliyon.2019.e01494

  32. Liu W, Chen X, Wu M, Li L, Liu J, Shi J, Hong T. Recombinant klotho protein enhances cholesterol efflux of THP-1 macrophage-derived foam cells via suppressing Wnt/β-catenin signaling pathway. BMC Cardiovasc Disord. 2020;20:120. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12872-020-01400-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Park MJ, Lee J, Bagon BB, Matienzo ME, Lee CM, Kim K, Kim DI. Therapeutic potential of AAV-FL-Klotho in obesity: impact on weight loss and lipid metabolism in mice. Biochim Biophys Acta Mol Basis Dis. 2024;1870:167055. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.bbadis.2024.167055.

    Article  CAS  PubMed  Google Scholar 

  34. Prud’homme GJ, Glinka Y, Kurt M, Liu W, Wang Q. Systemic klotho therapy protects against insulitis and enhances beta-cell mass in NOD mice. Biochem Biophys Res Commun Biochem Biophys Res Commun 2020, 525:693–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.bbrc.2020.02.123

  35. Charoenngam N, Ponvilawan B, Ungprasert P. Lower circulating soluble Klotho level is associated with increased risk of all-cause mortality in chronic kidney disease patients: a systematic review and meta-analysis. Int Urol Nephrol. 2020;52:1543–50. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11255-020-02510-1.

    Article  CAS  PubMed  Google Scholar 

  36. Aziz MS, Aamir AU, Khan A, Khan Z, Shah SQ, Safi SZ, Batumalaie K, Alobaid HM, Ali A, Imran M. Investigation of Klotho G395A and C1818T polymorphisms and their association with serum glucose level and risk of type 2 diabetes Mellitus. Genes (Basel). 2022, 13:1532. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/genes13091532

  37. Li QB, Wang PY, Gong Y, Xu MH, Wang MQ, Luan R, Liu JP, Li XR, Shao Y. α-Klotho prevents diabetic retinopathy by reversing the senescence of macrophages. Cell Commun Signal. 2024;22:449. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12964-024-01838-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Liu WY, Zhang X, Li G, Tang LJ, Zhu PW, Rios RS, Zheng KI, Ma HL, Wang XD, Pan Q, de Knegt RJ, Valenti L, Ghanbari M, Zheng MH. Protective association of Klotho rs495392 gene polymorphism against hepatic steatosis in non-alcoholic fatty liver disease patients. Clin Mol Hepatol. 2022;28:183–95. https://doiorg.publicaciones.saludcastillayleon.es/10.3350/cmh.2021.0301.

    Article  PubMed  Google Scholar 

  39. Liu Y, Chen M. Emerging role of α-Klotho in energy metabolism and cardiometabolic diseases. Diabetes Metab Syndr. 2023;17:102854. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.dsx.2023.102854.

    Article  CAS  PubMed  Google Scholar 

  40. Wang C, Liu D, Lu J, Huang B, Feng B, Yin J, Qiu J, Zhang Z. Gender differences in the relationship between the triglyceride-glucose index and serum Klotho concentrations among the middle-aged and elderly: a cross-sectional analysis. BMC Endocr Disord. 2024;24:185. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12902-024-01726-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Qiu S, Li C, Zhu J, Guo Z. Associations between the TyG index and the ɑ-Klotho protein in middle-aged and older population relevant to diabetes mellitus in NHANES 2007–2016. Lipids Health Dis. 2024;23:188. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-024-02172-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Tiano JP, Mauvais-Jarvis F. Importance of oestrogen receptors to preserve functional β-cell mass in diabetes. Nat Rev Endocrinol. 2012;8:342–51. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/nrendo.2011.242.

    Article  CAS  PubMed  Google Scholar 

  43. Hetemäki N, Savolainen-Peltonen H, Tikkanen MJ, Wang F, Paatela H, Hämäläinen E, Turpeinen U, Haanpää M, Vihma V, Mikkola TS. Estrogen Metabolism in Abdominal Subcutaneous and visceral adipose tissue in Postmenopausal Women. J Clin Endocrinol Metab. 2017;102:4588–95. https://doiorg.publicaciones.saludcastillayleon.es/10.1210/jc.2017-01474.

    Article  PubMed  Google Scholar 

  44. Tan Z, Li Y, Guan Y, Iqbal J, Wang C, Yan R, Ma XM. Klotho regulated by Estrogen Plays a key role in sex differences in stress resilience in rats. Int J Mol Sci. 2023;24:1206. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/ijms24021206.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Funding

This project was supported by Special Project of State Key Laboratory of Dampness Syndrome of Traditional Chinese Medicine jointly established by the Ministry and the Province (No. SZ2022KF05), Beijing University of Traditional Chinese Medicine Campus level Project (No. 90010961020140), High level Key Discipline of National Administration of Traditional Chinese Medicine-Traditional Chinese constitutional medicine (No. zyyzdxk-2023251), The Joint Fund focuses on project U22A20365 (No.90030271420007), and Shenzhen Longgang District Science and Technology Development Special Fund (LGWJ2021-142).

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Author notes

  1. Yaoyao Zhou and Yaqi Wang share first authorship.

Authors and Affiliations

  1. College of Basic Medical Sciences, Zhejiang Chinese Medical University, Zhejiang, 310053, China

    Yaoyao Zhou, Yaqi Wang & Taotao Wu

  2. National Institute of Traditional Chinese Medicine Constitution and Preventive Treatment of Diseases, Beijing University of Chinese Medicine, Beijing, 100029, China

    Yaoyao Zhou, Yaqi Wang, Taotao Wu & Yingshuai Li

  3. Department of Non-Disease treatment, Shenzhen Hospital, Beijing University of Traditional Chinese Medicine, Beijing, Guangdong, 518172, China

    Fangli Li

  4. School of Acupuncture-Moxibustion and Tuina, Henan University of Chinese Medicine, Henan, 450046, China

    Yiming Shi

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Contributions

YY: Writing – original draft, Conceptualization, Visualization, Formal Analysis, Methodology. YQ: Writing – original draft, Formal Analysis, Software, Methodology. FL: Formal Analysis, Software, Methodology. YM: Writing – review & editing, Formal Analysis, Software, Methodology. TT: Writing – review & editing, Formal Analysis, Software, Methodology. YS: Writing – review & editing, Supervision, Project administration, Funding acquisition, Conceptualization.

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Correspondence to Yingshuai Li.

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Zhou, Y., Wang, Y., Li, F. et al. The relationship of serum klotho levels and triglyceride glucose index-related indicators. Lipids Health Dis 23, 399 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-024-02379-4

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Keywords

Lipids in Health and Disease

ISSN: 1476-511X