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The association between the triglyceride-glucose index and serum anti-aging protein α- Klotho: a population-based study

Diabetology & Metabolic Syndrome volume 16, Article number: 259 (2024) Cite this article

Abstract

Background

Both anti-aging protein α-Klotho and the triglyceride-glucose (TyG) index hold predictive value for the incidence, progression, and outcomes of cardiovascular disease, diabetes and many other diseases. However, their relationship remains unclear.

Methods

We conducted a cross-sectional analysis using data from the National Health and Nutrition Examination Survey (NHANES) 2007–2016. Weighted multivariate linear regression models and subgroup analysis were constructed to assess the association between TyG index and α-Klotho levels. Nonlinear correlations were explored using restricted cubic splines (RCS), generalized additive models (GAM) and smooth curve fitting. Segmented regression model was conducted to explore potential threshold effects and identify the inflection point.

Results

A total of 2568 participants satisfied the predetermined criteria were enrolled in the final analysis. After fully adjusting for covariates, TyG index was shown to be markedly negatively correlated with α-Klotho [β=-74.07, 95%CI (-100.29,-47.85), p < 0.001]. Gender was significantly correlated with this negative connection according to subgroup analysis and interaction testing (p for interaction < 0.05).Additionally, we discovered a linear association between TyG index and α-Klotho in all participants (p for nonlinear = 0.761), while non-linear association in female (p for nonlinear = 0.016).The analysis of threshold effect in the female participants found that the inflection point of TyG index was 8.01, exceed which the level of α-Klotho decreased significantly with increasing TyG index[β=-151.72, 95%CI (-201.93, -101.50), p < 0.001].

Conclusion

Our findings demonstrate a negative association between TyG index and α-Klotho levels, with the effect being more pronounced in females. TyG index may serve as an early indicator of individuals with low α-Klotho levels, especially among females. These findings highlight the need for gender-specific considerations in clinical interventions to improve public health. Further research is needed to clarify the causal direction of this association.

Introduction

Cardiometabolic disease (CMD) constitutes a diverse array of chronic non-communicable diseases intricately linked to metabolic and cardiovascular health, with significant associations to conditions including diabetes, metabolic syndrome, insulin resistance (IR), obesity, and dyslipidemia [1]. Notwithstanding the advancements made in prevention and treatment strategies, CMD continues to represent a substantial public health challenge. Factors including inflammation, oxidative stress, and metabolic disturbances are pivotal in the onset and progression of CMD, analogous to the processes of aging [2, 3]. Epidemiological reports reveal a progressive increase in the prevalence of cardiovascular diseases among Americans as age advances, escalating from approximately 5.5% in individuals under 45 years to nearly 41% in those aged 65 years and older. Thus, cardiovascular disease can be fundamentally regarded as an aging-related condition [4]. The identification of biomarkers associated with aging, coupled with the implementation of corresponding interventions, may play a crucial role in enhancing our understanding of and potentially treating CMD.

Among the diverse array of novel biomarkers, α-Klotho has emerged as a prominent anti-aging protein, bearing substantial implications for metabolic health [5]. Genetic deficiency in Klotho expression in mice leads to a variety of aging-like phenotypes, such as shortened lifespan, atherosclerosis, and multi-organ failure, whereas overexpression is associated with lifespan extension [6, 7]. Consequently, it was named after the Greek goddess who presided over human destiny. The Klotho family consists of three transmembrane protein subtypes: α-Klotho, β-Klotho, and γ-Klotho, each exhibiting distinct biological functions and patterns of tissue expression [8]. In the absence of specification regarding the subtype, “Klotho” typically refers to α-Klotho. Research suggests that α-Klotho functions as a co-receptor for fibroblast growth factor-23 (FGF23), thereby regulating calcium-phosphate metabolism, exerting anti-aging effects, and providing protection to the cardiovascular system [9,10,11]. Soluble forms of Klotho (S-Klotho) are also present, arising from the shedding of the extracellular domain via the proteolytic activities of disintegrin and metalloproteinases 10 and 17, or through selective splicing of the Klotho gene [12]. S-Klotho is predominantly present in bodily secretions, including blood and urine, thereby offering potential avenues for detection [12]. S-Klotho is associated with the enhancement of insulin sensitivity, glucose homeostasis, and lipid metabolism, which have significant implications for metabolic diseases [9, 12,13,14]. Low serum α-Klotho levels are identified as independent risk factors for all-cause mortality and cardiovascular mortality in patients with cardiovascular disease, diabetes and metabolic syndrome [15, 16].

IR represents a significant characteristic of metabolic syndrome [17], closely linked to both microvascular and macrovascular complications [18]. The triglyceride-glucose (TyG) index, which is derived from fasting triglycerides (TG) and fasting blood glucose (FBG), has been validated as a biochemical surrogate marker for IR in both diabetic and non-diabetic individuals [19]. It presents several advantages, including accessibility, cost-effectiveness, and independence from insulin dependency. Elevated TyG index levels are predictive of the occurrence of cardiovascular risk factors, including diabetes, prediabetes, and hypertension [20], and are associated with heightened risks of all-cause and cardiovascular mortality across diverse populations [21]. Shorter leukocyte telomere length, a robust marker of biological aging, is associated with elevated TyG index values [22].

Despite the individual significance of α-Klotho and the TyG index in the context of CMD, as well as the established link between metabolism and aging, the relationship between these two biomarkers remains insufficiently explored. In this study, utilizing a nationwide representative large-scale database, we examined the association between serum α-Klotho levels and the TyG index.

Methods

Study population

The National Health and Nutrition Examination Survey (NHANES) is a continuing initiative employing a complex multi-stage sampling design to evaluate the health and nutritional status of both adults and children in the United States. The survey incorporates both interviews and physical examinations, having received approval from the Institutional Review Board of the National Center for Health Statistics (NCHS), as well as informed consent from all participants. Additional detailed information regarding NHANES is available on their official website. In consideration of data availability and to maximize the sample size, we extracted data from NHANES for the years 2007 to 2016. Participants with unknown or missing data were excluded from the analysis; ultimately, a total of 2,568 participants were included in the study. (Fig. 1)

Fig. 1
figure 1

Flow chart of participants selection from the National Health and Nutrition Examination Survey (NHANES) 2007–2016

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Calculation of TyG index

As the exposure variable, the TyG index was calculated using the following formula: TyG index = Ln [TG (mg/dL) × FBG (mg/dL) / 2]. Fasting blood samples were stored at -20 °C and subsequently transported to the laboratory for analysis. FBG and TG were enzymatically measured utilizing the Roche Cobas 6000 and Roche Modular P chemistry analyzers, respectively.

Measurement of serum α-Klotho concentrations

According to NHANES, serum specimens from participants aged 40 to 79 were collected and stored at -80 °C at the Centers for Disease Control and Prevention. Between 2019 and 2020, samples were received by the Northwest Lipid Metabolism and Diabetes Research Laboratories, Division of Metabolism, Endocrinology, and Nutrition at the University of Washington, where individual α-Klotho concentrations were quantified using commercially available ELISA kits (IBL International, Gunma, Japan). All sample analyses were performed in duplicate in accordance with the manufacturer’s protocols, and all results were subjected to scrutiny to ensure compliance with the laboratory’s standards of acceptability prior to report dissemination.

Covariates

Data pertaining to demographics, medical conditions, and laboratory tests of participants were collected. Demographic data encompassed age, gender, ethnicity, marital status, education level, and the income-to-poverty ratio (PIR). Medical condition information included hypertension, diabetes, CHF, coronary heart disease (CHD), stroke, smoking status, and body mass index (BMI). Laboratory blood test data comprised albumin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), total bilirubin, blood urea nitrogen (BUN), creatinine, uric acid, total calcium, phosphorus, sodium, potassium, chloride, FBG, total cholesterol (TC), TG, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Both demographic and medical condition information were acquired through interviews. BMI was calculated as weight (kg) divided by the square of height (m). Hypertension was defined as a self-reported diagnosis or the use of oral antihypertensive medications. Diabetes was defined as a self-reported diagnosis or the use of insulin and oral diabetic agents. Participants who self-reported a history of CHD, angina pectoris, or myocardial infarction (MI) were classified as having CHD.

Statistical analysis

In accordance with the analytical protocols established by NHANES, all analyses were conducted utilizing appropriate sample weights. We utilized one-fifth of the WTSAF2YR as a decade sample weight for analytical purposes. Baseline characteristics of the study population were stratified according to TyG index quartiles. Continuous variables were expressed as mean ± standard deviation (SD) and compared using weighted one-way analysis of variance (ANOVA), whereas categorical variables were presented as percentages and compared using the weighted chi-square test. The correlation between the TyG index and α-Klotho was evaluated using weighted multivariate linear regression analysis across four distinct models. The selection of covariates was guided by both related literature [23] and statistical considerations. Laboratory variables exhibiting variance inflation factors (VIF) of less than 5 and an effect size greater than 10% on the independent variable were included in the regression model. Model 1 was not adjusted for covariates. Model 2 adjusted for age, gender, and ethnicity. Model 3 further adjusted for BMI, marital status, PIR, education level, smoking status, hypertension, diabetes, stroke, CHF and CHD. Model 4 adjusted for Model 3 plus uric acid, FBG, HDL-C and GGT. The association between the TyG index and α-Klotho was assessed in the models using coefficients (β) and 95% confidence intervals (CI). A sensitivity analysis was conducted by converting the TyG index from a continuous variable to a categorical variable (quartile) to evaluate the robustness of the results. Restricted cubic spline (RCS), generalized additive models (GAM) and smooth curve fitting(both adjusted for age, gender and ethnicity, BMI, marital status, PIR, education level, smoking status, hypertension, diabetes, stroke, CHF, CHD, uric acid, FBG, HDL-C and GGT) were utilized to explore potential non-linear association between TyG index and α-Klotho. The selection of RCS models was guided by the goal of minimizing the Akaike Information Criterion (AIC) statistic, ultimately employing 7 knots for female participants and 3 knots for others (Additional file, Table S1). The statistical analysis procedures outlined above were similarly applied to the subgroups based on gender. Threshold effect analysis was performed on participants exhibiting a non-linear relationship between the TyG index and α-Klotho. Subgroup analysis was performed using weighted multivariate linear regression (adjusted for age, gender and ethnicity, BMI, marital status, PIR, education level, smoking status, hypertension, diabetes, stroke, CHF, CHD, uric acid, FBG, HDL-C and GGT) stratified by gender, age, BMI, smoking status, hypertension, diabetes, CHD, stroke and CHF. Statistical analyses were performed using SPSS 25.0 (IBM, Armonk, New York, USA) and R (version 4.2). A significance level of P < 0.05 was considered statistically significant.

Results

Baseline characteristics

A total of 2,568 participants who met the predetermined criteria were enrolled in the final analysis. The average age of the study participants was 57.10 ± 10.38 years, with 53.5% identified as male and 46.5% as female. The weighted baseline characteristics of the enrolled study participants, stratified by TyG index quartiles (quartile 1: n = 642, 5.65 ≤ TyG index ≤ 8.30; quartile 2: n = 615, 8.31 ≤ TyG index ≤ 8.69; quartile 3: n = 652, 8.69 ≤ TyG index ≤ 9.11; quartile 4: n = 659, 9.11 ≤ TyG index ≤ 11.07), are presented in Table 1. Significant differences were observed across all variables. Compared to participants in the lowest quartile of the TyG index, those in the higher quartiles exhibited lower α-Klotho levels and were significantly more likely to have hypertension, diabetes, CHF, and CHD, as well as elevated serum uric acid, FBG, GGT, ALT, AST, ALP, creatinine, and TG. Furthermore, they were more likely to be male, older, non-Hispanic White, have a lower education level, be married or cohabiting, possess a lower PIR, exhibit a higher BMI, and be current smokers.

Table 1 Weighted characteristics of the study population based on TyG quartiles
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Association between TyG index and α-Klotho

The correlation between the TyG index and α-Klotho is displayed in Table 2. According to the findings of our study, a negative association between the TyG index and α-Klotho was observed in Models 1–3, although it was not statistically significant. After fully adjusting for covariates (Model 4), this association reached statistical significance [β = -74.07, 95% CI (-100.29, -47.85), p < 0.001]. Individuals in the higher quartile of the TyG index experienced a greater reduction in α-Klotho levels in Model 4 compared to those in the lower quartile (all p for trend < 0.05).

Table 2 Weighted multivariate liner regression analysis of association between TyG and α-Klotho
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RCS curves were utilized to explore potential non-linear association between TyG index and α-Klotho. In the total cohort (Fig. 2A), a linear association was observed (p for nonlinear = 0.761). Gender stratification revealed a linear relationship in males (p for nonlinear = 0.824) and a non-linear association in females (p for nonlinear = 0.016) (Fig. 2B, C). Smooth curve fitting and GAM validated these findings (Fig. 3).We further analyzed the threshold effect in the female participants and the data indicated that the inflection point of TyG index was 8.01, exceed which the level of α-Klotho decreased significantly with increasing TyG index. [β=-151.72, 95%CI (-201.93,-101.50), p < 0.001]. (Table 3)

Fig. 2
figure 2

The restricted cubic spline (RCS) analysis between the TyG index and α-Klotho level

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Fig. 3
figure 3

The association between the TyG index and α-Klotho level. A Overall, the solid red line represents the smooth curve fit between variables and the blue bands represent the 95% confidence interval from the fit. B The association stratified by gender

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Table 3 Threshold effect analysis of TyG on -Klotho level using linear regression model in female
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Subgroup analysis

Stratified, weighted multivariate linear regression revealed heterogeneity in the association between the TyG index and α-Klotho across different subgroups (Fig. 4). Significant associations were observed in subgroups stratified by age, BMI, smoking status, hypertension, and diabetes (p < 0.05). Interaction tests revealed that gender significantly modified this relationship (p < 0.001), with female participants exhibiting a more pronounced decline in α-Klotho levels as the TyG index increased, compared to males.

Fig. 4
figure 4

Subgroup analysis for the association between the TyG index and α-Klotho level. Age, gender, ethnicity, BMI, marital status, PIR, education level, smoking status, hypertension, diabetes, stroke, congestive heart failure, coronary heart disease, uric acid, FG, HDL-C and GGT were adjusted. CI, confidence interval; CHD, coronary heart disease; CHF, congestive heart failure

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Discussion

In the present cross-sectional study, we observed a significant negative correlation between the TyG index and α-Klotho concentration within the United States population. A higher TyG index was independently associated with lower α-Klotho levels. Subgroup analyses and interaction testing indicated that the negative association between the TyG index and α-Klotho levels remained significant, irrespective of age, BMI, smoking status, hypertension, and diabetes. The association between the TyG index and α-Klotho levels may be more pronounced in female participants within this study. RCS, smooth curve fitting, and GAM analyses demonstrated a linear association between the TyG index and α-Klotho in all participants, while a non-linear association was observed in females. The analysis of the threshold effect in female participants indicated that the inflection point of the TyG index was 8.01; beyond this point, the level of α-Klotho significantly decreased with increasing TyG index.

IR has been extensively shown to significantly correlate with the incidence, progression, and prognosis of cardiovascular diseases [24, 25]. Quantifying IR levels in individuals susceptible to or already affected by cardiovascular diseases can provide valuable information for risk prediction and stratification, thereby scientifically guiding clinical interventions aimed at reducing cardiovascular event rates. The gold standard for assessing IR, the hyperinsulinemic-euglycemic clamp, is limited by its complexity, high cost, and time-consuming nature, greatly restricting its widespread clinical utility [26]. As researchers continue to investigate alternative biomarkers for IR, the homeostasis model assessment of insulin resistance (HOMA-IR) is widely recognized as a surrogate marker, calculated from fasting insulin and glucose levels [27]. However, fasting insulin is not routinely measured in clinical practice, and varying measurement standards across laboratories introduce unavoidable data heterogeneity, further limiting the clinical adoption of HOMA-IR. As an alternative marker for IR, the TyG index has been demonstrated to correlate closely with the gold standard assessment of IR [28]. Derived from two commonly used clinical laboratory indicators—fasting TG and FBG—this simple, easily accessible, and cost-effective index has recently been shown to be closely associated with macrovascular and microvascular complications in patients with diabetes [18, 29] and serves as an independent predictor of future diabetes, stroke, MI, and cardiovascular mortality events in the general population [30, 31]. Evidence from recent meta-analyses has also demonstrated a close association between the TyG index and heart failure, atrial fibrillation, and obstructive sleep apnea [32,33,34].

Serum soluble α-Klotho, an anti-aging protein encoded by the Klotho gene, has recently garnered attention for its role in modulating inflammation, oxidative stress, and aging processes [12, 35,36,37]. It is closely associated with cardiovascular risk factors, including high BMI, smoking, alcohol consumption, and lipid parameters [38]. Transgenic overexpression of α-Klotho has been demonstrated to promote cardiovascular protection, encompassing improvements in endothelial dysfunction, antihypertensive effects, and the attenuation of cardiac fibrosis and arteriosclerosis [4]. Several studies conducted across diverse regions and populations indicate a significant correlation between low circulating Klotho levels and elevated mortality rates [39,40,41]. Data from the NHANES have identified low serum α-Klotho levels as independent risk factors for the incidence of CHF and MI in the American population, as well as for overall mortality and cardiovascular mortality rates among patients with hypertension, CHF, and diabetes [4, 15].

Both α-Klotho and the TyG index are associated with glycemic and lipid metabolism, and hold predictive value for the incidence, progression, and outcomes of cardiovascular, diabetes and many other diseases. A high TyG index and low α-Klotho levels have been established as independent risk factors for both overall mortality and cardiovascular mortality. However, to date, no studies have investigated the relationship between these two factors. Our findings address this gap in the field. In the present study, we observed a significant negative correlation between the TyG index and α-Klotho concentration within the United States population, with this effect being particularly pronounced in females. These findings have significant implications for public health and primary healthcare strategies, underscoring the importance of managing blood sugar and lipid levels to mitigate age-related decline, particularly in women. Monitoring the TyG index may serve as an effective method for assessing the risk of aging-related diseases. Early identification of individuals with low α-Klotho levels and implementing scientific clinical interventions can improve population health and alleviate the burden on public health. Certain secondary preventive medications, including renin-angiotensin system inhibitors and statins, may lead to the upregulation and elevation of Klotho levels [42, 43]. Furthermore, hormonal compounds, peroxisome proliferator-activated receptor γ (PPAR-γ) agonists, vitamin D receptor agonists, antioxidants, anti-inflammatory agents, and mammalian target of rapamycin (mTOR) inhibitors also demonstrate potential for enhancing Klotho levels. Emerging technologies, including RNA modification, gene therapy, and gene editing, hold considerable promise for enhancing Klotho expression at the genetic level [44].

The underlying mechanisms that contribute to the negative correlation between the TyG index and α-Klotho levels remain inadequately elucidated. A higher TyG index may indicate a “worse condition” within the body, which is associated with a shorter lifespan, as evidenced by lower levels of α-Klotho. As an alternative insulin IR marker, the TyG index has demonstrated a strong correlation with the gold standard assessment of IR [28]. Research conducted in both animal and human subjects indicates an association between lower levels of Klotho and IR [45, 46]. IR is closely correlated with oxidative stress, activation of the systemic inflammatory response, renin-angiotensin-aldosterone system, and endothelial dysfunction [20, 47, 48]. These pathophysiological changes may be attributed to fluctuations in α-Klotho levels. In murine collecting duct cells, hydrogen peroxide (H2O2)-induced oxidative stress exhibited a dose-dependent downregulation of Klotho levels [49]. In patients undergoing peritoneal dialysis, circulating S-Klotho levels exhibited a significant negative correlation with levels of 8-isoprostane, a marker of oxidative stress [50]. Certain antioxidants, including vitamins C and E, as well as antioxidant compounds such as α-lipoic acid and polyphenols, have been shown to upregulate Klotho levels [51, 52]. Inflammatory responses have been demonstrated to decrease Klotho expression []. Inflammatory cytokines, including tumor necrosis factor-α (TNFα) and TNF-like weak inducer of apoptosis (TWEAK), can downregulate Klotho expression through the activation of the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB)-dependent pathway [53, 54]. Growing evidence indicates a negative association between Klotho and the renin-angiotensin system (RAS) [55]. Prolonged administration of angiotensin II (Ang-II) results in decreased renal Klotho mRNA and protein expression through the activation of the angiotensin type-1 (AT1) receptor [56]. Angiotensin receptor blockers (ARBs) and angiotensin-converting enzyme inhibitors (ACEIs), including valsartan and enalapril, may enhance Klotho levels by inhibiting Ang-II-mediated suppression of Klotho expression [44]. The identification of a non-linear relationship in females, characterized by a threshold effect at a TyG index of 8.01, is particularly intriguing. An elevation in the TyG index is frequently correlated with an increase in IR. Research indicates that α-Klotho plays a crucial role in insulin signaling [57]; therefore, reduced levels of α-Klotho may exacerbate IR, thereby perpetuating a vicious cycle. Hormonal fluctuations in females, including variations in estrogen levels, may significantly influence metabolic processes and inflammatory responses, thereby impacting the production of α-Klotho. When the TyG index surpasses a specific threshold, the effects of these hormonal changes may become particularly pronounced, resulting in a significant reduction in α-Klotho levels. Future research should prioritize elucidating the causal relationships among IR, hormonal influences, and α-Klotho, particularly in females.

The influence of gender on this association is particularly noteworthy. This negative correlation is statistically significant in females, whereas no such significance is observed in males. A study investigating the relationship between serum manganese levels and S-Klotho concentrations also revealed significant gender differences [58]. These disparities between sexes may be attributed to variations in biological structure and lifestyle factors [59]. Numerous studies have illustrated the intricate connections between hormones and Klotho, revealing certain gender disparities. For instance, parathyroid hormone levels are positively correlated with α-Klotho levels in males but negatively correlated in females [60]. Some sex hormones, such as testosterone and estrogen, also exhibit regulatory effects on Klotho levels [61,62,63]. Testosterone promotes the expression of the Klotho gene via an androgen receptor-mediated pathway [64]. Analogous to testosterone, estrogen also upregulates the expression of Klotho via the estrogen receptor α pathway [61]. In females, testosterone is primarily synthesized by the ovaries and adrenal glands, with levels progressively declining before menopause. Following menopause, there is a significant decrease in testosterone levels [65]. The women in our study population were over 40 years old and primarily comprised individuals in late perimenopause or menopause. The simultaneous and substantial decline of both testosterone and estrogen may represent a critical factor contributing to the observed gender differences in this study. Another plausible explanation pertains to sex differences in the regulation of free radical balance, as female mice with shorter lifespans have been demonstrated to exhibit elevated levels of oxidative stress [66]. Gender significantly influences the degree of IR, with women displaying a higher prevalence of impaired fasting glucose [67, 68]. Endogenous estrogen has been demonstrated to enhance insulin sensitivity in females [69]. However, as estrogen levels diminish, this protective effect becomes substantially reduced. Additional in-depth research is essential to elucidate the underlying mechanisms driving these gender differences and to further advance our understanding in the future.

This study effectively addresses a significant gap in the current body of research. The substantial sample size of this study further amplifies its unique contribution to the field, as it encompasses the entire U.S. population and employs standardized and homogeneous collection methods, thereby minimizing measurement bias to the greatest extent possible. Moreover, to bolster the robustness of our study findings, we meticulously mitigated confounding bias through comprehensive covariate adjustment. Nonetheless, it is important to acknowledge certain limitations inherent in this study. First, the observational nature of our study precludes the determination of causality. Second, despite adjustments for potential confounding factors, certain unmeasured confoundersmay still influence the correlation between the TyG index and α-Klotho levels. For instance, the consumption of alcoholic beverages [70, 71] and adherence to a pro-inflammatory dietary pattern [35, 72, 73] are inversely correlated with Klotho levels, whereas thyroid hormones exhibit a positive correlation with Klotho levels [74, 75].Therefore, caution is warranted in interpreting our findings, and future studies should incorporate these measures to facilitate a more comprehensive assessment of health outcomes. Third, the use of stored excess serum to quantify serum α-Klotho levels may introduce measurement bias. Fourth, conducting multiple subgroup analyses may heighten the risk of Type I errors (false positives). Corrections for multiple comparisons, such as the Bonferroni correction, were not applied, as they may be overly conservative and diminish the ability to detect true associations. Nevertheless, this remains a limitation of our study and should be taken into account when interpreting the findings. Furthermore, while our study did not impose strict age criteria for inclusion, the participants were aged 40 and above, rendering the applicability of our findings to populations outside this age range uncertain. Finally, this study employed NHANES data, which offers a robust and representative sample of the U.S. population, caution is warranted when generalizing these findings to populations outside the U.S. As NHANES primarily consists of U.S. residents, notable racial and ethnic disparities are observed in metabolic and aging markers. These disparities may be shaped by multiple factors, such as genetics, environmental conditions, dietary habits, lifestyle, and healthcare access, all of which can differ substantially across global populations. Consequently, further caution is necessary when extrapolating these findings to other countries or ethnic groups.

Conclusion

Our findings elucidate a significant negative correlation between the TyG index and α-Klotho levels, with this association being particularly pronounced in female subjects. TyG index may serve as an early indicator of individuals with low α-Klotho levels, thereby facilitating the implementation of scientifically grounded clinical interventions aimed at enhancing population health and alleviating public health burdens. Notwithstanding adjustments for potential confounding factors, certain unmeasured confounders may continue to exert an influence on this relationship. Consequently, future investigations should incorporate these measures to enable a more comprehensive assessment and to elucidate the underlying mechanisms of the observed association.

Data availability

The data analyzed in the current study were publicly available and can be found at https://www.cdc.gov/nchs/nhanes/.

Abbreviations

CMD:

Cardiometabolic disease

FGF23:

Fibroblast growth factor-23

S-Klotho:

Soluble Klotho

IR:

Insulin resistance

TyG index:

Triglyceride-glucose index

TG:

Fasting triglycerides

FBG:

Fasting blood glucose

NHANES:

National Health and Nutrition Examination Survey

NCHS:

National Center for Health Statistics

PIR:

Income to poverty ratio

BMI:

Body mass index

ALT:

Alanine aminotransferase

AST:

Aspartate aminotransferase

ALP:

Alkaline phosphatase

GGT:

Gamma glutamyl transferase

BUN:

Blood urea nitrogen

TC:

Total cholesterol

HDL-C:

High density lipoprotein cholesterol

LDL-C:

Low density lipoprotein cholesterol

SD:

Standard deviation

ANOVA:

One-way analysis of variance

VIF:

Variance inflation factors

CHF:

Congestive heart failure

CHD:

Coronary heart disease

CI:

Confidence intervals

RCS:

Restricted cubic spline

GAM:

Generalized additive models

AIC:

Akaike information criterion

HOMA-IR:

Homeostasis model assessment of insulin resistance

MI:

Myocardial infarction

PPAR-γ:

Peroxisome proliferator-activated receptorγ

mTOR:

Mammalian target of rapamycin

H2O2:

Hydrogen peroxide

TNFα:

Tumor necrosis factor-α

TWEAK:

TNF-like weak inducer of apoptosis

NF-κB:

Nuclear factor kappa-light-chain-enhancer of activated B cells

RAS:

Renin-angiotensin system

Ang-II:

Angiotensin II

AT1:

Angiotensin type-1

ARBs:

Angiotensin receptor blockers

ACEIs:

Angiotensin-converting enzyme inhibitors

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Acknowledgements

The authors express gratitude to all the staff and participants of the NHANES study.

Funding

This study was supported by Tianjin Natural Science Fund(S24YBL011).

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Author notes

  1. Yan Zhang and Kexin Song contributed equally to this work.

Authors and Affiliations

  1. Tianjin Union Medical Center, Tianjin Medical University, Tianjin, 300122, China

    Yan Zhang & Zhuhua Yao

  2. Graduate School of Hebei Medical University, Shijiazhuang, 050017, China

    Kexin Song

  3. The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, 300122, China

    Zhuhua Yao

  4. Department of Cardiology, Tianjin Union Medical Center, Tianjin, 300131, China

    Zhuhua Yao

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  1. Yan Zhang
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YZ, ZHY contributed to the study designation. YZ and KXS contributed to manuscript writing, data analysis and editing. ZHY conducted a critical revision of the manuscript. All authors approved the final manuscript.

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Correspondence to Zhuhua Yao.

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Zhang, Y., Song, K. & Yao, Z. The association between the triglyceride-glucose index and serum anti-aging protein α- Klotho: a population-based study. Diabetol Metab Syndr 16, 259 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-024-01487-6

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Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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